Find Academic Supervisors

• Stochastic Modeling of Communication, Information and Service Systems
• Queueing Theory
• Stochastic Processes
• +9 more

Alexander Stolyar is Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign , where he has taught courses on stochastic processes, queueing systems, and service engineering. Before joining Illinois in January 2017, he was the Timothy J. Wilmott Endowed Chair Professor at Lehigh University (2014–2017) and spent nearly two decades in industrial research at Bell Laboratories , Motorola, and AT&T Labs-Research. Stolyar’s research centers on stochastic modeling of large-scale communication, information, and service systems , including cloud data centers, communication networks, and call/contact centers. He develops mathematical frameworks for dynamic resource allocation, scheduling, congestion control, and distributed algorithms , leveraging tools from queueing theory, stochastic processes, fluid and diffusion limits, large deviations, and mean-field analysis . His recent publications (2021–2025) reveal sustained focus on asymptotic optimality in complex systems: designing lightweight algorithms that become provably optimal as system scale grows. Key themes include greedy and randomized policies for inventory systems with stochastic lead times , parallel-server systems with redundancy and cancel-on-completion , particle systems with mean-field interactions exhibiting traveling-wave behavior , and packing-constrained service systems motivated by cloud data-center operations . Scientific honors include election as INFORMS Fellow (2021) , the INFORMS Applied Probability Society Best Publication Award (2004) , and the ACM SIGMETRICS Best Simulation Paper Award (1996) . He serves or has served on the editorial boards of Queueing Systems (Editor, 2008–present), IEEE Transactions on Information Theory (2019–2021), Operations Research (2006–2011), Advances in Applied Probability / Journal of Applied Probability (2008–2015), and as Technical Program Committee member for leading conferences such as SIGMETRICS and IFIP Performance . Stolyar’s research has generated more than 30 U.S. patents spanning wireless network scheduling, cloud resource allocation, dynamic load balancing, and packet-data network control—many of which have been incorporated into commercial cellular and cloud systems. His work is supported by NSF and industry grants, and he has advised numerous PhD students and postdocs who now hold faculty and research positions worldwide.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +10 more

Robert Krauthgamer holds The Harry Weinrebe Professorial Chair of Computer Science at The Weizmann Institute of Science, where he also serves as Department Head of the Department of Computer Science & Applied Mathematics within the Faculty of Mathematics and Computer Science. His distinguished career includes editorial leadership as Editor-in-Chief of SIAM Journal on Computing (2019-2025) and significant service on numerous program committees for major theoretical computer science conferences. Dr. Krauthgamer's primary research focuses on Analysis of Algorithms , with particular expertise in Data Analysis and Massive Data Sets, Combinatorial Optimization, Approximation Algorithms and Hardness of Approximation, Average-case Analysis and Heuristics, Embeddings of Finite Metrics, and Routing and Peer to Peer networks. His work also spans broader areas of Discrete Mathematics and High-Dimensional Geometry, reflecting his deep mathematical approach to algorithmic problems. His recent publications reveal a strong emphasis on sparsification techniques, high-dimensional computational geometry, streaming algorithms, and graph-theoretic problems. Throughout his career, Krauthgamer has supervised numerous graduate students at both MSc and PhD levels, with recent theses addressing cutting-edge topics in algorithm design and analysis. His service contributions include steering committee roles for major conferences like SODA, ESA, and HALG, as well as committee membership for the prestigious Gödel Prize (2019-2021). As an educator, he regularly teaches advanced courses including Randomized Algorithms and Sublinear Time and Space Algorithms, often in collaboration with other leading researchers. He also organizes Weizmann's TheoryLunch and has coordinated the Foundations of Computer Science seminar for many years, demonstrating his commitment to fostering academic community and intellectual exchange.

• Distributed Computing
• Communication Complexity
• Quantum Computing
• +4 more

Rotem Oshman is affiliated with Tel-Aviv University, Israel , where he contributes to research in theoretical computer science, focusing on distributed computing, communication complexity, and quantum algorithms. His work addresses fundamental challenges in network protocols, concurrent data structures, and cryptographic verification.

Alexandr AndoniView profile

Associate Professor

• sublinear algorithms
• high-dimensional computational geometry
• metric embeddings
• +2 more

Alexandr Andoni is an Associate Professor in the Department of Computer Science at Columbia Engineering , affiliated with the Data Science Institute (DSI). His research focuses on algorithmic foundations of massive data, including sublinear algorithms, high-dimensional computational geometry, metric embeddings, and machine learning. Education: Ph.D. in Computer Science from MIT (2009), supervised by Prof. Piotr Indyk Postdoctoral roles: Center for Computational Intractability (Princeton, 2009-2010), NYU, IAS Industry experience: Researcher at Microsoft Research Silicon Valley (2010-2014) until its closure Additional affiliations: Visiting scientist at Simons Institute (UC Berkeley), Foundations of Data Science Center member His work addresses theoretical challenges in efficient data processing, particularly for large-scale problems, with emphasis on nearest neighbor search and dimensionality reduction techniques.

Sathyawageeswar SubramanianView profile

Research Fellow

• Theoretical Computer Science
• Quantum Computing
• Quantum Information
• +5 more

Dr. Sathyawageeswar Subramanian is a Senior Research Fellow at the Department of Computer Science and Technology at the University of Cambridge and a College Research Associate at Sidney Sussex College. He holds a PhD in Applied Mathematics and Theoretical Physics from the University of Cambridge (2020), supervised by Richard Jozsa, and a Master of Advanced Studies (MASt) in Applied Mathematics and Theoretical Physics from Cambridge (2016). His research focuses on quantum algorithms, complexity theory, learning theory, and property testing, with contributions to quantum computing and theoretical physics. Career: 2023–Present: Senior Research Fellow, University of Cambridge 2022–2023: 1851 Research Fellow, University of Warwick 2020–2022: Postdoctoral Research Associate, University of Warwick 2016–2020: PhD Candidate, University of Cambridge Research Interests: Theoretical Computer Science, Quantum Computing, Quantum Information, Algorithms and Complexity Theory, Learning Theory, Machine Learning, Property Testing, and Theoretical Physics. Publications: His recent work includes groundbreaking contributions to quantum circuit complexity, information-theoretic generalization bounds for quantum learning, and entropy estimation. His research bridges quantum algorithms with classical complexity theory, machine learning, and foundational physics. Awards: 1851 Research Fellowship in Science and Engineering (2022–2025) Warwick Faculty of Science PostDoc Prize (2023) Cambridge-India Ramanujan Scholarship (2016–2020) Invited to Heidelberg Laureate Forum (2024) and Dagstuhl Seminar (2024) Teaching & Advising: Lectures on Quantum Computation for Part III students and supervises PhD candidates in quantum algorithms. Co-supervised Marcel Dall’Agnol and Hugo Aaronson at Warwick/Cambridge. Leadership & Service: Organized CCC 2023, contributed to EDI initiatives at Warwick, and served on grant review panels for DOE and EPSRC.

• Proof Complexity
• Computational Logic
• Parameterized Complexity
• +8 more

Dr. Stefan Dantchev is an Assistant Professor in the Department of Computer Science at Durham University. His work focuses on theoretical computer science, particularly proof complexity, computational logic, and parameterized complexity. He has contributed extensively to research on propositional proof systems like Resolution, Sherali-Adams, and Lovász-Schrijver, analyzing their limitations and capabilities through combinatorial principles and optimization techniques. His research also explores sublinear-time algorithms, simplicial complex entropy, and dynamic cellular automata models. His research interests are centered around proof systems and their computational boundaries, including topics such as binary encoding of combinatorial problems, complexity gaps in QBF Resolution, and the interplay between cutwidth parameters and optimization algorithms. He has investigated foundational questions in computational logic, such as relativization effects on contradiction proofs and the axiomatisation of PageRank in edge-weighted networks. Dr. Dantchev’s publications span conferences like LATIN, COCOON, and workshops such as STOC and FOCS, alongside journals in computational complexity and combinatorial optimization. His work demonstrates a persistent engagement with core theoretical challenges in computer science, particularly in proof systems and algorithmic efficiency.

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.