Find Academic Supervisors

Ronitt RubinfeldView profile

Professor

• Theory of Computation
• Algorithms
• Randomized Algorithms
• +3 more

Ronitt Rubinfeld is the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). She holds a PhD from UC Berkeley (1991) and a BSE in Electrical and Computer Engineering from the University of Michigan. Prior to MIT, she was at Cornell University and NEC Research Laboratories. Her research focuses on randomized and sublinear-time algorithms, exploring how to understand data with minimal sampling. Notable roles include ONR Young Investigator, Sloan Fellow, and Radcliffe Fellow. She advises numerous PhD and master’s students, contributing to fields like algorithmic theory and data science. Education: PhD UC Berkeley (1991), BSE University of Michigan. Affiliations: MIT CSAIL, Theory of Computation Group, Algorithms Group. Research Interests: Sublinear algorithms, randomized algorithms, distribution testing, and complexity theory. Her work bridges theoretical foundations with practical applications in big data analysis. Awards: ONR Young Investigator, Sloan Research Fellowship, Cornell Teaching Award, Radcliffe Fellowship. Advising: Supervised 20+ PhD students (e.g., Funda Ergun, Badih Ghazi) and 10+ master’s students. Current advisees include Amartya Biswas and Ted Pyne. Grants include NSF funding for distribution testing and sublinear algorithms. Labs/Teams: Core member of MIT Theory of Computation group, CSAIL Algorithms Group, and collaborator on projects like WOLA (Workshop on Sublinear Algorithms).

Moses CharikarView profile

Professor

• Algorithms
• Machine Learning
• Approximation Algorithms
• +4 more

Moses Charikar is the Donald E. Knuth Professor of Computer Science at Stanford University, with a courtesy appointment in Mathematics. He earned his B.Tech. from the Indian Institute of Technology Bombay (1995) and a Ph.D. from Stanford University (2000). His research focuses on high-dimensional data processing, machine learning optimization, approximation algorithms, and metric embeddings. Charikar has held faculty positions at Princeton University (2001–2015) and spent time at Google Research. Research interests include algorithmic techniques for massive data sets, convex optimization for non-convex problems, and low-distortion metric embeddings. His work has led to influential contributions like locality-sensitive hashing (LSH), earning the Paris Kanellakis Award (2012) and ACM Fellowship (2021). He has also received the Simons Investigator award (2014) and multiple best-paper accolades at top conferences. Awards: Donald E. Knuth Professorship (Stanford) 2012 Paris Kanellakis Theory and Practice Award ACM Fellow (2021) Simons Investigator in Theoretical Computer Science (2014) Charikar’s research bridges theoretical computer science and practical applications, with notable contributions to clustering, approximation algorithms, and data analysis. His publications span algorithm design, computational geometry, and machine learning theory.

Mikkel ThorupView profile

Professor

• Algorithms
• Data Structures
• Hash Functions
• +3 more

Mikkel Thorup is a Professor at the Department of Computer Science, University of Copenhagen, and a VILLUM Investigator heading the Basic Algorithms Research Copenhagen (BARC) Center supported by a €5.3 million grant from the VILLUM Foundation. Born in 1965, he holds a D.Phil. from Oxford University (1993). Prior roles include positions at the University of Copenhagen (1993–1998) and AT&T Labs-Research (1998–2013). His research focuses on algorithms, data structures, and hashing, with a particular emphasis on unifying theoretical and practical aspects. Awards include the ACM Fellowship, Villum Kann Rasmussen Award, and Fulkerson Prize. Education: D.Phil. in Computer Science, Oxford University (1993). Earlier positions: University of Copenhagen (1993–1998), AT&T Labs-Research (1998–2013). Research interests encompass algorithm design, data structure optimization, and hashing techniques. His work bridges theory and practice, addressing problems like graph clustering, optimization, and efficient sampling. He is known for contributions to correlation clustering, hash function analysis, and dynamic graph algorithms. Awards include the 2011 MAA Robbins Award, 2015 Villum Kann Rasmussen Award (Denmark’s top research prize), and 2021 AMS-MOS Fulkerson Prize. His Erdős number is 2, and he boasts an H-index above 60 with over 200 publications in top venues like STOC, FOCS, and SODA. Advising and grants: Leads BARC since 2017, funded by VILLUM Foundation. His research has influenced both academia and industry, particularly in telecommunications and algorithmic efficiency. Collaborates internationally, with a focus on foundational algorithmic challenges. Labs/Teams: Founder and head of BARC, fostering research in algorithms and their applications. Engages in interdisciplinary work combining theory with practical implementations.

Nima AnariView profile

Assistant Professor

• Sampling Algorithms
• Markov Chains
• Parallel Algorithms
• +2 more

Nima Anari is an Assistant Professor of Computer Science at Stanford University and member of the Theory Group. He holds a Ph.D. in Computer Science from UC Berkeley (2015) and dual B.Sc. degrees in Computer Engineering and Mathematics from Sharif University of Technology (2010). His research focuses on theoretical computer science with emphasis on sampling algorithms, Markov chains, and combinatorial optimization. Research explores parallel sampling methods, log-concave polynomials, high-dimensional expanders, and applications in machine learning. Recent publications develop efficient algorithms for determinantal point processes, diffusion models, and optimization on discrete domains. Honors include the Michael and Sheila Held Prize (2025), Frontiers of Science Award (2023), Sloan Research Fellowship (2021), NSF CAREER Award (2021), and Best Paper Award at STOC 2019. He mentors 13 graduate and undergraduate students in theoretical computer science.

• Graph Algorithms
• Theoretical Computer Science
• Approximation Algorithms
• +2 more

Tianyi Zhang is a Researcher at the Professorship for Theoretical Computer Science, ETH Zurich, located at OAT Z 29, Andreasstrasse 5. His research focuses on advancing fundamental algorithms in graph theory, with particular expertise in dynamic graph problems, efficient spanner constructions, edge coloring optimizations, and shortest-path computations. Dr. Zhang develops both theoretical frameworks and practical implementations for complex computational challenges. His core research areas include the design of near-linear and subquadratic time algorithms for graph optimization problems, fault-tolerant network structures, streaming-optimized graph coloring, and geometric graph embeddings. Recent work emphasizes breakthroughs in Vizing's theorem implementations, dynamic set cover deamortization, and space-efficient distance oracles. Dr. Zhang's publications demonstrate consistent innovation in algorithm efficiency for planar graphs, Euclidean spaces, and dynamic network settings. His 2023-2025 articles reveal concentrated efforts on: 1) Optimizing edge coloring through multi-step Vizing chains and streaming adaptations, 2) Enhancing spanner constructions for doubling metrics and planar environments, and 3) Developing failure-resistant path algorithms with improved time/space complexity. These contributions address scalability challenges in large-scale network processing. He collaborates within the Theoretical Computer Science research group at ETH Zurich, contributing to the institution's leadership in algorithmic innovation. No information about awarded grants, supervised students, or educational background is available in the source materials.

Qin ZhangView profile

Professor

• Algorithms for Big Data
• Theoretical Foundations of Machine Learning
• Quantum Information and Computing
• +4 more

Roles and Affiliations: Professor of Computer Science at Indiana University Bloomington, Adjunct Professor in Mathematics. Previously at IBM Almaden Research Center and Aarhus University's Center for Massive Data Algorithmics. PhD from HKUST in Computer Science and Engineering. Education: PhD in Computer Science, HKUST, advised by Mordecai Golin and Ke Yi Research Interests: Focuses on algorithms for big data, including communication-efficient distributed computation, streaming/sketching algorithms, and quantum data management. Explores theoretical foundations of machine learning, particularly distributed learning. Leads projects funded by NSF grants on parallel reinforcement learning, distributed graph algorithms, and noisy data processing. Publications and Awards: Over 100 papers in top venues like FOCS, SIGMOD, and NeurIPS. Won the Best Paper Award at SPAA 2017 for distributed clustering work. Invited to special journal issues for top conferences. Teaching and Grants: Teaches advanced courses on sublinear algorithms and algorithm design. PI on NSF grants totaling over $2M, including collaborative projects on distributed computing and epidemiological modeling. Advises PhD students in areas like quantum algorithms and reinforcement learning. Service: Served on program committees for SIGMOD, NeurIPS, ICML, and PODS. Organizes theory seminars and Midwest Theory Day workshops.

Loek CleophasView profile

Assistant Professor

• Model-Driven Software Engineering (MDSE)
• Algorithm Engineering
• Pattern Matching
• +3 more

Loek Cleophas is an Assistant Professor in the Engineering of Software-Intensive Systems group at Eindhoven University of Technology (TU/e). He holds a joint appointment with the Mathematics and Computer Science department and is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute). His work bridges model-driven software engineering (MDSE) and algorithm engineering, focusing on metamodel analysis, parallel pattern matching, and high-tech system simulations. Cleophas earned his MSc and PhD in Computer Science from TU/e, with industry experience at ASML and academic collaborations at institutions like University of Pretoria and Umeå University. Since 2017, he has led the Dutch research school IPA as Managing Director. He also serves as a research fellow at Stellenbosch University, supervising postgraduate students and co-developing joint research initiatives. His research emphasizes correctness, efficiency, and elegance in software systems. Notable contributions include the SAMOS framework for metamodel clone detection, VPDSL for material flow simulations, and foundational work on sublinear pattern matching algorithms.

Sourav ChakrabortyView profile

Professor

• Theoretical Computer Science
• Boolean Functions
• Quantum Complexity
• +4 more

Sourav Chakraborty is a Professor in the Advanced Computing and Microelectronics Unit (ACMU) of the Computer and Communication Sciences Division at the Indian Statistical Institute (ISI), Kolkata, India. He joined ISI in July 2018 after serving as faculty at Chennai Mathematical Institute from 2010-2018. Previously, he held postdoctoral positions at Centrum Wiskunde & Informatica (CWI) in Amsterdam and Technion in Israel. Education: Ph.D. in Computer Science, University of Chicago (2008) M.S. in Computer Science, University of Chicago (2005) B.Sc. in Mathematics, Chennai Mathematical Institute (2003) Research Focus: Professor Chakraborty specializes in Theoretical Computer Science with emphasis on classical and quantum complexity of Boolean functions, including sensitivity analysis, property testing, and quantum database search. His work extends to graph algorithms, electronic commerce mechanisms, and coding theory. His research explores fundamental questions in computational complexity through innovative mathematical frameworks. Publication Trends: Recent work demonstrates a strong focus on property testing, sampling algorithms, and complexity theory, with significant contributions to streaming algorithms, Boolean function analysis, and quantum query complexity. His publications frequently appear in top theoretical computer science venues and exhibit consistent innovation in algorithm design and complexity boundaries. Awards & Honors: Praise from Donald E. Knuth for streaming algorithms research Inclusion in Oded Goldreich's 'my choices' list for Conditional Sampling and Huge-Object Model work Chakraborty's function named in his honor for Sensitivity Conjecture contributions Academic Service: Teaches courses in discrete mathematics and theoretical computer science, with detailed course materials available through institutional pages. Organized workshops including the 2020 Workshop on Sensitivity and Query Complexity at ISI.

Omri Ben-EliezerView profile

Senior Lecturer

• Sublinear-Time Algorithms
• Algorithms and Society
• Privacy in Machine Learning
• +3 more

Omri Ben-Eliezer is a Senior Lecturer (Assistant Professor) and Taub Fellow at the Faculty of Computer Science in the Technion — Israel Institute of Technology. He holds a PhD from Tel Aviv University (2020, supervised by Noga Alon), followed by postdoctoral positions at the Weizmann Institute (hosted by Moni Naor), Harvard University (mentored by Madhu Sudan), and a research fellowship at the Simons Institute. He also served as an instructor in applied mathematics at MIT. His research focuses on the theoretical and algorithmic foundations of big data, including sublinear-time algorithms, privacy in machine learning, beyond worst-case analysis, and complex networks. He has contributed to topics such as adversarial robustness, approximation algorithms, and knowledge representation. Ben-Eliezer's work spans algorithm design and analysis, with notable awards including the PODS 2020 Best Paper Award and the 2021 ACM SIGMOD Research Highlight Award. His publications appear in top venues like FOCS, SODA, and ITCS, and he frequently serves on program committees for conferences such as FOCS and PODS. He actively mentors students interested in algorithmic research bridging theory and practice and is affiliated with multiple academic institutions and collaborative projects.

Manolis ZampetakisView profile

Assistant Professor

• Theoretical Machine Learning
• Statistics
• Optimization
• +4 more

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley working with Michael Jordan. He earned his Ph.D. and M.S. from the EECS Department at MIT, where he was advised by Constantinos Daskalakis. His undergraduate education was completed at the National Technical University of Athens (NTUA). His research interests span Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, Mechanism Design, and Sublinear Algorithms. His work bridges theoretical computer science with practical applications in machine learning and economics. His research consistently appears in top-tier conferences such as STOC, FOCS, COLT, NeurIPS, and EC, with a strong focus on the theoretical foundations of machine learning and game theory. Zampetakis has received numerous prestigious awards including the ACM SIGEcom Doctoral Dissertation Award and the Google Ph.D. Fellowship. His publications from 2023-2025 demonstrate a consistent output of high-quality research across multiple domains, with a notable emphasis on bridging statistical theory with algorithmic approaches. His work shows increasing integration of robust statistics with mechanism design and game-theoretic approaches. ACM SIGEcom Doctoral Dissertation Award Google Ph.D. Fellowship Special Issue at SAGT 2015 Special Issue at WINE 2013 Second Prize in IMC 2012 Top 5 in Panhellenic Physics Competition 2008 Zampetakis actively advises multiple graduate students including Anay Mehrotra, Jane Lee, Vikram Kher, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis. He has served on program committees and organized workshops including the Workshop on Algorithms for Learning and Economics (WALE 2019, WALE 2022). His research has been supported by grants from Google and likely other major funding agencies given his publication record in top venues. His research group focuses on theoretical aspects of machine learning with connections to statistics and game theory. He collaborates extensively with researchers across institutions including MIT, UC Berkeley, and Yale colleagues.

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +4 more

Atri Rudra is the Katherine Johnson Chair in Artificial Intelligence and Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. He received his B.Tech from IIT Kharagpur in 2000 and his PhD from the University of Washington in 2007. Education: PhD, Computer Science and Engineering, University of Washington, 2007 MS, Computer Science and Engineering, University of Washington, 2005 B Tech, Computer Science and Engineering, Indian Institute of Technology, India, 2000 Rudra's research spans Theoretical Computer Science with specific interests in structured linear algebra (with applications in machine learning), database algorithms, theory of error-correcting codes, and algorithms and society. He is a member of both the Algorithms and Theory group and the Computing for Social Good group at UB. His work bridges fundamental theoretical questions with practical societal implications, particularly in how computing affects society. His publications reflect a strong focus on coding theory, as evidenced by his ongoing work on the book Essential Coding Theory with Venkatesan Guruswami and Madhu Sudan. His recent work shows an increasing interest in the intersection of machine learning and society, with courses like ML and Society reflecting this evolving research direction. His publications span theoretical foundations of coding theory to practical applications in database algorithms and societal implications of computing. Awards and Recognition: SUNY Chancellor's Award for Excellence in Teaching (2022) NSF CAREER Award (2009) Best Paper awards at PODS (2012) and ESA (2010) William Chan Memorial Dissertation Award (2007) IBM Faculty Award (2013) Rudra has advised numerous PhD students who have gone on to careers in academia and industry. His current research is supported by NSF grants IIS-1956149 and CCF-2247014. He has been actively involved in service to the community as an editor for IEEE Transactions on Information Theory and Theory of Computing Systems, and as program committee member for major conferences including PODS, SODA, and ITCS. He leads the EaGL Theory Computation Workshop series and co-organizes workshops on Multiple Approaches for Teaching Responsible Computing and Algorithmic Opportunities in the Modern LLM Revolution, reflecting his commitment to both theoretical foundations and societal impact of computing.

Hu DingView profile

Professor

• Algorithms
• Computational Geometry
• Machine Learning
• +4 more

Hu Ding is a pre-tenure Professor in the School of Computer Science and Engineering at the University of Science and Technology of China (USTC), where he directs the Data Intelligence, Algorithms, and Geometry (DIAG) research group. He previously held positions as a tenure-track Assistant Professor at Michigan State University (2016-2018) and a Simons-Berkeley Research Fellow jointly at Tsinghua University and UC Berkeley (2015-2016). Education: • Ph.D. in Computer Science, State University of New York at Buffalo (2015) • B.S. in Mathematics, Sun Yat-Sen University (2009) Research Interests: Hu Ding's research focuses on developing efficient algorithms for geometric optimization problems with applications in machine learning, big data, and biomedical imaging. His work bridges theoretical computer science (especially computational geometry) with practical challenges in distributed systems, outlier detection, and high-dimensional data analysis. Key areas include constrained clustering, truth discovery in crowdsourced data, and geometric methods for biomedical image analysis. Publication Trends: His recent publications demonstrate a strong focus on scalable algorithms for high-dimensional geometric optimization, particularly in distributed environments with noisy data. A consistent theme is developing theoretically-grounded solutions with practical efficiency, evidenced by work on sublinear-time algorithms, coreset constructions, and approximation frameworks for problems like k-center clustering and SVM optimization with outliers. Awards and Honors: Young Investigator Award, Ministry of Science and Technology (2021) Simons-Berkeley Research Fellowship (2015-2016) CCF Committee Member for Theoretical CS and Big Data (2021) Grants and Projects: USTC Innovation Group Grant: 'Toward Electronic Design Automation: Theories and Algorithms from AI' (2021) MOST Young Investigator Grant: 'Optimal Transportation in Medical Imaging' (3M RMB, 2021) Research Group: Leads the DIAG group with focus on geometric algorithms for data intelligence. Current team includes 6 PhD students and 15 Master's students working on problems in clustering, distributed optimization, and biomedical applications. Former students hold positions at Alibaba, ByteDance, and academic institutions.

Yann StrozeckiView profile

Associate Professor

• Enumeration Complexity
• Algorithmic Game Theory
• Simple Stochastic Games
• +7 more

Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.

Sourya RoyView profile

Assistant Professor

• Theoretical Computer Science
• Pseudorandomness
• Coding Theory
• +4 more

Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.

Yang LiuView profile

Assistant Professor

• Theoretical Computer Science
• Graph Algorithms
• Optimization
• +4 more

Yang P. Liu is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. Previously, he was a Postdoctoral Member at the Institute for Advanced Study and earned his PhD from Stanford University under the supervision of Aaron Sidford. His research focuses on theoretical computer science and mathematics, emphasizing graph algorithms, optimization, high-dimensional geometry, and additive combinatorics. He has received notable awards including the A.W. Tucker Prize and Google PhD Fellowship, alongside multiple best paper recognitions at major conferences like FOCS, STOC, and ITCS. Education: PhD in Computer Science from Stanford University (2023); BS from MIT (2018). Research Interests: Graph Algorithms Optimization (especially convex and high-dimensional) Algorithmic Techniques in Additive Combinatorics Geometric and Structural Aspects of Computation Teaching: Currently instructing CS 15-759: A Principled Approach to Optimization (Spring 2025), covering topics like gradient descent, interior-point methods, and sparsification techniques. Course emphasizes rigorous mathematical foundations. Awards: Recognized for contributions to optimization theory and algorithmic complexity. His work bridges discrete mathematics and continuous optimization paradigms.