Find Academic Supervisors

Artur CzumajView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Randomized Algorithms
• +9 more

Artur Czumaj is a Professor in the Department of Computer Science at the University of Warwick and serves as the Director of the Centre for Discrete Mathematics and its Applications (DIMAP). He is a member of the Division of Theory and Foundations (FoCS) and holds affiliations with the Alan Turing Institute, the Warwick Data Science Institute (WDSI), and the Warwick Centre for Doctoral Training in Mathematics of Real-world Systems (MathSys). Previously, he served as Head of Department and President of the European Association for Theoretical Computer Science (EATCS) from 2020 to 2024. His research lies at the core of theoretical computer science, focusing on the design and analysis of algorithms, particularly randomized, sublinear, parallel, and distributed algorithms. His work spans graph theory, combinatorics, computational geometry, algorithmic game theory, and property testing. He has led major research initiatives funded by EPSRC, IBM, the Royal Society, and Weizmann-UK grants. The trends in his recent publications highlight a strong emphasis on sublinear algorithms, dynamic graph algorithms, and property testing, with recurring themes in randomized methods, graph processing, and efficient data structures. His work bridges foundational theory with applications in data summarization, network analysis, and computational geometry. EPSRC grants: EP/D063191/1, EP/G064679/1, EP/G069034/1, EP/J021814/1, EP/N011163/1, EP/V01305X/1, EPSRC studentship IBM Faculty Award Royal Society International Exchanges Scheme Weizmann-UK Making Connections Grants on combinatorial and algorithmic primitives and the interplay between algorithms and randomness Peter Davies received the 2020 Warwick Faculty of Science Thesis Prize under his supervision Artur Czumaj has supervised numerous PhD students, including Anna Adamaszek, Michal Adamaszek, Sam Coy, Peter Davies, Michail Fasoulakis, Jan Hladky, Wang Xin, and Hairong Zhao. His research has been supported by sustained grant funding, reflecting his leadership in theoretical computer science. He has organized major workshops at Dagstuhl, Oberwolfach, Simons Institute, and the University of Warwick, and has served on the steering committees of HALG and as PC Chair for SODA 2018 and ICALP 2020. He is actively involved in organizing key research events, including the Simons Institute Special Semester on Sublinear Algorithms (2024), the Workshop on Sublinear Graph Simplification (2024), and the Computational Complexity Conference (CCC 2023) at Warwick. He also co-organizes the Warwick-Weizmann workshops and the IGAFIT Algorithmic Postdocs Workshop, fostering international collaboration in algorithms research.

Cameron MuscoView profile

Assistant Professor

• Theoretical Computer Science
• Machine Learning
• Numerical Linear Algebra
• +3 more

Cameron Musco is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. He is affiliated with the Theory Group and conducts research at the intersection of theoretical computer science, numerical linear algebra, and machine learning. His work focuses on randomized algorithms, streaming, and distributed computation, with applications in data science. Education: PhD in Computer Science, MIT (2019) BS in Computer Science and Applied Mathematics, Yale University Research Interests: Musco's research emphasizes algorithm design for large-scale data analysis, including fast randomized methods for linear algebraic problems. He explores topics such as low-memory computation, matrix approximations, and graph algorithms, driven by applications in machine learning and distributed systems. Publications: His recent work spans advancements in hierarchical matrix approximation, graph-based nearest neighbor search, and fair resource allocation, reflecting expertise in both theoretical foundations and practical algorithmic innovation. Awards: He has received an NSF Career Award, Google Research Scholar Award, and recognition for anti-racism leadership. He actively reviews for top conferences in theoretical computer science and machine learning. Advising & Grants: Musco advises multiple PhD students and has grants from NSF and Google. His lab collaborates on projects like low-rank matrix approximation and causal discovery. Labs/Teams: He is part of the Theoretical Computer Science Group and the Center for Data Science at UMass.

• Cybersecurity
• Cryptography
• Programming Languages
• +4 more

Elaine Shi is a Professor with a joint appointment in the Computer Science Department (CSD) and Electrical and Computer Engineering (ECE) at Carnegie Mellon University. She is also an Adjunct Professor of Computer Science at the University of Maryland. Her research focuses on cryptography, security, mechanism design, algorithms, blockchains, and programming languages. She co-founded Oblivious Labs, Inc., and her work on Oblivious RAM and differential privacy has been adopted by Signal, Meta, and Google. Education: Not explicitly listed, but her academic roles imply advanced degrees in computer science. Research Interests: Shi's work spans foundational areas such as secure computation, privacy-preserving algorithms, and blockchain protocols. She emphasizes practical implementations, such as Oblivious RAM and cryptographic compilers like Viaduct. Her research also explores game-theoretic mechanisms for decentralized systems and explores the intersection of theory and practice in secure distributed systems. Publications: Over 150+ articles in top venues like Eurocrypt, S&P, and CCS, covering topics like oblivious algorithms, blockchain security, and differential privacy. Recent work includes optimizing obfuscation, secure aggregation, and transaction fee mechanisms. Packard Fellow, Sloan Fellow, ACM Fellow, IACR Fellow Co-founder of CMU's Crypto Group and Crypto Seminar Series Advising & Grants: Supervises a large group of students and postdocs, with notable alumni holding academic and industry roles. Active in securing grants for research in secure computation and blockchain technologies. Labs & Teams: Leads research in Oblivious Labs and collaborates on projects like the Viaduct compiler and secure enclaves for privacy-preserving computation.

• Algorithm Theory
• Algorithms and Data Science
• Economics and Computation
• +6 more

Stefano Leonardi is a Full Professor in the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza Università di Roma. His research focuses on Algorithm Theory, Algorithms and Data Science, and Economics and Computation. He leads the ERC Advanced Grant project AMDROMA, exploring algorithmic mechanisms for online markets. He has held roles as Conference Chair for STOC 2021, WWW 2015, and FUN 2018, and coordinates the Sapienza School of Advanced Studies (2016-2018). His work spans approximation algorithms, online algorithms, and mechanism design. Awards include the ERC Advanced Grant and EATCS Fellowship. His research interests emphasize foundational algorithmic problems in web-based markets, leveraging rigorous design and large-scale data analysis. Recent projects include ALGADIMAR (PRIN 2019-2022) for digital market algorithms. He chairs the Highlights of Algorithms conference series and serves on program committees for top venues like EC, ICALP, and SODA. His lab focuses on web algorithmics and data mining, addressing challenges in online labor markets and fair division. Leonardi's academic contributions include over 100 publications, with recent work on fair algorithms, prophet inequalities, and mechanism design in auctions. He has pioneered methods for submodular optimization, online learning, and multi-agent systems. Grants and awards reflect his leadership in bridging theory with real-world applications, particularly in digital economies. Grants: ERC Advanced Grant (2018-2023), PRIN ALGADIMAR (2019-2022) Leadership: Chair of ACM STOC 2021, WWW 2015, and 9th FUN Conference Labs/Teams: Laboratory on Web Algorithmics and Data Mining Key Projects: AMDROMA (algorithmic mechanisms), ALGADIMAR (digital markets)

Nai-Hui ChiaView profile

Assistant Professor

• Quantum Algorithms
• Quantum Complexity Theory
• Quantum Cryptography
• +3 more

Nai-Hui Chia is an Assistant Professor in the Department of Computer Science at Rice University. Previously, he held positions at Indiana University Bloomington (2021-2022), the University of Maryland's Joint Center for Quantum Information and Computer Science (2020-2021), and the University of Texas at Austin (2018-2020). He earned his Ph.D. in Computer Science and Engineering from Penn State University (2018) and a Bachelor's degree from National Taiwan University (2010). His research focuses on quantum algorithms, quantum complexity theory, and quantum cryptography. Key questions include exploring quantum advantages, computational limits, and the impact of quantum computing on computer science. He has contributed to foundational work on quantum circuit complexity, verification, and cryptographic protocols resilient to quantum attacks. Dr. Chia has received prestigious awards, including the NSF CAREER Award (2024), DOE Quantum Testbed Pathfinder Award (2023), and Google Scholar Award (2023). He actively participates in program committees for major conferences like TQC, Asiacrypt, and Crypto. Advising includes students like Yu-Ching Shen and Chia-Ying Lin, along with postdoctoral researchers. His work bridges theoretical computer science and quantum computing, with grants supporting research into quantum circuit size problems and post-quantum cryptography.

• Cryptography
• Computer Security
• Coding Theory
• +2 more

Elette Boyle is an Associate Professor at the Efi Arazi School of Computer Science, Reichman University (IDC Herzliya), Israel. She serves as the Director of the FACT Research Center and is a Senior Scientist at NTT Research. Additionally, she heads the RRIS International Program. Her academic career spans prestigious institutions including MIT, Technion, and Cornell. Dr. Boyle received her Ph.D. in Mathematics from MIT under the guidance of Shafi Goldwasser and Yael Tauman Kalai. Following her doctorate, she completed postdoctoral research at the Technion Israel Institute of Technology (2013-2015) hosted by Yuval Ishai, and a short-term postdoc at Cornell University (Summer 2013) hosted by Rafael Pass. She completed her undergraduate studies in mathematics at Caltech. Her research focuses on the theoretical foundations of computer security and cryptography, with particular expertise in secure multiparty computation, function secret sharing, distributed point functions, and memory checking protocols. Her work bridges theoretical computer science with practical cryptographic applications, developing protocols that balance security guarantees with computational efficiency. Dr. Boyle's research has significantly advanced the field of cryptography, particularly in understanding the fundamental limits and possibilities of secure computation protocols. Analysis of her recent publications reveals a strong focus on the theoretical foundations of secure computation, with particular emphasis on understanding computational and communication complexity limits. Her work spans multiple dimensions of cryptography including foundational protocols, complexity analysis, and practical implementations. A recurring theme in her research is developing efficient cryptographic primitives that minimize communication overhead while maintaining strong security guarantees. Her contributions to function secret sharing, distributed point functions, and pseudorandom correlation generators have been particularly influential in the field. Dr. Boyle has advised several graduate students including Pierre Meyer (Ph.D., co-advised with Geoffroy Couteau), Matan Hamilis (Ph.D.), and D'or Banon (MSc., co-advised with Ran Cohen). Her research has been supported by various grants enabling her to lead significant projects in cryptography and secure computation. As Director of the FACT Research Center, she leads a team focused on foundational aspects of computer science and cryptography. Her center collaborates with researchers worldwide and serves as a hub for advancing cryptographic research in Israel and internationally.

• Sublinear-time algorithms
• Error-correcting codes
• Computational complexity
• +3 more

Elena Grigorescu is a Professor at the University of Waterloo, Department of Computer Science. She holds a Ph.D. from the Massachusetts Institute of Technology (2010), an M.S. from MIT (2006), and a B.A. from Bard College (2004). Her research focuses on sublinear-time algorithms, error-correcting codes, computational complexity, and learning theory. She explores foundational aspects of algorithms with constraints on time/space, privacy-preserving computation, and applications in graph theory and optimization. Her work includes advancements in spanner algorithms for network design, differential privacy in sublinear-time settings, and learning-augmented approaches for online optimization. Recent publications address trace reconstruction, privacy-utility trade-offs, and combinatorial optimization techniques. Grigorescu is actively involved in conferences like APPROX/RANDOM and IEEE Foundations of Computer Science, contributing to algorithmic theory and practical implementations. Her research emphasizes theoretical rigor while addressing real-world challenges in data analysis and distributed systems. No awards or formal advisees are explicitly listed in the provided information.

Sofya RaskhodnikovaView profile

Professor

• Sublinear-time algorithms
• Data Privacy
• Approximation Algorithms
• +3 more

Sofya Raskhodnikova is a Professor in the Department of Computer Science at Boston University, part of the College of Arts and Sciences. She holds a Ph.D. from MIT and has held positions at Penn State University and postdoctoral fellowships at the Hebrew University of Jerusalem and the Weizmann Institute of Science. Her research focuses on sublinear-time algorithms, data privacy, approximation algorithms, and complexity theory. She is a recipient of the NSF CAREER Award and has contributed significantly to the theoretical foundations of privacy-preserving computation and algorithm design. Education: Ph.D. in Computer Science from MIT (2003), postdoctoral research at Hebrew University of Jerusalem and Weizmann Institute of Science (2003–2006). Visiting positions at UCLA, Harvard University, and the Simons Institute for the Theory of Computing. Research Interests: Sofya’s work bridges theoretical computer science and practical applications, emphasizing algorithms that operate efficiently on large datasets. Key areas include property testing (e.g., monotonicity, sortedness), differential privacy, and sublinear-time algorithms. She explores how algorithms can analyze data while preserving privacy guarantees and minimizing computational resources. Publications: Over 50 peer-reviewed articles in top venues such as STOC, FOCS, and SODA, with recent contributions focusing on dynamic graph algorithms under privacy constraints and robust property testing against adversarial noise. Professional Activities: Editor for ACM Transactions on Computation Theory and Algorithmica ; program committee chair for WOLA 2021 and CSR 2022; active in mentoring initiatives like Sigma Camp and Artemis. Advising & Students: Current advisees include Ephraim Linder and Debanuj Nayak. Notable alumni include Iden Kalemaj (Meta Research) and Nithin Varma (Max Planck Institute). She has supervised over 15 Ph.D. students and postdocs, fostering a collaborative research environment.

Elad HazanView profile

Professor

• Machine Learning
• Mathematical Optimization
• Control Theory
• +4 more

Elad Hazan is a Professor of Computer Science at Princeton University and co-founder/director of Google AI Princeton. His research focuses on algorithmic foundations of machine learning and optimization, with significant contributions to online learning, nonstochastic control, and adaptive gradient methods. Princeton University (Faculty) Google AI Princeton (Co-founder & Director) His work bridges mathematical optimization, control theory, and computational complexity. Key contributions include the AdaGrad algorithm, sublinear-time optimization methods, and spectral filtering techniques for sequence modeling. Recent research emphasizes efficient neural architectures and provable guarantees in online control. Scientific awards include the Bell Labs Prize, IBM Goldberg Best Paper Award (twice), Google Research Award (twice), European Research Council grant, Marie Curie fellowship, and ACM Fellowship. He has served as program chair for COLT 2015 and on the Association for Computational Learning steering committee. His publications highlight trends in online convex optimization, spectral methods for dynamical systems, and adaptive gradient algorithms. Collaborations span Princeton, Google Brain Research, and interdisciplinary projects in robotics and AI safety.

Nikhil SrivastavaView profile

Associate Professor

• theoretical computer science
• algebraic graph theory
• spectral graph theory
• +5 more

Nikhil Srivastava is an Associate Professor of Mathematics at the University of California, Berkeley, and a Senior Scientist at the Simons Institute. He received his PhD in Computer Science from Yale University in 2010 under the supervision of Daniel Spielman, following his undergraduate studies at Union College. After postdoctoral positions at the Institute for Advanced Study (Princeton), MSRI, and a research position at Microsoft Research India, he joined UC Berkeley in 2015. His research spans theoretical computer science and mathematics, with particular focus on spectral graph theory, random matrices, the geometry of polynomials, asymptotic convex geometry, and numerical analysis . His work often bridges the gap between pure mathematics and theoretical computer science, developing deep connections between polynomial methods, linear algebra, and combinatorial structures. Srivastava's research has been recognized with several prestigious awards including the SIAM Polya Prize , the NAS Held Prize , and the AMS Foias Prize , highlighting his significant contributions to the field. His most recent work focuses on numerical linear algebra, spectral theory, and computational mathematics, with publications spanning from algorithmic foundations to applications in mathematical physics. He actively mentors graduate students, currently advising Rikhav Shah, Zack Stier, and Isabel Detherage. His past students include Jorge Garza Vargas (co-advised with Dan Voiculescu), Theo McKenzie, Jess Banks, Satyaki Mukherjee, Archit Kulkarni, Nick Ryder, and Aaron Schild (co-advised with Satish Rao). His research has been supported by the NSF and the Sloan Foundation. At Berkeley, Srivastava has been actively involved with the Simons Institute, participating in numerous programs including Complexity and Linear Algebra (Fall 2025), Sublinear Algorithms (Summer 2024), and several others dating back to 2013. He regularly teaches courses in mathematics, including Discrete Mathematics (Math 55) and Multivariable Calculus (Math 53), and runs a seminar on Discrete Analysis in Evans Hall.

• Theoretical Computer Science
• Distributed Algorithms
• Parallel Algorithms
• +3 more

Yi-Jun Chang is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS). He previously held a Junior Fellow position at the Institute for Theoretical Studies, ETH Zurich (2019–2021), and earned his Ph.D. in Computer Science and Engineering from the University of Michigan (2019). His research focuses on theoretical computer science, particularly distributed, parallel, and sublinear graph algorithms. Ph.D., University of Michigan (2019) M.S., National Taiwan University (2015) B.S., National Taiwan University (2013) Chang’s research explores the complexity and optimization of algorithms in distributed systems, including leader election, graph shattering, expander decomposition, and subgraph detection. His work addresses fundamental challenges in time-energy trade-offs, communication efficiency, and deterministic vs. randomized approaches in models like LOCAL and CONGEST. Recent publications highlight advancements in distributed triangle enumeration, optimal coloring, shortest path computation, and certification in bounded pathwidth graphs. Awards include the PODC 2019 Best Paper and Best Student Paper Awards, followed by the 2020 PODC Doctoral Dissertation Award. PODC 2019 Best Paper Award PODC 2019 Best Student Paper Award 2020 PODC Doctoral Dissertation Award Chang teaches courses such as CS3230 (Design and Analysis of Algorithms) and CS5275 (The Algorithm Designer's Toolkit). He advises Ph.D. students Hung Thuan Nguyen and Haoran Zhou, and has collaborated with postdoctoral researchers including Gopinath Mishra and Dean Leitersdorf.

Philip BilleView profile

Professor

• Algorithms
• Data Structures
• String Processing
• +9 more

Philip Bille is a Professor and Head of the Algorithms, Logic and Graphs section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), College of Engineering. His research centers on the design and analysis of efficient algorithms, particularly for string processing, compressed data, and data structures. His research interests lie at the intersection of theoretical computer science and practical applications. He focuses on algorithms , data structures , string indexing , pattern matching , and compressed computation . His work enables efficient querying and processing of large-scale, repetitive data, with applications in bioinformatics, intrusion detection, and green computing. The recent publications reflect a strong trend in developing space-efficient and fast algorithms for modern computational challenges. Key themes include compressed data structures , sliding window indexing , finite automata compression , and energy-aware matrix operations . These works demonstrate expertise in balancing theoretical rigor with practical performance. Philip Bille actively supervises multiple PhD students and leads several research projects. He contributes to advancing sustainable computing aligned with UN SDGs. His work integrates algorithmic theory with real-world efficiency. Supervises PhD projects on hierarchical compression, adaptive computation, and vector processor algorithms. Involved in research on green computing, compressed formats, and efficient data models. He is affiliated with the Algorithms, Logic and Graphs group at DTU, a hub for theoretical and applied algorithmic research. The team explores fundamental problems in data representation and processing, pushing the boundaries of what is computationally feasible in terms of time and space.

CHEN YuView profile

Assistant Professor

• Theoretical Computer Science
• Graph Algorithms
• Sublinear Algorithms
• +3 more

CHEN Yu serves as a NUS Presidential Young Professor (Assistant Professor) in the Department of Computer Science, School of Computing, National University of Singapore. Prior to joining NUS, he was a postdoctoral researcher with the theory group at EPFL. His academic credentials include: Ph.D., University of Pennsylvania, 2022 B.E., Shanghai Jiao Tong University, 2016 Dr. Chen's research spans theoretical computer science and mathematics, with a primary focus on graph algorithms. He has particular expertise in sublinear algorithms and graph sparsification, contributing to the fields of combinatorial algorithms and graph theory. His work addresses foundational challenges in algorithm design through rigorous mathematical approaches. His notable scientific awards are: SODA 2019 Best Paper Award The Morris and Dorothy Rubinoff Award at the University of Pennsylvania He teaches courses such as CS3230 Design and Analysis of Algorithms and CS5234 Algorithms at Scale, demonstrating commitment to both undergraduate and graduate education while maintaining active research leadership in theoretical computer science.

• Cryptography
• Privacy
• Secure Multiparty Computation (MPC)
• +5 more

Gabe Kaptchuk is an Assistant Professor in the Computer Science Department at the University of Maryland, College Park (UMD), focusing on cryptography, privacy, and their social implications. He is affiliated with UMD's MC² (Maryland Cybersecurity Center) and UMIACS (Institute for Advanced Computer Studies). Previously, he was research faculty at Boston University and earned his PhD from Johns Hopkins University under advisors Avi Rubin and Matt Green. Research Interests: Kaptchuk's work bridges theoretical cryptography and social applications, emphasizing secure multiparty computation (MPC), zero-knowledge proofs, and steganography. He also explores the intersection of cryptography with law, policy, and human-centered design. Notable projects include Meteor (secure steganography using generative models) and Pulsar (MPC for dynamic participants). Teaching: Courses include Governing Algorithms (UMD) and Law and Algorithms (BU), focusing on algorithmic governance, privacy, and legal implications of technology. He has taught computer security and networks at Johns Hopkins and BU. Grants & Funding: Recent NSF awards include Collaborative Research: ReDDDoT Phase 2 (2024) for participatory privacy protections in AI training data. Active in deploying MPC for social good, such as analyses for Boston Women’s Workforce Council. Labs/Teams: Collaborates with researchers at Georgetown, Columbia, and institutions like the Wikimedia Foundation. Co-authors include Rachel Cummings, Elissa Redmiles, and Matthew Green on privacy and usability topics.

Sandy IraniView profile

Professor

• Quantum Computing
• Algorithm Design
• Computational Complexity Theory
• +5 more

Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.