phd on machine learning

Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

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Best Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salaries

If you want to take your career in machine learning to the next level, you might be considering enrolling in one of the best PhDs in machine learning. However, it can be hard to figure out which program is right for you, how to fulfill all the requirements, or how to secure the right funding opportunities so you can continue your education in this field.

This comprehensive guide will look at the best options for a machine learning PhD, both in-person and online. We’ll also discuss the best machine learning jobs and how to get them with this type of degree, as well as the average PhD in machine learning salary you can earn upon graduation.

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What is a phd in machine learning.

A PhD in machine learning is a research-intensive degree program that helps students further their education in machine learning. A machine learning PhD is a doctorate degree that involves coursework, qualifying exams, and oral examinations. Professors and members of faculty also work closely with students to help them develop a strong dissertation throughout their degree program.

Students interested in pursuing a PhD in machine learning should have already completed a bachelor’s degree in a relevant field. They also need to have completed a master’s degree , or commit to completing it along the way.

How to Get Into a Machine Learning PhD Program: Admission Requirements

The admission requirements to get into a machine learning PhD program typically include filling out an application form and submitting an application fee, academic transcripts from your undergraduate degree, two to three recommendation letters, a statement of purpose, GRE scores, a resume, writing sample, and English proficiency test scores for international students.

Each school’s website will have a detailed breakdown of all the requirements needed for the application process. Some schools may require you to pay an application fee, have a minimum GPA score, and take the Graduate Record Examination (GRE), although most schools have waived this requirement until 2023.

You will need two or three recommendation letters for your PhD application. The recommendation letter should be from faculty members and colleagues familiar with your work. Part of the application process is a statement of purpose, which is an essay that should tell the admission committee why you want to pursue a PhD in Machine Learning.

PhD in Machine learning Admission Requirements

Application form
Application fee
College transcripts
Minimum GPA of 3.0 (varies)
Two to three recommendation letters
Statement of purpose
Writing sample
English proficiency test (only for non-native English speakers)

Machine Learning PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Machine Learning?

It is hard to get into a PhD program in machine learning. Prestigious schools are usually very selective and have a low admission rate ranging between four and 30 percent. For example, Harvard University has an admission rate of four percent, so make sure you prepare a strong application and have a high GPA if you want to get into Harvard or another highly-reputable university.

However, not all PhD programs are extremely selective. For instance, institutions in the University of California system have higher acceptance rates, such as 34.4 percent. To improve your chances of acceptance, you can ask a friend or mentor to look over your PhD application. You should also apply to more than one program.

How to Get Into the Best Universities

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Best PhDs in Machine Learning: In Brief

Best universities for machine learning phds: where to get a phd in machine learning.

The best universities for machine learning PhDs include Carnegie Mellon University, Georgia Tech, and University of Washington. These schools can help you earn your machine learning PhD. If you’re wondering where you can get a PhD in machine learning, the list below discusses 10 excellent programs, along with their essential details.

Carnegie Mellon University was founded in 1900. It is known for its high-quality graduate programs in engineering, artificial intelligence (AI), and computer science. There are about 29 graduate degree programs offered at Carnegie Mellon University’s graduate school. Students and faculty conduct open and restricted research in four main areas, including AI, learning sciences, robotics, and neuroscience.

PhD in Machine Learning

The PhD in Machine Learning at Carnegie Mellon University requires students to take six core courses and one elective course. This research-focused degree program requires students to present and defend a thesis by the end of the program.

During this program, students need to work as teaching assistants for two semesters and will complete a presentation to show adequate presentation skills to the Speaking Skills committee. Common courses for this program include an introduction to machine learning, intermediate statistics, and regression analysis.

PhD in Machine Learning Overview

Program Length: 5 years
Acceptance Rate: 17%
Tuition and Fees: $645/unit
PhD Funding Opportunities: Graduate assistantships, scholarships, and grants

PhD in Machine Learning Admission Requirements

GRE (recommended)
TOEFL (for international applicants)
Recommendation letters
High level of knowledge in computer science and math

Georgia Institute of Technology is a reputable university founded in 1885. It is known for its excellent STEM majors, of which 86 percent of students are enrolled. It offers many graduate degree programs to its 25,011 graduate students, but the most well-known programs are in electrical and computer engineering, computer science, and mechanical engineering.

The PhD in Machine Learning at Georgia Institute of Technology will teach you excellent machine learning techniques through machine learning courses. Students need to complete four core courses, five elective courses, responsible conduct of research course, and three doctoral minors.

Typical courses for this PhD program include machine learning theory and methods, advanced theory, and computing and optimization. This program consists of many research hours and requires PhD students to complete the defense of a dissertation. Students also need to complete a qualifying exam.

Program Length: 4 years
Acceptance Rate: 21%
Tuition and Fees: $586/credit (in state); $1,215/unit (out of state)
PhD Funding Opportunities: Federal loans, private loans, federal work-study program
Minimum GPA of 3.0
Three letters of recommendation
IELTS minimum score of 7.5 or higher for non-native speakers
TOEFL minimum score of 100 or higher for non-native speakers
GRE (optional)

Harvard University is a highly reputable and well-known private research university founded in 1636. It currently has about 33,276 students enrolled in undergraduate degrees, graduate degrees, and certificate programs. Harvard University has 12 graduate schools and a fantastic faculty, of which members have received Nobel prizes in chemistry, medicine, physics, literature, peace, and economic sciences.

PhD in Computer Science

The machine learning PhD program at Harvard University teaches students about the interaction of computation with the world and computation fundamentals. Students will work with highly-rated faculty members conducting research in programming languages, machine learning, and artificial intelligence during this excellent program. As they move through their program, students will learn about connecting computer science to other fields while they interact with lawyers, scientists, and engineers.

PhD in Computer Science Overview

Acceptance Rate: 4%
Tuition and Fees: $50,928/year
PhD Funding Opportunities: Grants, fellowships, traineeships, research assistantships, and teaching fellowships

PhD in Computer Science Admission Requirements

Transcripts
At least one recommendation letter
Show English proficiency (for non-native English speakers)

Northwestern University was launched in 1851 and is one of the top research universities in the country. Its more than 50 research centers focus on topics like nanotechnology, neuroscience, biotechnology, and drug discovery.

Currently, Northwestern university has over 13,000 grad students enrolled in its 173 graduate degree and certificate programs. Northwestern University is known for its fantastic business, education, and materials engineering degree programs.

PhD in Computer Science and Learning Sciences

The machine learning PhD program at Northwestern University is research-driven and helps students understand and build a connection between research on computation and learning. Students can choose between many different areas of study, including machine learning and programming language design.

To complete this program, there should be apparent relevance in your research between computer science and the learning science in your field of study, such as machine learning. You must also complete a qualifying exam, research projects, and a PhD dissertation. Courses include Machine Learning, Foundations of Learning Science, and Artificial Intelligence Programming.

PhD in Computer Science and Learning Sciences Overview

Program Length: 4-9 years
Acceptance Rate: 7%
Tuition and Fees: $18,689/quarter for programs with 8 or fewer quarters; $4,672/quarter for more than 8 quarters
PhD Funding Opportunities: Assistantships, grants, and fellowships

PhD in Computer Science and Learning Sciences Admission Requirements

Online application form
Academic transcripts
GRE scores (temporarily not required, but still recommended)
TOEFL scores (for international applicants)

Tulane University was launched in 1834 and is in the top two percent of research universities in the US. Tulane University conducts research in bio-innovation, health, energy, and the environment. It offers over 200 graduate degrees to over 5,000 grad students.

Students at Tulane University graduate school can pursue PhDs in computer science, environmental health studies, economics, and more. This University offers excellent funding opportunities such as fellowships and stipends.

The PhD in Computer science at Tulane University is a research-intensive program. Students must conduct research in a specific depth area such as machine learning, artificial intelligence, or data science. Students who specialize in machine learning will research machine learning techniques, theory of applications, machine learning systems, and algorithms.

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Topics covered during this graduate program include algorithms, machine learning, and computer networks. Students also need to take three research courses. Students will do a qualifying oral exam during this program, complete a prospectus presentation, and a PhD dissertation in their preferred specialization, such as machine learning.

Program Length: 4-7 years
Acceptance Rate: 9.73%
Tuition and Fees: $1,831/credit; $35,088/year with 9 credits per semester
PhD Funding Opportunities: Scholarships, fellowships, and stipends
University transcripts
Statement of Purpose
GRE test scores
TOEFL scores (for international applicants)

University of California Irvine is a public land-grant university established in the 1960s as part of the University of California system. It is a research-focused institution and boasts eight Nobel Prize winners among its alumni. The graduate school offers over 100 graduate programs. This university offers many different PhDs, including bioengineering, machine learning and data science, and mechanical engineering.

The PhD in Computer Science at University of California Irvine helps students learn computer science fundamentals and essential machine learning skills. This program involves a research project. Students need to choose a research topic such as machine learning and artificial intelligence, scientific computing, or any other research topic listed on the website.

Students need to complete at least 47 units during their program and maintain a 3.5 GPA. Courses for this degree include Machine Learning, Machine Learning and Data Mining, and Analysis of Algorithms. Before the end of the program, students will complete a candidacy exam, submit a dissertation plan, complete a final exam, and defend their dissertations.

Program Length: 6-7 years
Acceptance Rate: 28.96%
Tuition and Fees: $18,037/year (in state); $33,139/year (out of state)
PhD Funding Opportunities: Fellowships, graduate employment, research assistantships, and training grants
English proficiency test scores (for international applicants)

University of California San Diego traces its roots back to 1960 and had its first enrollments in 1964. It offers over 200 degree programs at the undergraduate and graduate levels. It is a research-focused institution that conducts research in a variety of fields, from robotics and climate to microbiomes.

PhD in Machine Learning and Data Science

The PhD in Machine Learning and Data Science program teaches students essential machine learning techniques to help them further or start their careers in machine learning and artificial intelligence . During this graduate program, students need to complete 36 credit hours. They will conduct an in-depth research project, a preliminary exam, and a qualifying exam.

At the end of the PhD, students need to submit and defend a doctoral thesis. They are allowed to consider the faculty and choose a research advisor that fits their research style and goals. The research advisor will support the student through their PhD from start to finish. Courses included in this degree are Linear Algebra & Application, Deep Learning & Applications, Machine Learning for Image Processing, and Statistical Learning.

PhD in Machine Learning and Data Science Overview

Program Length: 6-8 years
Acceptance Rate: 34.3%
Tuition and Fees: $ 11,442/year
PhD Funding Opportunities: Fellowships

PhD in Machine Learning and Data Science Admission Requirements

GRE test scores (recommended)
English proficiency test (for international applicants)
Three recommendation letters
High school and college transcripts

University of Pennsylvania is a research-driven university based in Philadelphia. It opened its doors to students in 1751. It prides itself on research and encourages students to conduct research during their studies. This university has twelve graduate schools that offer graduate degrees and certificates. Some of the fields for PhD level studies include biochemistry, economics, and materials science and engineering.

PhD in Computer and Information Science

The PhD in Computer and Information Science at the University of Pennsylvania has a specialization called Machine Learning + X, allowing students to choose machine learning and one other specialization to focus on throughout their programs. For example, you could choose to do a Machine Learning + Computer Architecture specialization.

This degree requires specific courses, a preliminary exam, a teaching assistantship, a defense proposal, a defense of your dissertation, and a submission of your thesis. This PhD will help students gain new machine learning skills and experience in machine learning.

PhD in Computer and Information Science Overview

Tuition and Fees: $19,919/year for the first eight semesters; $1,836 flat rate after the first eight semesters
PhD Funding Opportunities: Fellowships, teacher assistantships, and research assistantships

PhD in Computer and Information Science Admission Requirements

Personal statement
Unofficial academic transcripts
Three official recommendation letters
GRE scores (optional until 2023, but still recommended)

This public research university was established in 1895 and is known for its high-quality doctoral research. University of Texas at Arlington has more than 174 graduate degrees and other graduate study options. New and current students can pursue a PhD in different fields like computer science, civil engineering, and mathematics.

The PhD in Computer Science offered by University of Texas at Arlington allows students to choose a study track. There are eight options, but students interested in machine learning should choose the intelligent systems track, which covers machine learning methods, neural networks, parallel AI, and more.

Throughout this degree program, students will complete 18 hours of coursework and complete two comprehensive exams, one of which is a dissertation proposal. They will also submit a final dissertation defense before being awarded their PhD.

Program Length: 4-5 years
Acceptance Rate: Not stated
Tuition and Fees: $11,044/year (in state); $23,486/year (out of state)
PhD Funding Opportunities: Teacher’s assistantships, research assistantships, fellowships, grants, and scholarships
College transcripts

University of Washington is a highly reputable school located in Washington that started conducting classes in 1861. It is known for its high-quality research and boasts that seven of its researchers have won Nobel prizes in physics, physiology, and medicine.

New and current students at University of Washington can choose to continue their education with over 300 graduate degree programs offered at its three campuses. This university provides PhDs in physics, mathematics, and machine learning and big data.

PhD in Machine Learning and Big Data

The PhD in Machine Learning and Big Data program at University of Washington teaches students valuable machine learning methods and how to conduct data analysis of big data sets. It will help students build a strong foundation in machine learning and big data methodologies.

Students need to meet the coursework requirements, write a general examination, conduct research to write a dissertation, and meet the credit hour requirement of 90 credits. Courses included in this PhD are Foundational Machine Learning, Advanced Machine Learning, and Advanced Statistical Learning.

PhD in Machine Learning and Big Data Overview

Program Length: Up to 10 years
Acceptance Rate: 10.58%
Tuition and Fees: $6,725/quarter (in state); $11,688/quarter (out of state)
PhD Funding Opportunities: Fellowships, internships, and research assistantships

PhD in Machine Learning and Big Data Admission Requirements

GRE scores (optional)
Funding application

Can You Get a PhD in Machine Learning Online?

No, you cannot get a PhD in machine learning online. However, you can pursue an online PhD in computer science with a machine learning component such as an online machine learning course or specialization. Many fantastic online computer science PhDs will help you fine-tune your machine learning expertise.

Best Online PhD Programs in Machine Learning

How long does it take to get a phd in machine learning.

It takes between four and 10 years to get a PhD in Machine learning. According to Statista, the average time to complete a doctorate degree is seven and a half years. A PhD takes this long to complete because it is research-intensive and involves several stages.

Students need to take required courses and complete coursework in the first two years of a PhD program. Once the coursework is complete, students will write an examination to ensure they have completed all the essential skills and expertise in machine learning.

In the final years of a PhD, students conduct research and write a dissertation which takes between two to five years to finish. Usually, the school will have information on their website regarding the maximum time students have to meet all the PhD requirements.

Is a PhD in Machine Learning Hard?

Yes, a PhD in Machine Learning is hard because it is research-driven. If you decide to pursue a PhD in machine learning, you need to ensure that you are motivated and determined to work hard because this program involves many hours of independent research and writing.

A PhD is also a lengthy degree program that takes a minimum of four years to complete. Don’t let the difficulty of a PhD in machine learning discourage you, though. If you are determined and enjoy researching and learning about machine learning, you will succeed.

How Much Does It Cost to Get a PhD in Machine Learning?

It costs $19,314 annually to get a PhD in Machine Learning , according to the figures from 2019 stated by the National Center for Education Statistics (NCES). The total tuition of your machine learning PhD depends on specific factors, including format, location, school, and specialization.

Colleges and universities are usually public or private institutions. Depending on what kind of school you attend, the tuition will differ. The average tuition for a PhD at a public institution is $12,171, while a PhD at a private institution costs $25,929. Search your school’s website or contact it directly to learn about the specific tuition costs of your PhD program.

How to Pay for a PhD in Machine Learning: PhD Funding Options

The funding options that students can use to pay for their PhD in machine learning include research assistantships, teaching assistantships, fellowships, internships, grants, and stipends. These funding options will lighten the financial burden of pursuing a PhD in machine learning.

Some schools offer teaching assistantships to students. You work a certain number of hours per week and receive a stipend or a tuition waiver or discount. A research assistantship is similar to a teaching assistantship, but they have different duties. According to Statista, research assistantships are the most common funding option for doctoral degrees .

Find out directly from your school if there are available paid internships, along with any other funding opportunities for PhD students in machine learning. Some schools award funding opportunities to students nominated by faculty members.

Best Online Master’s Degrees

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What Is the Difference Between a Machine Learning Master’s Degree and PhD?

The difference between a machine learning master’s degree and a PhD is that a PhD is research-intensive and focused, while a master’s degree is more focused on one’s career and may or may not include research for a master’s thesis.

A PhD is the highest degree level that a person can pursue, whereas a master’s degree is one level below. According to Statista, PhD degree holders make more than master’s degree graduates . Upon completing a master’s degree, students can earn an average salary of $92,272, while PhD graduates earn an average salary of $136,702.

Master’s vs PhD in Machine Learning Job Outlook

You can get a job as a computer information research scientist with a master’s degree, which comes with a job outlook of 22 percent . This is much faster than the average job outlook. With a PhD in machine learning, you can get any job in machine learning, but a job that explicitly requires a PhD is a university lecturer.

The job outlook for a machine learning lecturer is 12 percent , according to information cited by the US Bureau of Labor Statistics (BLS). This job outlook is much lower than that of a computer information research scientist. However, 12 percent is still an excellent average growth rate.

Difference in Salary for Machine Learning Master’s vs PhD

There is a significant contrast in earnings between a Machine learning PhD and a Machine learning Master’s degree. Although PayScale does not list the salary of Machine learning graduates specifically, it lists salary information for artificial intelligence, a field closely related to machine learning.

The average salary of an artificial intelligence PhD graduate is $115,000, while an AI master’s degree graduate earns an average salary of $103,000, annually . As you can see, a PhD will get you a very high average annual wage, and your salary can increase depending on your experience, location, and position.

Related Machine Learning Degrees

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Why You Should Get a PhD in Machine Learning

You should get a PhD in machine learning because it will open up new job opportunities, help you earn a higher salary, and allow you to add value to the machine learning industry. If you enjoy doing research, learning new things, and want to earn a higher salary, then a PhD is perfect for you.

Reasons for Getting a PhD in Machine Learning

Higher salaries. Earning a PhD ensures that you will get a job with a high-paying salary. A PhD is the highest degree level that you can achieve, and PhD graduates earn a significantly higher salary than associate, bachelor’s, or master’s degree holders.
Contributing to your professional industry. While completing a PhD, students conduct a lot of original research, broaden their skills and add value to their field. At the end of a PhD, students submit a dissertation, a document that identifies a problem within the industry and presents a solution through research.
Enhancing specialized and soft skills. A PhD will help you improve and gain valuable specialized skills and techniques in machine learning, such as statistics and natural language processing. You will also gain excellent soft skills in machine learning, like problem-solving and time management.
Increasing job opportunities. Once you earn your PhD, your job opportunities will increase. A PhD will help you get a senior profession, such as a lecturer or senior machine learning engineer. According to PayScale, a senior machine learning engineer earns an annual wage of $153,255 .
Gaining valuable knowledge. Due to a PhD’s research-intensive nature, students constantly learn new things and gain valuable knowledge. If you enjoy learning, you should get a PhD because the learning opportunities are endless.

Getting a PhD in Machine Learning: Machine Learning PhD Coursework

Man with black t-shirt fitting a robotic arm onto a man with a blue t-shirt

Getting a PhD in Machine Learning requires taking specific courses to meet the necessary credit hours to graduate from your PhD program. Required courses typically include machine learning, introduction to AI, and statistical learning. Machine learning PhD coursework will help you gain essential machine learning skills and knowledge.

During the machine learning course, students will learn about the fundamental topics and techniques in machine learning, such as logistic regression, clustering, classifications, deep neural networks, linear models, and support vector machines. This course encourages reinforcement learning by looking at several real-world examples.

Deep Learning

Deep learning is an essential part of machine learning and involves artificial neural networks. The deep learning course will teach students about theoretical and practical aspects of deep learning, including neural networks, optimization algorithms, and structured models.

Statistical Learning

This course will cover modern learning algorithms such as variational approximations, boosting, and support vector machines. While completing the statistical learning course, students will learn about statistical algorithms for data analysis and applications of signal processing. Students should know programming languages to enroll in this course.

Introduction to Artificial Intelligence

While completing a PhD in machine learning, students will need to complete an Artificial Intelligence course. An Introduction to AI course involves the study of models and theories related to systems that emulate human intelligence. Students will cover Bayesian networks, constraint satisfaction, probabilistic reasoning, and natural language processing.

Analysis of Algorithms

The analysis of algorithms course looks at different efficient algorithms and studies their complexity and correctness. Topics covered include network flow, dynamic programming, and amortized analysis. Students will discuss problems with no solutions and cover all different kinds of algorithms.

Best Master’s Degrees

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How to Get a PhD in Machine Learning: Doctoral Program Requirements

Read the list below to find out how to get a PhD in Machine Learning. There are specific criteria that each student needs to meet before being awarded their degree. Common requirements include the completion of coursework, a research project, and a final thesis.

A machine learning PhD usually requires 40 to 48 credit hours. Students must take about six core courses and one elective course. During the first four semesters of their programs, students need to complete a specific number of credits before the next stage of their PhD.

Research is a considerable part of a PhD, so most programs will require students to take one or more responsible conduct of research courses. The responsible conduct of research courses involves seminars and workshops that help students learn the best methods of conducting research. Some research courses involve a project that will help students learn through practice.

Machine learning PhDs will include a research project after completing the required research courses. The research project will be directed by a faculty member and requires students to conduct research and write a report. Students will then present their reports to the PhD committee. Research projects usually focus on a specific topic within machine learning or computer science.

Once students have completed the core course requirements and written their research project, they must complete a qualifying exam which typically includes an oral examination. The PhD committee sets the qualifying exam and is designed to assess whether students are ready to conduct independent research for their PhD thesis.

You need to act as a teaching assistant for two semesters in a machine learning course. This is a requirement that only some PhD programs have. The graduate chair and coordinator set the requirements of the teaching practicum.

The PhD thesis requires a few years of research around a specific topic in machine learning. Students research a particular topic, and then they need to present their findings to the PhD committee. The thesis also includes a defense of the dissertation. Usually, students need to submit a thesis draft to the committee for approval before defending it.

Potential Careers With a Machine Learning Degree

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PhD in Machine Learning Salary and Job Outlook

Machine learning PhD graduates earn a highly favorable salary because a PhD is the highest degree level someone can earn. As stated above, PayScale does not list the average salary of a machine learning PhD graduate, but it notes that the average salary of an AI PhD graduate is $115,000. Artificial intelligence is a field very closely related to machine learning.

The job outlook for a machine learning PhD graduate is between 12 and 22 percent. That is a very favorable job outlook. The BLS has stated that there are approximately 33,000 machine learning jobs each year.

What Can You Do With a PhD in Machine Learning?

With a PhD in machine learning, you can become a computer and information research scientist, a deep learning research engineer, or a computational linguist. Most higher education institutions offer career coaching services that help students prepare for interviews, write resumes, and find jobs. Contact your college to find out whether it offers career services.

Best Jobs with a PhD in Machine Learning

Computer and Information Research Scientist
Machine Learning Engineer
Deep Learning Research Engineer
Professor of Machine Learning
Computational Linguist

What Is the Average Salary for a PhD in Machine Learning?

The average salary for a PhD in machine learning is $115,000 per year . This is a high average salary, but it varies based on factors such as experience, location, and job description. The more experience you have and the higher your degree level is, the higher your salary will be. If you decide to become a computer and information research scientist, you can earn an average salary of $131,490. If you are part of the 90th percentile, you can earn more than $208,000 annually .

Highest-Paying Machine Learning Jobs for PhD Grads

Best machine learning jobs with a doctorate.

Now that we have looked at all the details about a machine learning PhD and how to become a machine learning engineer , let’s look at the five highest-paying machine learning Jobs for PhD graduates, in detail.

A machine learning engineer develops artificial intelligence systems that research and create algorithms that use large datasets. These algorithms can learn and make accurate predictions. Machine learning engineers are very skilled at programming, and they use programming languages like Java and Python.

Salary with a Machine Learning PhD: $112,513
Job Outlook: 22% job growth from 2020 to 2030
Number of Jobs: 33,000
Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, Washington

Deep learning research engineers use deep learning platforms to create programming systems that copy brain functions. They do this using neural networks, which have a similar structure to the human brain. These programming systems are designed to learn without the help of humans.

Salary with a Machine Learning PhD: $110,679

A computer and information research scientist improves and creates computer hardware and software using complex algorithms. They streamline these complex algorithms and enhance system efficiency. Computer and information research scientists' simplified algorithms lead to advancements in machine learning systems and other types of technology.

Salary with a Machine Learning PhD: $100,384

Professors of machine learning usually teach students at a university or college. They will teach courses related to a specific field. In this case, they will teach courses related to machine learning. Professors at big institutions may also conduct research and experiments and publish original research. If you enjoy teaching you can become a professor of machine learning.

Salary with a Machine Learning PhD: $98,500
Job Outlook: 12% job growth from 2020 to 2030
Number of Jobs: 1,276,900
Highest-Paying States: Alaska, New York, Utah, California, New Jersey

Computational linguists are a specific kind of computer scientist. They work with computers and teach computer systems how to understand human languages. They have excellent coding skills because they use programming languages to code. They also conduct computational linguistic research around a specific functional area or product line.

Salary with a Machine Learning PhD: $80,330

Is a PhD in Machine Learning Worth It?

Yes, a PhD in Machine Learning is worth it. There are many excellent institutions that can help you earn a PhD in Machine Learning while providing valuable support from faculty members. Earning this type of degree can help you further your machine learning career.

If you pursue a PhD in machine learning, you will very likely add value to your industry with the research conducted during your dissertation. Completing a PhD takes many years and is research-intensive but completely worth it if you look at the jobs that use machine learning and the average PhD in Machine learning salary.

Additional Reading About Machine Learning

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PhD in Machine Learning FAQ

The cheapest PhD in machine learning is the PhD in Machine Learning and Data Science offered by University of California San Diego. The PhD in Machine Learning and Data Science tuition at University of California San Diego costs $11,442 per year for both residents and non-residents.

Many top companies hire machine learning PhD graduates, including Google, Microsoft, Adobe, PayPal, Amazon, IBM, and Duolingo. With a PhD in machine learning, you can land a job at one of these companies and earn a high salary.

Yes, there are many remote jobs available for machine learning graduates. A quick search on websites such as Indeed, Glassdoor, and LinkedIn can put you in touch with many possible machine learning jobs. Make sure you read the details of each job carefully before you apply.

Yes, you can get a job in machine learning with a bootcamp. Bootcamps are short, but they are intensive and can teach you all the necessary skills to have a successful career in the machine learning industry. There are many excellent machine learning bootcamps to help you start your machine learning career.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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College of Computing

Ph.d. in machine learning, about the curriculum.

The central goal of the Ph.D. program is to train students to perform original, independent research. The most important part of the curriculum is the successful defense of a Ph.D. dissertation, which demonstrates this research ability.

The curriculum is designed with the following principal educational goals:

• Students will develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline. • Students will develop a deep understanding and set of skills and expertise in a specific theoretical aspect or application area of the machine learning discipline. • The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance. • Students will be able to engage in multidisciplinary activities by being able to communicate complex ideas in their area of expertise to individuals in other fields, be able to understand complex ideas and concepts from other disciplines, and be able to incorporate these concepts into their own work. The curriculum for the Ph.D. in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: • Computer Science (Computing) • Computational Science and Engineering (Computing) • Interactive Computing (Computing) – see Computer Science • Aerospace Engineering (Engineering) • Biomedical Engineering (Engineering) • Electrical and Computer Engineering (Engineering) • Industrial Systems Engineering (Engineering) • Mathematics (Sciences) Students must complete four core courses, five electives, a qualifying exam, and a doctoral dissertation defense. All doctorate students are advised by ML Ph.D. Program Faculty . All coursework must be completed before the Ph.D. proposal. An overall GPA of 3.0 is required for the Ph.D. coursework.

Research Opportunities

Our faculty comes from all six colleges across Georgia Tech’s campus, creating many interdisciplinary research opportunities for our students. Our labs focus on research areas such as artificial intelligence, data science, computer vision, natural language processing, optimization, machine learning theory, forecasting, robotics, computational biology, fintech, and more.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school.

The Machine Learning Ph.D. admissions process works bottom-up through the home schools. Admissions decisions are made by the home school, and then submitted to the Machine Learning Faculty Advisory Committee (FAC) for final approval. Support for incoming students (including guarantees of teaching assistantships and/or fellowships) is determined by the home schools.

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

The machine learning doctorate degree prepares students for a variety of positions in industry, government, and academia. These positions include research, development, product managers, and entrepreneurs.

Graduates are well prepared for position in industry in areas such as internet companies, robotic and manufacturing companies and financial engineering, to mention a few. Positions in government and with government contractors in software and systems are also possible career paths for program graduates. Graduates are also well-suited for positions in academia involving research and education in departments concerned with the development and application of data-driven models in engineering, the sciences, and computing.

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

From the Catalog:

Georgia Institute of Technology

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PhD Program

The PhD in Machine Learning is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences). Students are admitted through one of nine participating home schools:

Contact SCS
Contact CSE
Contact ChBE
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Contact ISYE
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Application requirements and deadlines follow the same as that of the home unit an applicant is applying through. For example, ML PhD applicants to the ECE home unit follow the same rules as the PhD ECE application requirements and deadlines.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school with the earliest deadline of December 1. Most home schools have a final deadline of December 15. Check with home schools above for more specific details.

Click here for application information and to apply

Applicants must meet all admissions standards (including requirements on the minimum GPA, minimum GRE/TOEFL scores) of the home unit, which may vary. After an initial review, the unit’s representative of the ML Ph.D. Faculty Advisory Committee (FAC) will submit their candidates for review and the final admission decision will be made by the ML FAC.

Note most home units have made the GRE optional for fall 2023 applications. Contact the home unit at the above links for any specific info.

The committee’s decision to admit will be based on (1) prior academic performance of the applicant in a B.S. or M.S. program at a recognized institution, including coursework and independent research projects, (2) prior work experience relevant to ML, (3) the applicant’s statement of purpose, and (4) the letters of support.

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships) are determined by the home units. Please review the home unit links above or contact them directly for details.

Have Questions?

Please contact the above home units directly for questions related to:.

Application deadlines
Application fee waivers
Assistantship/fellowship opportunities
Program fit
Advising Matching
GRE requirements - Many units have made this test optional.
TOEFL minimum requirements and TOEFL waivers are determined by the GT Graduate Education Office: https://grad.gatech.edu/english-proficiency . Note home units may required higher scores.
Desired content in Statement of Purpose and Recommendation Letters

For technical application questions, please contact [email protected]:

Creating or using an account login
Forgotten password
Uploading documents
Difficulty with recommender emails
How to access application status information (including application checklist)
Difficulty with the touchnet payment system

For general inquiries about curriculum or program requirements, please see FAQs or contact [email protected] .

Georgia Tech Transfer Students

If you are already enrolled in a Ph.D. program in one of the nine participating schools noted above, you may apply to the ML Ph.D. program as a transfer student. You will be subject to the standard ML curriculum and qualifying requirements, so this is recommended only for graduate students in their first or second year.

Potential transfer students must have a ML PhD Program thesis adviso r who is willing to support them on a research assistantship. For more information, please email [email protected] .

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College of Computing

Ph.d. in machine learning, about the curriculum.

The curriculum is designed with the following principal educational goals:

Research Opportunities

External applications are only accepted for the Fall semester each year. The application deadline varies by home school.

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

From the Catalog:

Carnegie Mellon University School of Computer Science

Machine learning department.

Ph.D. in Machine Learning

Machine learning is dedicated to furthering scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision-making based on that understanding. The doctoral program in machine learning trains students to become tomorrow's leaders in this rapidly growing area.

Joint Ph.D. in Machine Learning and Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop state-of-the-art machine learning technologies and apply these technologies to real-world policy issues.

Joint Ph.D. in Neural Computation and Machine Learning

This Ph.D. program trains students in the application of machine learning to neuroscience by combining core elements of the machine learning Ph.D. program and the Ph.D. in neural computation offered by the Center for the Neural Basis of Cognition.

Joint Ph.D. in Statistics and Machine Learning

This joint program prepares students for academic careers in both computer science and statistics departments at top universities. Students in this track will be involved in courses and research from both the Department of Statistics and the Machine Learning Department.

Visit the Website

Back to Doctoral Programs

More Information

Graduate Studies

Machine Learning and Big Data PhD Track

Optional PhD Tracks: Statistical Genetics , Statistics in the Social Sciences , Machine Learning and Big Data

About The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. Students in this track will have a multidisciplinary experience, taking courses across departments and interacting with faculty and graduate students from these departments. A similar PhD track is being offered in Computer Science and Engineering (CSE), and students from both of these tracks will interact significantly in the core courses.

More details about ML @ UW can be found here and here .

For application details, click here .

Program Requirements

Statistics Core: STAT 570 , STAT 581 , STAT 582
ML/BD Core:
(i) Foundational ML: STAT 535 (ii) One advanced ML course: STAT 538 or STAT 548 (iii) One CSE course: CSE 544 (Databases) or CSE 512 (Visualization) (iv) One MLBD related elective such as a course from the list below and Two electives from the general electives list: * Advanced Statistical Learning ( STAT 538 ) * Machine Learning for Big Data ( STAT 548 ) * Graphical Models ( CSE 515 ) * Visualization (CSE 512) * Databases ( CSE 544 ) * Convex Optimization ( EE 578 )
All other statistics PhD requirements hold, except STAT 571 may be used to satisfy the Applied Data Analysis Project.
STAT 583 is not required.

Advanced Data Science Transcriptable Option A student in the MLBD track can, in addition, choose to enroll/satisfy the Advanced Data Science Option. To further expand students' education and create a campus-wide community, students will register for at least 4 quarters in the weekly eScience Community Seminar . Satisfying this option means that the student will have "ADS" listed on their transcript.

eScience ADSO

ML Lunch Series A lunchtime seminar on a topic related to machine learning is held nearly weekly on Tuesdays during term. Lunch is provided. Updates are posted here .

ML Mailing List General announcements related to machine learning are made on the ML mailing list .

Internal wiki

PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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17 Compelling Machine Learning Ph.D. Dissertations

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 12, 2021 Daniel Gutierrez, ODSC

Working in the field of data science, I’m always seeking ways to keep current in the field and there are a number of important resources available for this purpose: new book titles, blog articles, conference sessions, Meetups, webinars/podcasts, not to mention the gems floating around in social media. But to dig even deeper, I routinely look at what’s coming out of the world’s research labs. And one great way to keep a pulse for what the research community is working on is to monitor the flow of new machine learning Ph.D. dissertations. Admittedly, many such theses are laser-focused and narrow, but from previous experience reading these documents, you can learn an awful lot about new ways to solve difficult problems over a vast range of problem domains.

In this article, I present a number of hand-picked machine learning dissertations that I found compelling in terms of my own areas of interest and aligned with problems that I’m working on. I hope you’ll find a number of them that match your own interests. Each dissertation may be challenging to consume but the process will result in hours of satisfying summer reading. Enjoy!

Please check out my previous data science dissertation round-up article .

1. Fitting Convex Sets to Data: Algorithms and Applications

This machine learning dissertation concerns the geometric problem of finding a convex set that best fits a given data set. The overarching question serves as an abstraction for data-analytical tasks arising in a range of scientific and engineering applications with a focus on two specific instances: (i) a key challenge that arises in solving inverse problems is ill-posedness due to a lack of measurements. A prominent family of methods for addressing such issues is based on augmenting optimization-based approaches with a convex penalty function so as to induce a desired structure in the solution. These functions are typically chosen using prior knowledge about the data. The thesis also studies the problem of learning convex penalty functions directly from data for settings in which we lack the domain expertise to choose a penalty function. The solution relies on suitably transforming the problem of learning a penalty function into a fitting task; and (ii) the problem of fitting tractably-described convex sets given the optimal value of linear functionals evaluated in different directions.

2. Structured Tensors and the Geometry of Data

This machine learning dissertation analyzes data to build a quantitative understanding of the world. Linear algebra is the foundation of algorithms, dating back one hundred years, for extracting structure from data. Modern technologies provide an abundance of multi-dimensional data, in which multiple variables or factors can be compared simultaneously. To organize and analyze such data sets we can use a tensor , the higher-order analogue of a matrix. However, many theoretical and practical challenges arise in extending linear algebra to the setting of tensors. The first part of the thesis studies and develops the algebraic theory of tensors. The second part of the thesis presents three algorithms for tensor data. The algorithms use algebraic and geometric structure to give guarantees of optimality.

3. Statistical approaches for spatial prediction and anomaly detection

This machine learning dissertation is primarily a description of three projects. It starts with a method for spatial prediction and parameter estimation for irregularly spaced, and non-Gaussian data. It is shown that by judiciously replacing the likelihood with an empirical likelihood in the Bayesian hierarchical model, approximate posterior distributions for the mean and covariance parameters can be obtained. Due to the complex nature of the hierarchical model, standard Markov chain Monte Carlo methods cannot be applied to sample from the posterior distributions. To overcome this issue, a generalized sequential Monte Carlo algorithm is used. Finally, this method is applied to iron concentrations in California. The second project focuses on anomaly detection for functional data; specifically for functional data where the observed functions may lie over different domains. By approximating each function as a low-rank sum of spline basis functions the coefficients will be compared for each basis across each function. The idea being, if two functions are similar then their respective coefficients should not be significantly different. This project concludes with an application of the proposed method to detect anomalous behavior of users of a supercomputer at NREL. The final project is an extension of the second project to two-dimensional data. This project aims to detect location and temporal anomalies from ground motion data from a fiber-optic cable using distributed acoustic sensing (DAS).

4. Sampling for Streaming Data

Advances in data acquisition technology pose challenges in analyzing large volumes of streaming data. Sampling is a natural yet powerful tool for analyzing such data sets due to their competent estimation accuracy and low computational cost. Unfortunately, sampling methods and their statistical properties for streaming data, especially streaming time series data, are not well studied in the literature. Meanwhile, estimating the dependence structure of multidimensional streaming time-series data in real-time is challenging. With large volumes of streaming data, the problem becomes more difficult when the multidimensional data are collected asynchronously across distributed nodes, which motivates us to sample representative data points from streams. This machine learning dissertation proposes a series of leverage score-based sampling methods for streaming time series data. The simulation studies and real data analysis are conducted to validate the proposed methods. The theoretical analysis of the asymptotic behaviors of the least-squares estimator is developed based on the subsamples.

5. Statistical Machine Learning Methods for Complex, Heterogeneous Data

This machine learning dissertation develops statistical machine learning methodology for three distinct tasks. Each method blends classical statistical approaches with machine learning methods to provide principled solutions to problems with complex, heterogeneous data sets. The first framework proposes two methods for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The second method provides a nonparametric approach to the econometric analysis of discrete choice. This method provides a scalable algorithm for estimating utility functions with random forests, and combines this with random effects to properly model preference heterogeneity. The final method draws inspiration from early work in statistical machine translation to construct embeddings for variable-length objects like mathematical equations

6. Topics in Multivariate Statistics with Dependent Data

This machine learning dissertation comprises four chapters. The first is an introduction to the topics of the dissertation and the remaining chapters contain the main results. Chapter 2 gives new results for consistency of maximum likelihood estimators with a focus on multivariate mixed models. The presented theory builds on the idea of using subsets of the full data to establish consistency of estimators based on the full data. The theory is applied to two multivariate mixed models for which it was unknown whether maximum likelihood estimators are consistent. In Chapter 3 an algorithm is proposed for maximum likelihood estimation of a covariance matrix when the corresponding correlation matrix can be written as the Kronecker product of two lower-dimensional correlation matrices. The proposed method is fully likelihood-based. Some desirable properties of separable correlation in comparison to separable covariance are also discussed. Chapter 4 is concerned with Bayesian vector auto-regressions (VARs). A collapsed Gibbs sampler is proposed for Bayesian VARs with predictors and the convergence properties of the algorithm are studied.

7. Model Selection and Estimation for High-dimensional Data Analysis

In the era of big data, uncovering useful information and hidden patterns in the data is prevalent in different fields. However, it is challenging to effectively select input variables in data and estimate their effects. The goal of this machine learning dissertation is to develop reproducible statistical approaches that provide mechanistic explanations of the phenomenon observed in big data analysis. The research contains two parts: variable selection and model estimation. The first part investigates how to measure and interpret the usefulness of an input variable using an approach called “variable importance learning” and builds tools (methodology and software) that can be widely applied. Two variable importance measures are proposed, a parametric measure SOIL and a non-parametric measure CVIL, using the idea of a model combining and cross-validation respectively. The SOIL method is theoretically shown to have the inclusion/exclusion property: When the model weights are properly around the true model, the SOIL importance can well separate the variables in the true model from the rest. The CVIL method possesses desirable theoretical properties and enhances the interpretability of many mysterious but effective machine learning methods. The second part focuses on how to estimate the effect of a useful input variable in the case where the interaction of two input variables exists. Investigated is the minimax rate of convergence for regression estimation in high-dimensional sparse linear models with two-way interactions, and construct an adaptive estimator that achieves the minimax rate of convergence regardless of the true heredity condition and the sparsity indices.

8. High-Dimensional Structured Regression Using Convex Optimization

While the term “Big Data” can have multiple meanings, this dissertation considers the type of data in which the number of features can be much greater than the number of observations (also known as high-dimensional data). High-dimensional data is abundant in contemporary scientific research due to the rapid advances in new data-measurement technologies and computing power. Recent advances in statistics have witnessed great development in the field of high-dimensional data analysis. This machine learning dissertation proposes three methods that study three different components of a general framework of the high-dimensional structured regression problem. A general theme of the proposed methods is that they cast a certain structured regression as a convex optimization problem. In so doing, the theoretical properties of each method can be well studied, and efficient computation is facilitated. Each method is accompanied by a thorough theoretical analysis of its performance, and also by an R package containing its practical implementation. It is shown that the proposed methods perform favorably (both theoretically and practically) compared with pre-existing methods.

9. Asymptotics and Interpretability of Decision Trees and Decision Tree Ensembles

Decision trees and decision tree ensembles are widely used nonparametric statistical models. A decision tree is a binary tree that recursively segments the covariate space along the coordinate directions to create hyper rectangles as basic prediction units for fitting constant values within each of them. A decision tree ensemble combines multiple decision trees, either in parallel or in sequence, in order to increase model flexibility and accuracy, as well as to reduce prediction variance. Despite the fact that tree models have been extensively used in practice, results on their asymptotic behaviors are scarce. This machine learning dissertation presents analyses on tree asymptotics in the perspectives of tree terminal nodes, tree ensembles, and models incorporating tree ensembles respectively. The study introduces a few new tree-related learning frameworks which provides provable statistical guarantees and interpretations. A study on the Gini index used in the greedy tree building algorithm reveals its limiting distribution, leading to the development of a test of better splitting that helps to measure the uncertain optimality of a decision tree split. This test is combined with the concept of decision tree distillation, which implements a decision tree to mimic the behavior of a block box model, to generate stable interpretations by guaranteeing a unique distillation tree structure as long as there are sufficiently many random sample points. Also applied is mild modification and regularization to the standard tree boosting to create a new boosting framework named Boulevard. Also included is an integration of two new mechanisms: honest trees , which isolate the tree terminal values from the tree structure, and adaptive shrinkage , which scales the boosting history to create an equally weighted ensemble. This theoretical development provides the prerequisite for the practice of statistical inference with boosted trees. Lastly, the thesis investigates the feasibility of incorporating existing semi-parametric models with tree boosting.

10. Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys

This dissertation develops Bayesian methods for handling unit nonresponse, item nonresponse, and erroneous responses in large-scale surveys and censuses containing categorical data. The focus is on applications of nested household data where individuals are nested within households and certain combinations of the variables are not allowed, such as the U.S. Decennial Census, as well as surveys subject to both unit and item nonresponse, such as the Current Population Survey.

11. Localized Variable Selection with Random Forest

Due to recent advances in computer technology, the cost of collecting and storing data has dropped drastically. This makes it feasible to collect large amounts of information for each data point. This increasing trend in feature dimensionality justifies the need for research on variable selection. Random forest (RF) has demonstrated the ability to select important variables and model complex data. However, simulations confirm that it fails in detecting less influential features in presence of variables with large impacts in some cases. This dissertation proposes two algorithms for localized variable selection: clustering-based feature selection (CBFS) and locally adjusted feature importance (LAFI). Both methods aim to find regions where the effects of weaker features can be isolated and measured. CBFS combines RF variable selection with a two-stage clustering method to detect variables where their effect can be detected only in certain regions. LAFI, on the other hand, uses a binary tree approach to split data into bins based on response variable rankings, and implements RF to find important variables in each bin. Larger LAFI is assigned to variables that get selected in more bins. Simulations and real data sets are used to evaluate these variable selection methods.

12. Functional Principal Component Analysis and Sparse Functional Regression

The focus of this dissertation is on functional data which are sparsely and irregularly observed. Such data require special consideration, as classical functional data methods and theory were developed for densely observed data. As is the case in much of functional data analysis, the functional principal components (FPCs) play a key role in current sparse functional data methods via the Karhunen-Loéve expansion. Thus, after a review of relevant background material, this dissertation is divided roughly into two parts, the first focusing specifically on theoretical properties of FPCs, and the second on regression for sparsely observed functional data.

13. Essays In Causal Inference: Addressing Bias In Observational And Randomized Studies Through Analysis And Design

In observational studies, identifying assumptions may fail, often quietly and without notice, leading to biased causal estimates. Although less of a concern in randomized trials where treatment is assigned at random, bias may still enter the equation through other means. This dissertation has three parts, each developing new methods to address a particular pattern or source of bias in the setting being studied. The first part extends the conventional sensitivity analysis methods for observational studies to better address patterns of heterogeneous confounding in matched-pair designs. The second part develops a modified difference-in-difference design for comparative interrupted time-series studies. The method permits partial identification of causal effects when the parallel trends assumption is violated by an interaction between group and history. The method is applied to a study of the repeal of Missouri’s permit-to-purchase handgun law and its effect on firearm homicide rates. The final part presents a study design to identify vaccine efficacy in randomized control trials when there is no gold standard case definition. The approach augments a two-arm randomized trial with natural variation of a genetic trait to produce a factorial experiment.

14. Bayesian Shrinkage: Computation, Methods, and Theory

Sparsity is a standard structural assumption that is made while modeling high-dimensional statistical parameters. This assumption essentially entails a lower-dimensional embedding of the high-dimensional parameter thus enabling sound statistical inference. Apart from this obvious statistical motivation, in many modern applications of statistics such as Genomics, Neuroscience, etc. parameters of interest are indeed of this nature. For over almost two decades, spike and slab type priors have been the Bayesian gold standard for modeling of sparsity. However, due to their computational bottlenecks, shrinkage priors have emerged as a powerful alternative. This family of priors can almost exclusively be represented as a scale mixture of Gaussian distribution and posterior Markov chain Monte Carlo (MCMC) updates of related parameters are then relatively easy to design. Although shrinkage priors were tipped as having computational scalability in high-dimensions, when the number of parameters is in thousands or more, they do come with their own computational challenges. Standard MCMC algorithms implementing shrinkage priors generally scale cubic in the dimension of the parameter making real-life application of these priors severely limited.

The first chapter of this dissertation addresses this computational issue and proposes an alternative exact posterior sampling algorithm complexity of which that linearly in the ambient dimension. The algorithm developed in the first chapter is specifically designed for regression problems. The second chapter develops a Bayesian method based on shrinkage priors for high-dimensional multiple response regression. Chapter three chooses a specific member of the shrinkage family known as the horseshoe prior and studies its convergence rates in several high-dimensional models.

15. Topics in Measurement Error Analysis and High-Dimensional Binary Classification

This dissertation proposes novel methods to tackle two problems: the misspecified model with measurement error and high-dimensional binary classification, both have a crucial impact on applications in public health. The first problem exists in the epidemiology practice. Epidemiologists often categorize a continuous risk predictor since categorization is thought to be more robust and interpretable, even when the true risk model is not a categorical one. Thus, their goal is to fit the categorical model and interpret the categorical parameters. The second project considers the problem of high-dimensional classification between the two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, it is proposed to perform simultaneous variable selection and linear dimension reduction on original data, with the subsequent application of quadratic discriminant analysis on the reduced space. Further, in order to support the proposed methodology, two R packages were developed, CCP and DAP, along with two vignettes as long-format illustrations for their usage.

16. Model-Based Penalized Regression

This dissertation contains three chapters that consider penalized regression from a model-based perspective, interpreting penalties as assumed prior distributions for unknown regression coefficients. The first chapter shows that treating a lasso penalty as a prior can facilitate the choice of tuning parameters when standard methods for choosing the tuning parameters are not available, and when it is necessary to choose multiple tuning parameters simultaneously. The second chapter considers a possible drawback of treating penalties as models, specifically possible misspecification. The third chapter introduces structured shrinkage priors for dependent regression coefficients which generalize popular independent shrinkage priors. These can be useful in various applied settings where many regression coefficients are not only expected to be nearly or exactly equal to zero, but also structured.

17. Topics on Least Squares Estimation

This dissertation revisits and makes progress on some old but challenging problems concerning least squares estimation, the work-horse of supervised machine learning. Two major problems are addressed: (i) least squares estimation with heavy-tailed errors, and (ii) least squares estimation in non-Donsker classes. For (i), this problem is studied both from a worst-case perspective, and a more refined envelope perspective. For (ii), two case studies are performed in the context of (a) estimation involving sets and (b) estimation of multivariate isotonic functions. Understanding these particular aspects of least squares estimation problems requires several new tools in the empirical process theory, including a sharp multiplier inequality controlling the size of the multiplier empirical process, and matching upper and lower bounds for empirical processes indexed by non-Donsker classes.

How to Learn More about Machine Learning

At our upcoming event this November 16th-18th in San Francisco, ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on machine learning and machine learning research. You can register now for 50% off all ticket types before the discount drops to 40% in a few weeks. Some highlighted sessions on machine learning include:

Towards More Energy-Efficient Neural Networks? Use Your Brain!: Olaf de Leeuw | Data Scientist | Dataworkz
Practical MLOps: Automation Journey: Evgenii Vinogradov, PhD | Head of DHW Development | YooMoney
Applications of Modern Survival Modeling with Python: Brian Kent, PhD | Data Scientist | Founder The Crosstab Kite
Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems: Veena Mendiratta, PhD | Adjunct Faculty, Network Reliability and Analytics Researcher | Northwestern University

Sessions on MLOps:

Tuning Hyperparameters with Reproducible Experiments: Milecia McGregor | Senior Software Engineer | Iterative
MLOps… From Model to Production: Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co
Operationalization of Models Developed and Deployed in Heterogeneous Platforms: Sourav Mazumder | Data Scientist, Thought Leader, AI & ML Operationalization Leader | IBM
Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber: Eduardo Blancas | Data Scientist | Fidelity Investments

Sessions on Deep Learning:

GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow: Ajay Baranwal | Center Director | Center for Deep Learning in Electronic Manufacturing, Inc
Machine Learning With Graphs: Going Beyond Tabular Data: Dr. Clair J. Sullivan | Data Science Advocate | Neo4j
Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0: Oliver Zeigermann | Software Developer | embarc Software Consulting GmbH
Get Started with Time-Series Forecasting using the Google Cloud AI Platform: Karl Weinmeister | Developer Relations Engineering Manager | Google

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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Machine Learning Career: Pros and Cons of Having a PhD

September 25, 2021 at 4:30 pm November 28, 2022 at 12:02 pm

It is often said that data science jobs are for seasoned professionals, and many job ads still show a preference for a profile with a PhD, with years of experience. Yet, many corporate employers have been disillusioned about the value that a PhD brings to the company. Likewise, many professionals, especially among those who just completed a PhD and were offered their first job, find the work sometimes unrewarding.

A PhD may command a slightly higher salary initially, and may be required for a position in a research lab (whether private or government-operated). But for many positions, it may not bring an advantage. Corporate work can be mundane and fast-paced, and the search for perfect algorithms is discouraged, as it hurts ROI. In many companies, a solution close to 80% of perfection is good enough, and requires far less time than reaching 99% perfection, especially since the machine learning models employed are just an approximation of the reality. People with a PhD are not well prepared for that.

Here are some of the negative aspects.

Even if you pay someone to write your PhD thesis (such services exist), you may spend several years of your life working on your PhD, possibly in a stressful environment, with low pay, delaying buying a home, or getting married. Meanwhile, you see your non-PhD friends ahead of you in their personal life. If you married when working on your PhD, this could eliminate some of these problems.
Some recruiters may say that you are over-qualified, that your experience is not really relevant to the job you are applying for (or too specialized), and that adapting to a fast-paced corporate environment might be challenging.
If you land a job in the corporate world, you might find it menial or boring. You could be disappointed that the research you did during your PhD years is a thing of the past, not leading to anything else. This is especially true if your hope was to get a tenured position in the academia, but can’t get one despite your very strong credentials, due to the fierce competition. It can bring long-lasting regrets and nostalgia.
You may be lacking some coding skills (SQL in particular), which put you at a disadvantage against a candidate with an applied master. Of course, it is always possible and desirable to gain these skills on your own (or via data camps) when working on your PhD.
Your salary might not be higher than that of a younger candidate with a master degree and the right experience. Your cumulative wealth over your lifetime may be lower.
Some employers (Google, Facebook, Microsoft, Wall Street, or defense-related companies) routinely hire PhD’s to work on truly exciting projects. Some only hire from top universities and if your PhD was not from an ivy-league, you will be by-passed. That said, there are plenty of companies that will hire non ivy-league candidates, and I think that’s a smart move. After all, I earned my PhD in some unknown university, and eventually succeeded in the corporate world.

For some, the pros outweigh the cons by a long shot. This was my case. I provide a few examples below.

If your PhD was very applied in a hot field (in my case in 1993, processing digital satellite images for pattern detection), you learned how to code, played with a lot of messy data, and even got part-time job in the corporate world, related to your thesis when working on it, then you are up to a good start. In my case, solid funding for the research, and even data sets, came from governmental agencies (EU and others) and private companies (Total, for instance) trying to solve real problems. This adds credibility to your PhD experience. On the downside, my mentor was not a great scholar, but a good salesman able to attract many well paid contracts.
If you earned your PhD abroad like I did, it is quite possible that you were paid better than your peers in US. In my case, my salary, as a teaching assistant, was similar to that of a high school teacher. And conference attendance (worldwide) was paid by the university or by the agencies that invited me as a speaker. Coming from abroad is sometimes perceived as an advantage, due to showing cultural adaptation, and in most cases, being multilingual and able to easily relocate in various locations if corporate needs ask for it.
You can still continue to do your research, decades after leaving academia. I still write papers and books to this day. The level is even higher than during my PhD years, but the style and audience is very different, as I try to present advanced results, written in simple English, to a much larger audience. I find this more rewarding than publishing in scientific journals, read by very few, and obfuscated in jargon.
There are great positions in many research labs, private or government, available only to PhD applicants. The salary can be very competitive.
VC funding is usually contingent to having a well-known PhD scientist on staff, for startup companies. So if you create your own startup, or work for one, a PhD is definitely an advantage. Even when I started my own, self-funded publishing / media company (acquired by Tech Target in 2020, and focusing on machine learning), my wife keeps reminding me that I would have had considerably less success without my education, even though you don’t legally need any degree or license to operate this kind of business.

Conclusions

Having a PhD can definitely offer a strong advantage. It depends on the subject of your thesis, where you earned your PhD, and if you worked on real-life problems relevant to the business world. More theoretical PhD’s can still find attractive jobs in various research labs, private or government. The experience may be more rewarding, and probably less political, than a tenured position in academia. It goes both ways: it is not unusual for someone with a pure corporate / business background, to make a late career move to academia, sometimes in a business-related department. Or combining both: academia and corporate positions at the same time.

I wrote an article in 2018, about how to improve PhD programs to allow for an easy transition to the business world. I called it a doctorship program, and you can read about it here . I will conclude by saying that another PhD scientist, who earned his PhD in the same unknown math department as me at the same time (in Belgium), ended up becoming an executive at Yahoo, after a short stint (post-doc) at the MIT, working on transportation problems. His name is Didier Burton. Another one (Michel Bierlaire), same year, same math department, also with a short post-doc stint at MIT (mine was at Cambridge University), never got a corporate job, but he is now an happy full professor at EPFL. Also, a Data Science Central intern (reporting to me), originally from Cuba and with very strong academic credentials (PhD, Columbia University, EPFL) got his first corporate job after his internship with us (I strongly recommended him). Despite a mixed academic background in physics and biology, he is now chief data scientist of a private company. His name is Livan Alonso.

About the Author

Vincent Granville is a pioneering data scientist and machine learning expert, founder of MLTechniques.com and co-founder of Data Science Central (acquired by TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS).

Vincent published in Journal of Number Theory , Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence . He is also the author of multiple books, available here . He lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.

Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
Our students receive job offers from top academic and non-academic institutions .
Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
GRE requirement: Waived for Fall 2024.
Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
Deadline: Jan 8, 2024 .
Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
Timeline: P.hD students begin the program in September only. Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
What are the required application materials? Specific admission requirements for our programs can be found here .
Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle.
Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
What is the minimum TOEFL or IELTS requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously? For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences. Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

This option is only available for fall-term applicants.
Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University 107 Low Library, MC 4303 535 West 116th Street New York, NY 10027
How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
How do I find out which professors are taking on new students to mentor this year? Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at [email protected]

Upcoming Events

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.

Throughout the coursework, you will:

Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
Learn how to approach and solve large-scale data science problems.
Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world.

A Powerful Certificate. Conveniently Offered.

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known.

For Today’s Problem Solvers

This leading certificate program is best suited for:

Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.

At a Glance

Start Date May 2024

Application Deadlines Rolling Admissions

We are still accepting applications for a limited number of remaining spots to start in Summer 2024. Apply today to secure your space in the program.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to [email protected] with your inquiries .

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more. Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction.

Number One in the nation for our artificial intelligence programs.

Number One in the nation for our programming language courses.

Number Four in the nation for the caliber of our computer science programs.

students walking across Cal's campus with Campanile in the background

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This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.

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MSU graduate student researches artificial intelligence, machine learning to glean vital ag info

Contact: Samuel Hughes

STARKVILLE, Miss.—Collecting valuable agricultural and environmental information from the ground can be challenging, but a Mississippi State graduate student is improving the ability of artificial intelligence to effectively use remote sensing data for better insight.

Dakota Hester, a first-generation doctoral student in MSU’s Department of Agricultural and Biological Engineering, is completing a competitive, six-month internship as a machine learning scientist at multinational healthcare and agriculture corporation Bayer.

Hester’s expertise is specifically concentrated on improving deep-learning land cover mapping, which can have powerful implications in agriculture and forestry. Hester’s Bayer Crop Science research has similar goals to his ongoing graduate research at MSU: improving the ability of remote sensing—satellite data and aerial photographs—to cost-effectively increase understanding of agricultural processes and natural resources. While collecting data from the ground can be expensive, cumbersome and time-consuming, satellite imagery is readily available.

“Remote sensing is the best way to increase our understanding of natural resources and how crops and plants grow, without necessarily needing someone on the ground monitoring every single crop day in and day out,” Hester said. “Once we hit a certain resolution, we can extract much more information than what has previously been available in the past 10 to 15 years because of new technologies in satellites, remote sensing and artificial intelligence.

“Bayer has given me access to and experience with novel tools and technology. I’m excited to return to our research lab and leverage some of the techniques they use,” Hester said.

While Hester’s Bayer research primarily relates to agriculture, his work in MSU’s Department of Agricultural and Biological Engineering hopes to use deep learning to map entire states.

“Being at MSU has definitely opened up a ton of different opportunities for me. I don’t think I would have gotten this internship had I not been involved in the research that is being done at Mississippi State,” Hester said. “I’ve gotten to meet so many wonderful scientists and researchers from across the globe, and honestly that’s the highlight for me—it’s the people.”

The Tishomingo native credits his interest in agricultural science to his father, a field mechanic for a logging equipment supplier.

“Particularly during the summers when school was out, he would take me into the field with him. I got a firsthand look at what the natural resources industry looks like,” Hester said. “That definitely shaped what I wanted to pursue later in life as I was going through undergrad, and my focus has shifted away from programming and software engineering toward sciences and natural resources—how we can extract the most value from what we have naturally growing in Mississippi.”

After completing his bachelor’s degree in computer science at MSU in 2021, Hester worked as a graduate research assistant in MSU’s Department of Sustainable Bioproducts, where he researched a fully automated, artificial intelligence application that identifies species of wood with high accuracy using scans of the wood’s surface.

Hester then moved in 2023 to the agricultural and biological engineering research lab of Assistant Professor Vitor S. Martins, where he conducts current research on deep machine learning. He hopes to continue this after obtaining his doctorate, which he expects to complete in 2025.

The MSU Department of Agricultural and Biological Engineering focuses on engineering and technology for agriculture and natural resources, including autonomous agricultural systems and precision agriculture, as well as ecological engineering and sustainable energy. The department has split responsibilities in the College of Agriculture and Life Sciences, Bagley College of Engineering, Mississippi Agricultural and Forestry Experiment Station and MSU Extension Service. For more on the department, visit www.abe.msstate.edu .

Mississippi State University is taking care of what matters. Learn more at www.msstate.edu .

Wednesday, April 10, 2024 - 3:07 pm

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PhD student in Generative Machine Learning & Computational Statistics

Your work assignments.

We are looking for a PhD student working in the intersection of generative machine learning and computational statistical inference. Generative models based on diffusion processes have emerged as a prominent approach to machine learning with impressive performance in many application domains. A well-known use case is for image generation (these models are the main workhorse for tools such as DALL-E and Stable Diffusion) but the same technology has also shown great promise in applications as diverse as probabilistic weather forecasting, biochemistry, and materials discovery.

Conceptually, training a generative model is similar to solving a conventional statistical learning problem. Guided by this similarity, the research focus of the current position is to answer the questions:

Can we leverage recent advances in generative AI for solving statistical learning problems?
Can we leverage state-of-the-art statistical inference methods for improving generative modeling?

We will address these questions through novel methodological research resulting in new machine learning models and computational algorithms. We will also work on applied research to demonstrate the usefulness of the new methods, with particular emphasis on the application domains listed above. This is made possible by our active collaborations with applied researchers and domain experts within all of these fields.

As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20% of full-time. The work assignments also include actively contributing to the collaborative environment within which the project will be carried out (read more under “Your workplace” below).

N.B. When applying for the position we want you to provide a personal letter (first field in the application form). This letter should contain a paragraph where you briefly explain/list the qualifications that you believe are particularly relevant for the research topic described above. This paragraph should start with the words “ Suitability for research topic: ”.

Your qualifications

You have graduated at Master’s level in machine learning, statistics, computer science, or a related area that is considered relevant for the research topic of the project, or have completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in the subject areas mentioned above. Alternatively, you have gained essentially corresponding knowledge in another way.

A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. The applicant should furthermore have a strong drive towards performing fundamental research; the ability and interest to work collaboratively; and strong communication skills. The applicant should be able to communicate freely in oral and written English.

Your workplace

Linköping University is one of the leading AI institutions in Sweden. We have strong links to prominent national research initiatives, such as WASP and ELLIIT and you will have access to state-of-the-art computing infrastructure for machine learning, e.g. through BerzeLiUs .

The advertised position is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society and industry. Read more: https://wasp-sweden.org/

The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/

The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science. At STIMA we conduct research and education in both statistics and machine learning, at the undergraduate, advanced and PhD levels. We regularly publish solid contributions at the best machine learning conferences. STIMA is characterized by a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning which clearly integrates the two subject areas within the division. For more information about STIMA, please see https://liu.se/en/organisation/liu/ida/stima

The project will be carried out in a collaboration between STIMA (main supervisor: Fredrik Lindsten, senior associate professor in machine learning) and the Division of Systems and Control at Uppsala University (co-supervisor: Jens Sjölund, [email protected] , assistant professor in AI). We will strive for a tight collaboration between the groups, including regular meetings and research visits. As a PhD student in the project, you are expected to actively engage in the teamwork and contribute to this collaboration.

The employment

When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University

The employment has a duration of four years’ full-time equivalent. You will initially be employed for a period of one year. The employment will subsequently be renewed for periods of maximum duration two years, depending on your progress through the study plan. The employment may be extended up to a maximum of five years, based on the amount of teaching and departmental duties you have carried out. Further extensions can be granted in special circumstances.

Starting date by agreement.

Salary and employment benefits

The salary of PhD students is determined according to a locally negotiated salary progression.

More information about employment benefits at Linköping University is available here.

Union representatives

Information about union representatives, see Help for applicants .

Application procedure

Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than May 3, 2024.

Applications and documents received after the date above will not be considered.

Contact persons

Fredrik Lindsten

Associate professor, Head of division STIMA

+46 734 20 16 00

[email protected]

Sofie Bondesson

HR Administartor

[email protected]

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Carnegie Mellon University School of Computer Science

Ph.D. in Machine Learning

Joint Ph.D. in Machine Learning and Public Policy

Joint Ph.D. in Neural Computation and Machine Learning

Joint Ph.D. in Statistics and Machine Learning

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