Find Academic Supervisors

Rafail OstrovskyView profile

Professor

• Algorithms
• Cryptography and Computer Security
• Theoretical Computer Science
• +3 more

Rafail Ostrovsky is a Professor of Computer Science and Mathematics at UCLA , affiliated with the Center for Information and Computation Security at the Henry Samueli School of Engineering and Applied Science. He earned his Ph.D. in Computer Science from MIT in 1992 under Silvio Micali. Research Focus: His work spans cryptography, algorithms, and theoretical computer science, emphasizing secure multi-party computation, zero-knowledge proofs, oblivious RAM, and high-dimensional data analysis. Applications include privacy-preserving data mining, systems security, and quantum cryptography. Article Trends: Recent publications address concurrent security protocols, robust secret sharing via expander graphs, and efficient multi-party computation. Topics intersect computational complexity, cryptographic reductions, and practical security implementations. Awards: Recipient of the 2018 RSA Conference Excellence in Mathematics Award , 2017 IEEE Fellow , and multiple IEEE/ACM honors. Holds 14 U.S. patents and over 290 refereed papers. Advising: Supervised 27 Ph.D. students, many now professors at top institutions. Served on 40+ program committees, including FOCS 2011 Chair. Labs: Leads the CICS research center, fostering interdisciplinary work in information security and cryptographic systems.

• Theoretical Computer Science
• Mathematics
• Spectral Graph Theory
• +5 more

Daniel Alan Spielman is the Sterling Professor of Computer Science at Yale University , where he has been affiliated since 2006. He serves as Chair of the Department of Statistics and Data Science and is Co-Director of the Yale Institute for Network Science. His research spans theoretical computer science and mathematics, focusing on algorithms, spectral graph theory, and numerical linear algebra. Educational background: Bachelor of Arts in Mathematics and Computer Science from Yale University (1992) PhD in Applied Mathematics from MIT (1995) Research interests include smoothed analysis of algorithms , graph-based codes , spectral graph theory , and numerical linear algebra . His work on the Kadison–Singer problem and graph theory applications has been groundbreaking. Scientific awards and honors include: Gödel Prize (2008, 2015) Nevanlinna Prize (2010) MacArthur Fellowship (2012) Pólya Prize (2014) Breakthrough Prize in Mathematics (2022) Election to the National Academy of Sciences (2017) His publications highlight advancements in network science , operator theory , and algebraic combinatorics , with a focus on algorithmic solutions and mathematical proofs.

Raffi KhatchadourianView profile

Associate Professor

• Software Engineering
• Programming Languages
• Software Evolution
• +5 more

Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College and the Graduate Center of the City University of New York (CUNY). His research focuses on techniques for automated software evolution, particularly automated refactoring and source code recommendation systems, with the goal of easing the burden associated with evolving large and complex software through automated tools. He also conducts research on the automated analysis of Object-Oriented programs. Ph.D., Computer Science & Engineering, Ohio State University (2011) MS, Computer Science & Engineering, Ohio State University (2010) BS, Computer Science, Monmouth University (2004) Khatchadourian's research spans multiple areas of software engineering and programming languages, with particular emphasis on automated software evolution techniques. His work addresses critical challenges in refactoring legacy systems to modern language constructs, optimizing parallel processing in Java 8 streams, and addressing technical debt in machine learning systems. His recent research has expanded into deep learning program transformation, where he develops techniques to convert imperative deep learning code to more efficient graph execution models while ensuring safety. His approach combines static analysis, program transformation, and empirical validation to create practical tools that developers can integrate into their workflows. Analysis of Khatchadourian's recent publications reveals a strong focus on bridging the gap between theoretical program analysis and practical software engineering challenges. His work increasingly intersects with machine learning systems, examining both how to improve ML code through refactoring and how to ensure safety in deep learning frameworks. The research demonstrates consistent evolution from foundational work on Java language features toward more complex systems involving concurrency, deep learning, and automated program transformation. Distinguished Paper Award at SCAM '18 for work on Java 8 stream optimization EAPLS Best Paper Award at FASE '20 for study on Java 8 stream usage EAPLS Distinguished Paper Award at FASE '25 for Deep Learning refactoring work Best Paper Award nominee at IJCAI '24 for AI safety framework Khatchadourian actively mentors graduate and undergraduate students, with several advisees going on to successful academic and industry positions. His former Ph.D. student Tatiana Castro Vélez accepted a tenure-track Assistant Professor position at the University of Puerto Rico. He has supervised numerous master's theses and undergraduate research projects, often resulting in co-authored publications at top software engineering venues. His research has been supported by various grants, though specific funding details are not prominently featured in the available information. Through his work on tools like Fraglight for aspect-oriented programming and Hybridize Functions for deep learning refactoring, Khatchadourian has established a research group focused on practical program analysis and transformation. His lab develops Eclipse plugins and other IDE-integrated tools that help developers with automated refactoring, bug detection, and code optimization. The group maintains active collaborations with researchers at other institutions and contributes to open-source projects on GitHub.

Jan SpakulaView profile

Associate Professor

• C*-algebras
• noncommutative geometry
• geometric group theory
• +2 more

Dr Jan Spakula is an Associate Professor in Pure Mathematics at the University of Southampton within the School of Mathematical Sciences. He joined the university in 2013 as a Lecturer and was promoted to Associate Professor in 2022, maintaining active research and teaching responsibilities. Education: PhD in Mathematics from Vanderbilt University (2008) Postdoctoral positions at the University of Münster and University of Vienna Research Focus: Spakula specializes in the intersection of operator algebras and geometric structures, particularly examining C*-algebras through the lens of noncommutative geometry and coarse geometric properties of groups. His work establishes critical connections between analytic representations and geometric boundaries of non-positively curved spaces, often collaborating with Kevin Boucher on boundary representation frameworks. Publication Trends: His 2013-2024 publications reveal persistent investigation into rigidity phenomena in Roe algebras, asymptotic expander constructions, and coarse median structures. This body of work consistently bridges geometric group theory with functional analysis, demonstrating how coarse geometric invariants manifest in operator algebraic settings through Property A and quasi-locality frameworks. Research Funding: Boundary representations of non-positively curved groups (EPSRC) EU FP7 - Coarse Analysis project (European Union) Academic Contributions: Dr Spakula teaches undergraduate modules including Linear Algebra II and Engineering Foundation Year Mathematics (GENG0001/GENG0002). He actively supervises research as a member of the Pure Mathematics Centre for Geometry, Topology, and Applications, currently accepting new PhD students for projects in geometric group theory and operator algebras.

Jared TannerView profile

Professor

• Compressed Sensing
• Matrix Completion
• Signal and Image Processing
• +7 more

Professor Jared Tanner is Professor of the Mathematics of Information at the University of Oxford's Mathematics Institute and a Fellow of Exeter College. Previously, he held positions at the University of Edinburgh (2007-2012) as Professor, Reader, and Lecturer in Mathematics, University of Utah (2006-2007) as Assistant Professor, and Stanford University (2004-2006) as an NSF Postdoctoral Fellow. His research focuses on extracting models from high-dimensional data to reveal essential information, with specific contributions including sampling theorems in compressed sensing using stochastic geometry, efficient algorithms for matrix completion, and theoretical understanding of deep neural networks. Recent interests include neural network initialization techniques to preserve geometric and information-theoretic properties, as well as network pruning methods. Professor Tanner has supervised numerous doctoral students at Oxford and Edinburgh, including Alireza Naderi, Thiziri Nait Saada, Ilan Price, Giuseppe Ughi, Charles Millard, Michael Murray, Simon Vary, Bernadette Stolz, Bogdan Toader, Rodrigo Mendoza-Smith, Ke Wei, Bubacarr Bah, and Andrew Thompson, many of whom have gone on to prestigious positions in academia and industry. His publication record spans over two decades with significant contributions to compressed sensing, matrix completion, and more recently deep learning theory. His work demonstrates a consistent progression from foundational theoretical work to practical applications in signal processing and machine learning. As an academic leader, Professor Tanner serves as Founding Editor-in-Chief of Information and Inference: A Journal of the IMA and has held editorial positions at several prestigious journals including Applied and Computational Harmonic Analysis and IEEE Signal Processing Letters . He has organized numerous conferences and workshops including Prospects in Mathematics and the FoCM Computational Harmonic Analysis workshop.

• Foundations of Computer Science
• Combinatorics
• Probabilistically Checkable Proofs (PCPs)
• +4 more

Irit Dinur is a professor at the Weizmann Institute of Science , specializing in theoretical computer science and combinatorics. She works in the Department of Applied Mathematics and Computer Science, focusing on probabilistically checkable proofs (PCPs), hardness of approximation, and high-dimensional expanders (HDX). Organizer of the Winter School on Expansion in Groups, Combinatorics, and Complexity (2025) at Weizmann Institute Co-organizer of the ICTS Workshop on HDX and Codes (Bangalore, April 2025) Teaching a summer course on Robust Computation: From Local to Global (2025) Her research bridges theoretical computer science and mathematics, with recent work exploring: Interactive proof systems and their applications to undecidability Connections between high-dimensional expanders and quantum LDPC codes Advances in PCP theory and their implications for computational complexity Sparse graph counting techniques in additive combinatorics She has been instrumental in organizing educational initiatives that explore cutting-edge topics like: Expansion properties in Cayley graphs Coboundary expansion in coset complexes Applications of finite free probability to expander graphs

• Combinatorics
• Algebraic Combinatorics
• Log-Concave Polynomials
• +13 more

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research lies at the intersection of combinatorics, optimization, and theoretical computer science, with a focus on log-concave and Lorentzian polynomials and their applications in discrete and continuous settings. Assistant Professor, University of Waterloo (2022–present) Dirichlet Postdoctoral Fellow, TU Berlin (2020–2022) Postdoctoral Fellow, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoctoral Fellow, KTH, Stockholm (Fall 2019) James H. Simons Fellow, Simons Institute, UC Berkeley (Spring 2019) His research explores the deep connections between algebraic structures and combinatorial phenomena, particularly through polynomial capacity and Lorentzian polynomials. He applies these tools to problems in optimization, sampling, and representation theory. His work often involves developing new algebraic and analytic techniques to tackle longstanding conjectures and algorithmic challenges. The recent publications highlight a consistent focus on Lorentzian polynomials, capacity bounds, and their applications in combinatorics, optimization, and theoretical computer science. Key themes include matroid theory, log-concavity, sampling algorithms, volume approximation, and connections to Lie theory and representation theory. The research spans both theoretical developments and algorithmic applications, often in collaboration with leading researchers in the field. Dirichlet Postdoctoral Fellowship, TU Berlin Postdoc Fellowship in Algebraic and Enumerative Combinatorics, Institut Mittag-Leffler James H. Simons Fellowship, Simons Institute, UC Berkeley Jonathan Leake has advised or collaborated with several researchers, though formal advisees are not listed in the provided text. His work has been supported by prestigious fellowships and collaborations with institutions such as the Simons Institute and TU Berlin. He has taught courses including CO 250: Introduction to Optimization, MATH 239: Introduction to Combinatorics, and CO 739: Lorentzian Polynomials at the University of Waterloo and TU Berlin. While specific lab or research group names are not mentioned, Leake's collaborative work with researchers like Petter Brändén, Nisheeth Vishnoi, and Leonid Gurvits suggests active participation in research teams focused on algebraic combinatorics, optimization, and theoretical computer science. His publicly shared code for sampling from HCIZ densities and verifying positivity in Lie-theoretic contexts indicates an active computational research component.

Pedro ParedesView profile

Lecturer

• Theoretical Computer Science
• Spectral Graph Theory
• Pseudorandomness
• +3 more

Pedro Paredes is a Lecturer in the Department of Computer Science at Princeton University . He completed his PhD in 2022 at Carnegie Mellon University under Ryan O'Donnell , following undergraduate and master's degrees from the University of Porto where he was advised by Pedro Ribeiro . He received the SEAS Excellence in Teaching Award in 2025. Education PhD, Carnegie Mellon University (2022) MSc & BSc, University of Porto (2017) Research Interests Specializes in Theoretical Computer Science , particularly Spectral Graph Theory , Pseudorandomness , Coding Theory , and Quantum Information Theory Contributes to Subgraph Analysis and Network Science through collaborative works Publication Trends Focuses on Expander Graphs and their applications in Quantum Computing , with recent works on Quantum LDPC Codes and Approximate Unitary Designs Develops algorithms for Spectral Graph Operations and Graph Expansion with mathematical rigor Engages in Interdisciplinary Research connecting Time Series Analysis with network theory Awards SEAS Excellence in Teaching Award (2025) Teaching & Outreach Teaches Algorithms and Data Structures at Princeton Organizes Competitive Programming Club at Princeton Active in Computer Science Education and Math Olympiads

• Geometry
• Topology
• Quantum Field Theory
• +5 more

Ryan Grady is an Associate Professor in the Department of Mathematical Sciences at Montana State University, affiliated with the College of Letters & Science. His research bridges geometry, topology, and quantum field theory (QFT), with a focus on derived geometry and higher Lie theory. Research Interests: His work applies QFT techniques to geometric and topological problems, explores derived algebraic structures (e.g., L-infinity spaces, cosheaves), and investigates connections between renormalization group flows and sigma models. Recent publications address K-theoretic invariants, operadic structures in topology, and algebraic models for topological field theories. Scientific Awards: Stannard Award for Graduate Teaching (2021) NExT Fellow (2019) ICCM Best Paper Award (2018) Research Enhancement Grant (2018) Member, MSU Center for Faculty Excellence (2017) Education: Ph.D. and M.S. in Mathematics from the University of Notre Dame (2012, 2009), and B.S. in Mathematics from the Colorado School of Mines (2007). Students: Co-chaired Eric Berry (PhD 2021) and Adam Howard (PhD 2021), advised Garrett Oren (MS 2021), and mentored Bryce Morrow (BS 2023).

Steven StrogatzView profile

Professor

• Nonlinear Dynamics
• Complex Systems
• Applied Mathematics
• +3 more

Steven Strogatz is the Susan and Barton Winokur Distinguished Professor for the Public Understanding of Science and Mathematics, and a Stephen H. Weiss Presidential Fellow at Cornell University's College of Arts and Sciences, Department of Mathematics. He holds a Ph.D. from Harvard University (1986). His research focuses on nonlinear dynamics, complex systems, and applied mathematics, with notable contributions to synchronization in networks, mathematical biology, and science communication. Strogatz is renowned for his books, including Infinite Powers and The Joy of x , and his media work, such as the New York Times column and Quanta Magazine's The Joy of Why podcast. He has received prestigious awards like the National Award for Science Communication and the George Pólya Prize for Mathematical Exposition. His academic roles include teaching courses on nonlinear dynamics and supervising research. Strogatz's work bridges academia and the public, emphasizing the beauty and relevance of mathematics. His research extends to modeling social networks, epidemic dynamics, and urban mobility systems, with applications in physics, biology, and technology.

Alex KontorovichView profile

Professor

• Automorphic Forms and Representations
• Homogeneous Dynamics
• Harmonic Analysis
• +5 more

Alex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, where he holds the academic rank of Professor. His primary affiliation is with the Department of Mathematics, School of Arts and Sciences. He currently serves as Managing Editor of the Journal of the Association for Mathematical Research and is Executive Director of Rutgers MathCorps . During the 2024-2025 academic year, he is on leave, visiting Princeton University and the Institute for Advanced Study (IAS). Kontorovich's research focuses on automorphic forms, homogeneous dynamics, harmonic analysis, and number theory, with interdisciplinary connections to data science and machine learning. His work bridges pure mathematics with computational aspects, including sphere packing geometry and arithmetic dynamics. He has held distinguished visiting roles, such as the 2020-21 Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath), where he contributed to academic content and exhibits. His academic career includes teaching advanced courses like Graduate Complex Analysis, Automorphic Representations, and History of Mathematics. Notable awards include the Simons Foundation Fellowship, von Neumann Fellowship at IAS, and Alfred P. Sloan Research Fellowship. Grants include multiple NSF awards (regular, CAREER, FRG) and a Binational Science Foundation grant. His research explores topics like spectral gaps, thin groups, and applications of modular forms to equidistribution problems. Kontorovich’s scholarly contributions extend beyond academia: he serves on the Scientific Board of Quanta Magazine , advises the Lean Focused Research Organization , and participates in editorial and strategic roles across institutions. His work emphasizes the interplay between theoretical mathematics and computational methods, shaping modern research in number theory and dynamics.

Emmanuel BreuillardView profile

Professor

• Combinatorics
• Geometric Group Theory
• Ergodic Theory
• +1 more

Emmanuel Breuillard is a Professor of Pure Mathematics at the University of Oxford. Previously, he held positions as Sadleirian Professor at the University of Cambridge (2017-2021), Professor at Université Paris-Sud (2008-2017), and Associate Hadamard Professor at École Polytechnique (2006-2008). His academic journey includes a PhD from Université Paris-Sud and Yale University, supervised by Frédéric Paulin and Gregory Margulis, and a Master in Mathematics from Cambridge. PhD: Université Paris-Sud (2004), supervised by Frédéric Paulin; Yale University, supervised by Gregory Margulis Master: University of Cambridge (2000) Ecole Normale Superieure (1997-1999) His research spans Combinatorics , Geometric Group Theory , Ergodic Theory , and Number Theory , focusing on approximate groups, expansion in finite simple groups, and Diophantine geometry. His recent work analyzes Elekes-Szabo problems in projective geometries. He has authored 50+ publications in prestigious journals, addressing topics like random polynomials, C*-simplicity, and uniform independence in linear groups. Fellow of the Royal Society (2024) ERC Consolidator Grant (2014-2019) Prix Charles-Louis de Freycinet (2013) European Mathematical Society Prize (2012) ERC Starting Grant (2008-2013) Cours Péccot, Collège de France (2006)

• Geometric Group Theory
• Group Rings
• Algebra
• +6 more

Dawid Kielak is a Professor of Pure Mathematics at the University of Oxford and a Tutorial Fellow at Hertford College, Oxford. He holds a DPhil from the University of Oxford and has held academic positions in Warsaw, Bonn, and Bielefeld before returning to Oxford. His research focuses on geometric group theory, particularly the interplay between group rings, algebraic structures, and topological spaces. He is a member of the Algebra and Topology research groups at Oxford. His research interests include geometric group theory, cohomology of arithmetic groups, ℓ²-invariants, and the study of automorphism groups of free groups. His work is funded by prestigious grants such as the ERC Starting Grant 'Fibring' (2019) and the ERC Consolidator Grant 'HigherHyper' (2024). He has also received awards like the Whitehead Prize (2022) and the Frontiers of Science Award (2023). Key research contributions include studies on Kazhdan constants for Chevalley groups, profinite rigidity of fibring, and coherence properties of groups. His work often bridges algebraic and topological methods, with applications to 3-manifold groups and random groups. He supervises multiple DPhil students and has mentored postdoctoral researchers in areas like ℓ²-Betti numbers and group rings. Teaching responsibilities include pure mathematics tutorials at Hertford College and advanced courses on geometric group theory, 3-manifolds, and ℓ²-invariants at the University of Oxford and Bielefeld University. His lab focuses on collaborative projects in algebraic topology and geometric group theory, with a particular emphasis on virtual fibring and higher Kazhdan properties.

Leonard J. SchulmanView profile

Professor

• Algorithms
• Communication Protocols
• Combinatorics
• +6 more

Leonard J. Schulman is a Professor of Computer Science at the California Institute of Technology (Caltech), where he has been on the faculty since 2000. He is affiliated with the Caltech Center for the Mathematics of Information (which he directed from 2003 to 2017) and the Institute for Quantum Information and Matter. His academic appointments have included positions at UC Berkeley, the Weizmann Institute of Science, the Georgia Institute of Technology, and the Mathematical Sciences Research Institute. Schulman received his BSc in Mathematics in 1988 and his PhD in Applied Mathematics in 1992, both from the Massachusetts Institute of Technology (MIT). Schulman's research spans several overlapping areas in theoretical computer science and applied mathematics. His work focuses on algorithms and communication protocols , combinatorics and probability , coding and information theory , and quantum computation . More recently, his research has expanded into causal inference and machine learning , particularly in the areas of mixture models, causal discovery, and structure learning. His approach combines deep theoretical insights with practical applications across multiple domains. An analysis of Schulman's recent publications (2019-2025) reveals a strong focus on causal inference and machine learning, particularly in the areas of mixture models, causal discovery, and structure learning. His work bridges theoretical computer science with statistical learning, often developing novel algorithms with provable guarantees. He has also maintained his foundational work in coding theory, algorithms, and quantum computation, demonstrating remarkable breadth across theoretical computer science. IEEE Schelkunoff Prize (2004) ACM Notable Paper (2012) UAI Best Paper Award (2016) FOCS Test of Time Award (2022) S. A. Schelkunoff Transactions Prize Paper Award (2004) SIAM Fellow NSF CAREER award NSF mathematical sciences postdoctoral fellowship MIT Bucsela prize in mathematics Schulman has advised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His former students and postdocs include notable researchers such as Ashwin Nayak, Yaoyun Shi, Sean Hallgren, Jie Gao, and Michael Langberg. He served as Editor-in-Chief of the SIAM Journal on Computing from 2013 through 2018 and has been on the editorial boards of several prestigious journals including the Journal of the ACM, ACM Transactions on Algorithms, and SIAM Journal on Discrete Mathematics. Schulman directs the Caltech Center for the Mathematics of Information, a research center focused on the mathematical foundations of information processing, communication, and computation. His work often involves interdisciplinary collaborations across computer science, mathematics, physics, and economics.

Ioan TodincaView profile

Professor

• Graph Algorithms
• Theoretical Computer Science
• Exact Algorithms
• +6 more

Ioan Todinca is a Professor of Computer Science at the University of Orléans, France, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades, with significant contributions to theoretical computer science, particularly in graph algorithms and distributed computing. Faculty of Science, University of Orléans LIFO Research Laboratory Member of Institut thématique pluridisciplinaire Modélisation, Systèmes, Langages (since 2014) Former director of MIPTIS doctoral school (2012-2014) Former head of Computer Science degree program (2007-2011) Former leader of LIFO Graphs, Algorithms and Computational Models team (2008-2012) Todinca's research focuses primarily on graph algorithms, with expertise in exact algorithms (moderately exponential), parameterized algorithms, and algorithms for specific graph classes. He has made significant contributions to techniques involving tree decompositions, treewidth, minimal separators, and potential maximal cliques. More recently, his work has expanded into distributed algorithms, especially in communication-constrained models like the broadcast congested clique. His research bridges theoretical foundations with practical algorithmic approaches for NP-hard problems. The analysis of his recent publications reveals a strong trend toward distributed computing problems, particularly in congested network models. His work spans from fundamental graph theory problems (cycle detection, graph modification) to applications in quantum computing and model checking. The consistent focus on communication complexity, verification, and efficient algorithms across different computational models demonstrates his ability to adapt theoretical computer science principles to emerging computational paradigms. Todinca has supervised numerous PhD students and has been actively involved in the theoretical computer science community through conference organization and editorial work. His publications appear consistently in top-tier venues including SIAM Journal on Computing, Algorithmica, and proceedings of major conferences like STACS, WG, and DISC. Teaching responsibilities include algorithms, graph theory, and discrete structures for undergraduate and graduate students, with previous experience teaching databases, programming, and software engineering. His educational materials are hosted on the university's Celene platform.