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Doctoral degree program in computer science.

The goal of a doctorate in computer science is the development and dissemination of new knowledge in computer science. A doctorate usually takes three to four years and ends with the award of a doctorate in computer science.

A doctorate is carried out in close collaboration with a supervisor from the Institute of Computer Science. Possible research topics are mostly in the areas covered by the research groups of the institute. These include computer graphics, computer vision, communication and distributed systems, software composition, and logic and theory. Interested persons can contact a leader of a research group directly to clarify the possibility of a doctorate and to discuss potential research topics.

It is expected that scientific papers will be independently prepared during a doctoral degree and published in international journals. Research results will also be presented at conferences in lectures and posters. Furthermore, cooperation with international research groups is often one of the activities during a doctorate. The dissertation ends with a written thesis and a final examination in the form of a lecture.

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• Studienplan für das Fach Informatik (Bachelor, PhD, Minor) vom 01.08.2011 mit Änderungen (PDF, 62KB)
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• Promotionsreglement der Philosophisch-naturwissenschaftlichen Fakultät der Universität Bern (PromR Phil.-nat. 19) vom 12.12.2019 (PDF, 1.1 MB)
• Regulations governing doctoral studies at the Faculty of Science of the University of Bern (PromR Phil.-nat. 19) of December 12., 2019 (PDF, 244KB)

Universität Fribourg Course offerings

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Computer Science

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Fascinated by software , logic , computing infrastructures or artificial intelligence ? Take your career to the next level with a PhD in Computer Science!

Why study Computer Science? Computer Science is today at the heart of our society. Computational techniques are transforming all aspects of our daily lives, from Smart Cities infrastructures to transportations, banking, the media or the manufacturing industry. Demand for highly qualified computer scientists is high, both in Switzerland and around the world. With a PhD in Computer Science, you will be able to face the tsunami of data that we are confronted to and help both our society and companies evolve towards a more efficient digital society.

Our PhD Programme in Computer Science The University of Fribourg is proud to offer a PhD programme in Computer Science that will make you ready to tackle key scientific problems both for academia and the industry. Computer Science has a transformative impact on many facets of our society. Research topics that can be explored as part of a PhD in Computer Science at the University of Fribourg are quite diverse, and include:

• Applied Statistics and Modelling
• Decision Support and Operations Research
• Document, Image and Video Analysis
• Data Science and Big Data Infrastructures
• Foundations of Dependable Systems
• Digitalisation and Information Systems
• Software Engineering
• Human Computer Interaction
• Smart Cities and Cognitive Computing

Apart from detailed knowledge in the field, PhD students will also learn to conduct independent research projects, to guide bachelor and master students, to interpret and present scientific data, and to put their work into a general context. We also actively promote exchanges and interactions with other universities and with companies by organising workshops and research visits. We give all our PhD students the opportunity to present their work at international conferences and to meet people working on similar questions from close and far.

PhD students also have the opportunity to take graduate courses, by following free courses and workshops organised by the CUSO Doctoral Program in Computer Science in Western Switzerland. Funding for PhD projects is available in two main ways: 1) through project funds attributed to individual research groups. Interested candidates are encouraged to select a research group in which they would like to carry out their PhD (see Contact for a current list) and contact the group leader directly to ask for a possible opening; 2) through funds acquired by the PhD candidate (including Swiss Government Excellence Scholarships for Foreign Scholars and Artists : http://studies.unifr.ch/go/en-swiss-gov-scholarships ). Before applying for funds, candidates should contact the group leader they would like to work with.

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Structure of studies.

No ECTS credits can be earned.

Doctoral school

https://informatique.cuso.ch

In order to be admitted to a doctorate the candidate must have been awarded an academic bachelor's and master's degree or an equivalent qualification from a university recognised by the University of Fribourg.

Before applying for a doctorate the candidate must contact a professor who would be willing to supervise the thesis work.

There is no general right to be admitted to a doctorate.

The respective conditions of admission for each doctoral study programme are reserved.

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Department of Informatics

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The Doctoral Program of Informatics is where academia and industry meet, where theories are developed and challenged in praxis, where new knowledge is created, and where scholars emerge. The Department of Informatics offers Switzerland’s best application oriented informatics and, internationally, ranks among the best institutions in its field. Application areas range from business information systems to social sciences, linguistics, natural sciences, and medicine. The Department creates joint research ventures and integrates their results in the teaching of students. In addition, internationally renowned visiting professors regularly give courses on special topics. All of us in the Department of Informatics wish you good fortune as you pursue your advanced degree, and we hope you will contact us if we can help you along the way.

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PhD in Information Systems

The doctoral program in Information Systems supports doctoral students in developing their ability to complete an independent scientific study and to add to the body of knowledge in research fields that are closely related to new technologies and digital innovation. Successful applicants must hold an MSc degree in Information Systems, Computer Science, or an equivalent degree (with a minor in Information Systems / min. 30 ECTS). The Ph.D. in Information Systems opens the way for an academic career, and it also offers employment opportunities in consulting, large corporations, or public institutions. The Ph.D. is also an excellent starting point for entrepreneurial activities.

Undertaking doctoral studies requires a strong personal commitment for three to five years. During the first phase, the doctoral candidate acquires methodological skills and domain-specific knowledge through a tailor-made course program (minimum 18 ECTS). The candidate also performs a critical reading exam and defends a thesis proposal within the first two years . The second phase is dedicated to research projects, and the candidate prepares submissions to international conferences and journals in Information Systems and Computer Science. Candidates complete their Ph.D. with the defense of their dissertation.

Doctoral Program in Information Systems University of Lausanne HEC Lausanne Internef 234 Quartier de Dorigny CH-1015 Lausanne

sarah.duplan@unil.ch +41 21 692 35 80

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The ICS offers three possible career paths for prospective graduate students:  Graduate School for Computational Science ,  Doctoral Programme in Data Science ,  and  Doctoral Programme in Theoretical Astrophysics and Cosmology .

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• 10 April 2024

Randomness in computation wins computer-science ‘Nobel’

• Davide Castelvecchi

You can also search for this author in PubMed   Google Scholar

Avi Wigderson received the Turing Award for his foundational contributions to the theory of computation. Credit: Dan Komoda

A leader in the field of computational theory is the latest winner of the A. M. Turing Award, sometimes described as the ‘Nobel Prize’ of computer science.

Avi Wigderson at the Institute for Advanced Study (IAS) in Princeton, New Jersey, is known for work straddling several disciplines, and had already won a share of the Abel Prize , a top mathematics award, three years ago.

He receives the Turing Award “for foundational contributions to the theory of computation, including reshaping our understanding of the role of randomness in computation, and for his decades of intellectual leadership in theoretical computer science”, the Association for Computing Machinery (ACM) in New York City announced on 10 April.

“I was extremely happy, and I didn’t expect this at all,” Wigderson tells Nature . “I’m getting so much love and appreciation from my community that I don’t need prizes.”

‘A towering intellectual force’

Wigderson was born in Haifa, Israel, in 1956. He studied at Technion — Israel Institute of Technology in Haifa and later at Princeton University; he has been at the IAS since 1999. He is known for his work on computational complexity — which studies how certain problems are inherently slow to solve, even in principle — and on randomness in computation. Many practical algorithms make random choices to achieve their objectives more efficiently; in a series of groundbreaking studies in the 1990s, Wigderson and his collaborators showed that conventional, deterministic algorithms can, in principle, be roughly as efficient as ‘randomized’ ones 1 . The results helped to confirm that random algorithms can be as accurate as deterministic ones are.

“Wigderson is a towering intellectual force in theoretical computer science,” said ACM president Yannis Ioannidis in a statement. In addition to Wigderson’s academic achievements, the ACS cited his “friendliness, enthusiasm, and generosity”, which have led him to be a mentor to or collaborate with hundreds of researchers worldwide. Wigderson admits that he is a “big proselytizer” of the intellectual pleasures of his discipline — he wrote a popular book about it and made it freely available on his website . “I think this field is great, and I am happy to explain it to anybody.”

The Turing Award is named after the celebrated British mathematician and code-breaker Alan Turing (1912–54), who in the 1930s laid the conceptual foundations of modern computing. “I feel completely at home with mathematics,” says Wigderson, adding that as an intellectual endeavour, theoretical computer science is indistinguishable from maths. “We prove theorems, like mathematicians.”

doi: https://doi.org/10.1038/d41586-024-01055-y

Impagliazzo, R. & Wigderson, A. in Proc. 29th ACM Symposium on Theory of Computing 220–229 (ACM, 1997).

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Graduate & Professional Studies

Stories of Graduate Student Success: Saba Heidari, doctoral student in the Department of Computer Science

• April 16, 2024
• 2 min. read

Saba Heidari is a doctoral student in the Department of Computer Science at Marquette University studying adversarial attacks on neural networks. She participated in the spring 2024, Three Minute Thesis (3MT) Competition  and qualified as one of the top ten event finalists. The 3MT is a global research competition that challenges participants to effectively explain their research in three minutes or less to a non-specialist audience. Participants can win prizes and represent Marquette University at the regional competition.

Heidari’ s 3MT presentation was titled, “ Adversarial Attacks on Graph Neural Networks” and her research focuses on the field of machine learning, specifically addressing adversarial attacks as well as enhancing the robustness of neural networks, with a particular emphasis on Graph Neural Networks (GNNs). The core of Heidari’ s research studies involves leveraging medical images to augment the reliability of neural networks within the medical domain.

When asked why she entered the 3MT competition, Heidari said she wanted to help raise awareness about the vulnerability of adversarial attacks. She noted that “we often hear about artificial intelligence (AI) and its usefulness in making decisions. However, it’s important to understand that AI systems can be misled by manipulated inputs, leading them to make incorrect decisions. Given that AI is increasingly being used in critical areas like self-driving cars and healthcare, it’s crucial to study how these systems can be vulnerable to such attacks. By doing so, we can work on strengthening AI systems to better withstand these challenges.”

Heidari is a research assistant in the Machine Learning, Optimization and Data Lab (MODLab) under the supervision of Dr. Nasim Yahyasoltani. She is passionate about the field of machine learning and has more than twelve journal and conference publications as well as fifty+ citations recorded in her research area.

When Hedari is not engaging in research, she enjoys spending her leisure time engaging in photography, reading books, and practicing yoga.

Want to read similar stories or have a success story to share? Visit  Stories of Graduate Student Success  to learn more.

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Stories of Graduate Student Success: Adebayo Adeleye, doctoral student in the Department of Chemistry

Four Ph.D. Students Honored with Ann G. Wylie Dissertation Fellowship

The University of Maryland's Graduate School has announced Department of Computer Science graduate students Nakul Garg , Shoken Kaneko ,  Mazda Moayeri and Gowthami Somepalli as recipients of the Ann G. Wylie Dissertation Fellowship , an award recognizing outstanding research contributions and academic performance by doctoral students in the later stages of their dissertation research.

The Ann G. Wylie Dissertation Fellowship is a testament to the university's commitment to fostering academic excellence and research innovation. It includes a $15,000 stipend, a candidacy tuition award and additional benefits to facilitate the completion of innovative dissertation work.

Inaugurated in 2005, the award is named in honor of Department of Geology Professor Emerita Ann G. Wylie . The fellowship underscores the importance of academic and research endeavors at the University of Maryland. It is a key component of the Graduate School's efforts to nurture doctoral candidates' academic and professional development. 

The research focuses of the awardees are:  

Garg is a Ph.D. student advised by Assistant Professor Nirupam Roy . His research focuses on the development of sustainable computing technologies. Garg primarily deals with batteryless, AI-driven ambient computing technologies that enhance operational longevity and intelligence while reducing power requirements. His work is intended to support the creation of smarter cities, increase supply chain efficiencies and further develop advanced healthcare systems with a reduced environmental footprint.

In addition to his fellowship, Garg has collaborated with Microsoft Research to investigate AI solutions in supply chains, specifically focusing on dynamic tracking systems to reduce global food waste. His projects also include developing next-generation batteryless tags for applications like geofencing, wildlife monitoring and environmental sensing.

Garg plans to explore ambient computing's capabilities further using AI and machine learning to address challenges in egocentric sensing, perception and communications. After completing his Ph.D., he is considering founding a company to commercialize his research and maintain his contributions to the academic field.

Shoken Kaneko

Kaneko is a Ph.D. student advised by Professor Ramani Duraiswami . His work focuses on computational audio and acoustics, specifically spatial audio and boundary element analysis. His work aims to improve numerical simulations in acoustics and electrostatics, enhancing accuracy and reducing costs.

Kaneko has developed algorithms that could significantly improve the efficiency of numerical simulations. 

"The methods I've worked on improve spatial audio capture and processing, audio rendering, and sound localization," Kaneko explained. "My research could fundamentally change how we simulate and interact with audio in real-world and virtual environments, like virtual reality and the metaverse."

Looking ahead, he plans to refine spatial audio technologies and further develop engineering tools for audio and acoustics, aiming to enhance how audio is integrated and manipulated in physical and digital spaces.

Mazda Moayeri

Mazda Moayeri is a Ph.D. student advised by Associate Professor Soheil Feizi . Moayeri's research centers on building interpretability tools for artificial intelligence, aiming to enhance transparency and mitigate the risks associated with AI. His research addresses potential limitations within AI systems to prevent harmful impacts, making strides toward safer, more reliable and more equitable AI applications across different societal sectors.

Moayeri's work promises to fundamentally alter how society interacts with AI by making the technology's inner workings more accessible and understandable.

"AI is incredible because it can be applied to so many problems, and it is advancing at an astonishing rate," Moayeri explained. "A technology so wide-reaching is exciting but also scary, as the risks rise with the number of use cases, especially since we don't always know what's going on under the hood. My work aims to create a future where people can confidently decide when to rely on AI by enhancing the technology's transparency and proactively addressing bias issues before they cause harm."

Looking forward, Moayeri plans to continue exploring the field of AI, likely transitioning to industry roles that emphasize the societal impacts of technological advancements.

"I truly believe AI can empower all of us, but I also worry that it may widen existing gaps in our society," Moayeri stated. "I hope to build tools that put the power of AI in people's hands instead of cruelly replacing them. The exact problems I work on will evolve as the field does, which it currently is doing, but I will always care about fair AI, bias mitigation, model debugging and increasing transparency."

Gowthami Somepalli

Somepalli is a Ph.D. student advised by Professor Tom Goldstein . Her research focuses on identifying and addressing failure modes in multimodal deep learning models, aiming to enhance their reliability and functionality. Somepalli's work has notably included a study on understanding and memorization in diffusion models, which has significant implications for their practical application across various industries.

In addition to her primary research focus, Somepalli is enhancing vision large language models (LLMs), exploring ways to bolster their efficiency and applicability. This work aligns with her broader objectives to address critical challenges in deep learning technologies.

"My work aims to ensure that deep learning systems have a significantly reduced failure rate before they can be utilized on a large scale," Somepalli explained. "The application of my research in diffusion models was notably referenced during the Stable Diffusion lawsuit, and Stability AI has incorporated it in their recent SD3 model to minimize memorization."

Looking ahead, Somepalli plans to continue her research on improving multimodal systems. "Tackling the robustness of these systems is both a challenging and essential task," she remarked, emphasizing the importance of her future endeavors in contributing to the field of artificial intelligence.

—Story by Samuel Malede Zewdu, CS Communications 

The Department welcomes comments, suggestions and corrections.  Send email to editor [-at-] cs [dot] umd [dot] edu .

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PhD student receives fellowship from Apple Scholars program

Nataliya Nechyporenko, a computer science Ph.D. student, has received a PhD fellowship in AI and Machine Learning (AIML) through the Apple Scholars program . The program was created by Apple to recognize the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. 

The fellowship provides Nechyporenko support for her research and academic travel for two years, internship opportunities and a two-year mentorship with an Apple researcher. 

Let's learn more about Nechyporenko's research aims and her perspective on the future of robotics research: 

What research do you hope to accomplish through this fellowship?

Think about how you might manually feel around an object to understand its shape, weight, and texture. Or if something is in your way, you'd just push it aside without overthinking it. If you drop something, you'll persistently keep trying to pick it up from different angles until you get it. As you're doing these everyday tasks, you're constantly building up an intuitive sense of your surroundings through trial-and-error. That's the kind of resourceful, flexible, multi-sensory approach I want robots to have when manipulating things – rather than just blindly following a fixed routine. 

The goal is for robotic arms to move and behave with that same kind of curious, improvisational, problem-solving spirit we take for granted as humans. As an Apple AIML scholar, I hope to gain insights into this problem with the help of a fresh network of mentors and collaborators.  

Is this an extension of work you are already doing in your lab? If so, how?

Driven to establish contact-rich planning as a dominant feature in robotics, I focused the first two years of my PhD on analyzing the methods used by state-of-the-art planners and solving the shortcomings leading to the lack of physical robot interaction. 

I have started to extend this work by integrating the empirical formulation of machine learning with model-based algorithmic approaches. I believe this is the path to making robots more adaptable to chaotic human environments. I will continue this work as an Apple scholar. 

What do you think of the current hype around AI and ML? What do you wish people understood about this research area?

The AI and machine learning hype trains have been barreling full steam ahead lately. But robotics? That's an entirely different beast that doesn't follow the overnight disruption narratives. It's a synergy of achievements in areas like materials, manufacturing, sensing, controls theory, and others aligning to reshape the physical world. 

The robotics future will reshape industries and labor concepts, but it will be catalyzed through the patient advancement of many disciplines.

How did you come to study at CU Boulder?

I spent a couple years in the trenches, getting my hands dirty actually building and deploying robots in industry. But after a while, I got this craving -- like there was so much more potential waiting to be unlocked if I could really dive into the deep scientific questions around robotics. That's why I decided to take the plunge back into academia.

What is one of your plans or hopes for the future, either professionally or personally?

I hope to be an expert, a leader, a thinker and a builder. Outside of research endeavors, I aim to be a leader and educator for the robotics and the AI community. Previously, I’ve led volunteering activities, mentored students, and co-organized events that foster discussions around AI. I hope to continue to do so in the future at a larger scale. 

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Nataliya Nechyporenko

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Engineering student works to create safer ground transportation

Sai bonthu is a doctoral candidate at the university of cincinnati.

Ground transportation has a substantial impact on the daily lives of humans. Sai Bonthu, doctoral student of electrical and computer engineering at the University of Cincinnati, is working to develop a product that has the potential to accurately reduce traffic accidents.

Currently, he is working on a grant with the Ohio Department of Transportation and was named Graduate Student Engineer of the Month by the College of Engineering and Applied Science. 

Why did you choose UC? What drew you here?

Sai Bonthu is working with the Ohio Department of Transportation to create a product that has the potential to accurately reduce traffic accidents. Photo/provided

Coming to UC was a relatively easy decision for me. After completing my master's in electrical engineering at Akron in 2017, I was hired as an electrical engineer at Cincinnati Incorporated, making UC a natural destination for my PhD. 

With automation being the core of the machining world, I wanted to utilize the opportunity of pursuing a PhD at UC under the guidance of esteemed professors in the Department of Electrical and Computer Engineering. 

Doctors Arthur Helmicki and Victor Hunt have advised me during my studies and as I have worked closely with them at the UC Infrastructure Institute (UCII) , they have demonstrated extensive experience in applying control systems and machine learning algorithms to real-world applications such as smart mobility and civil infrastructure monitoring. 

Why did you choose your field of study?

Electrical engineering draws certain characteristics from individuals pursuing the highest level of the study. Back in 2012, I earned my bachelor's in electrical and electronics engineering. However, it alone wasn't enough for me to standout in a competitive environment. In a discussion with my sister, I realized that there weren't many opportunities to learn the toughest part of electrical engineering — power electronics and electric motor drives. That's how I got into a master's program at Akron and then successfully landed a job in Cincinnati. From then on, I wanted to earn a PhD. UC has a wide range of opportunities to dig deeper into electrical engineering and apply my skills towards researching something more meaningful to me and the community I live in. 

Professor Helmicki (my research adviser) and I have secured a National Science Foundation (NSF) Innovation Corps (I-Corps) award followed by a research grant from the Ohio Department of Transportation. These awards empowered me to pursue an exciting research project with a real-world case that transformed my life in the last few years. I'm also grateful to the consistent support that I've received from my superiors at Cincinnati Incorporated and HDS Global to pursue my dream while working full-time. 

Briefly and simply describe your research work. What problems do you hope to solve?

Sai Bonthu is working with the Ohio and U.S. Departments of Transportation on his PhD research. Photo/Kristian McNeal, The Gaskins Foundation

Ground transportation has one of the most significant impacts on human life. According to National Highway Traffic Safety Administration (NHTSA), in 2021, traffic fatalities were 10.5% higher than the previous year at 42,915. During the NSF I-Corps program, our team conducted more than 175 stakeholder interviews and converted raw transportation data into inputs for generating real-time safety responses in traffic operations as well as producing insights for transportation planners. Transportation planners and engineers have identified specific transportation problems such as an increase in traffic crashes and the lack of accuracy validation in road user detection, classification and localization of vulnerable road users. 

My PhD research focuses on the development of a product that can potentially accurately reduce traffic crashes. The outcomes will benefit UC Digital Technology Solutions, public safety and planning teams to better understand road user trajectories and the capabilities of existing and new solutions to improve road user experience on campus. Furthermore, one of the research outcomes to test Vehicle-to-Everything (V2X) alerts is prioritized by the U.S. Department of Transportation (DOT) to improve safety nationwide. 

What are some of the most impactful experiences during your time at UC?

Working as an entrepreneurial lead for the NSF Innovation Corps award and as a project lead for ODOT's Student Transportation Advancement Research (STAR) have been my most impactful experiences at UC. 

While the NSF I-Corps program expanded my network into the transportation ecosystem and taught me about real-world challenges, the ODOT STAR project has provided me with an opportunity to develop and execute solutions to identified challenges. 

As part of these projects, I've attended several national and international conferences to interview folks who are in the middle of dealing with real-world problems. I attended the American Association for State Highway Transportation Officials annual meeting in 2022, as well as several ITS conferences. FInally, one of the highlights of my time at UC was my visit to the Netherlands for the 2023 TRAIL International PhD Autumn School on Cycling in Cities. 

I had the opportunity to develop and demonstrate Connected and Automated Vehicle Education lesson plans for K-12 STEM educators. This was through the support of my research team, Mary Welsh Schlueter at Partnership for Innovation in Education, FHWA Saxton Lab, and ODOT/DriveOhio technical advisory committee. 

What are a few of your accomplishments of which you are most proud?

Sai Bonthu placed third in UC's Elevator Pitch Competition. Photo/provided

Publishing my research work at the UCII is definitely at the top of my list. Beyond that, winning third prize in UC's one-minute elevator pitch competition validated my ability to translate society and market needs into a value proposition for a new product idea. Finally, participating in numerous tradeshows, international conferences and expos to network, interview and learn from experts in the industry has been the greatest accomplishment during my PhD program at UC. 

What are your plans after earning your degree?

I will graduate in August of 2024 with my PhD. My doctoral studies at UC and the experience through the ODOT STAR research project have dramatically changed my perspective towards sustainable and smart transportation. I'm going to continue exploring pathways toward sustainable and active transportation through a technology lens. I strongly believe automation is possible at any level in any application, but autonomous vehicles (e.g., self-driving robot taxis) are not ready to completely blend into our traffic. Meanwhile, connected vehicles are here to stay. In the near future, all vehicles will communicate with one another and the infrastructure in real-time, causing trans-disciplinary impacts on e-commerce, supply chain, daily deliverance systems, decarbonization, circular economy, uncrewed vehicles (ground and aerial) and sustainable multi-modal mobility. I'm hopeful that I'll be uniquely skilled and positioned to address important issues in such an evolving and complex ecosystem. 

Do you have any other hobbies or experiences you'd like to share?

Like many other South Asians, I love cricket. I've played in the Midwest Cricket Tournament since I moved to the U.S. in 2013. At UC, I currently serve as the president of the UC Cricket Club and represented the club as captain and wicket-keeper/batter between 2021-22. We've made it to the finals of MCT Division II 35 over tournament in 2021, and that is one of the most memorable experiences for me during my time at UC. Beyond that, I've helped out Tri-State Trails, a nonprofit to connect people and places with a regional trail and bikeway network, as a trail monitoring intern in the summer of 2023, and it was a great experience to learn about the active transportation network in the Cincinnati/Northern Kentucky/Indiana area. 

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UMD’s Computer Science Graduate Program Holds Steady as Top 10 Public

The University of Maryland’s  computer science  graduate program ranks No. 10 among the country’s public institutions in the 2024 edition of  U.S. News & World Report ’s “Best Graduate Schools.” The program ranks 17th overall, the same as last year.

UMD also ranks high in three computer science specialties:

• Artificial intelligence at No. 15 (No. 9 among publics)
• Theory at No. 22 (No. 10 among publics)
• Programming language at No. 23 (No. 13 among publics)

The Department of Computer Science has 450 graduate students in its computer science M.S. and Ph.D. programs and nearly 150 graduate students in its data science and machine learning master’s programs run by the college’s Science Academy.

Because U.S. News doesn’t rank all graduate programs annually, other current honors for the College of Computer, Mathematical, and Natural Sciences include:

• Atomic/molecular/optical specialty at No. 6
• Quantum specialty at No. 9
• Condensed matter specialty at No. 17
• Applied math specialty at No. 15
• Analysis specialty at No. 24
• Earth Sciences at No. 27 (No. 16 among public institutions)
• Chemistry at No. 46 (No. 27 among public institutions)
• Biological Sciences at No. 68 

The rankings are based on reputational surveys and widely used by prospective students when considering where to apply.

About the College of Computer, Mathematical, and Natural Sciences

The College of Computer, Mathematical, and Natural Sciences at the University of Maryland educates more than 8,000 future scientific leaders in its undergraduate and graduate programs each year. The college's 10 departments and six interdisciplinary research centers foster scientific discovery with annual sponsored research funding exceeding $250 million.

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Abby robinson, related news.