Jelani Nelson is a Professor and Department Chair in the UC Berkeley EECS Department (College of Engineering). His work focuses on theoretical computer science , particularly algorithms , data streams , dimensionality reduction , and privacy-preserving computation . Advising : Current students include Ishaq Aden-Ali, Xin Lyu, Mihir Singhal, and Hongxun Wu (co-advised with leading researchers). Education : PhD from MIT (George M. Sprowls Award), M.Eng from MIT. Research Highlights : Developed foundational results in Johnson-Lindenstrauss dimensionality reduction (optimality, sparse embeddings). Advancements in differential privacy (lower bounds, private mean estimation, threshold learning). Pioneering work on streaming algorithms for heavy hitters, norm estimation, and graph problems. Innovations in compressed sensing and oblivious subspace embeddings . Scientific Awards : PODS Best Paper Award (2011, 2022) IBM Pat Goldberg Memorial Best Paper Award (2011) George M. Sprowls Award for MIT doctoral thesis (2009) NeurIPS 2020 Spotlight Presentation
Debmalya Panigrahi is a Professor and Associate Chair in the Department of Computer Science at Duke University. He holds a PhD in Theoretical Computer Science from MIT and has prior affiliations with Microsoft Research, Bell Labs, and the Simons Institute for Theory of Computing. His research focuses on algorithms, particularly graph algorithms, algorithms under uncertainty, and learning-augmented methods. He has received NSF CAREER and other awards, and his work spans peer-reviewed publications in top venues like STOC, FOCS, and SODA. He advises PhD students and mentors postdocs, emphasizing theoretical contributions with practical applications. His teaching includes courses on approximation algorithms, graph algorithms, and discrete mathematics. Education: PhD (MIT, advised by David Karger), MSc (Indian Institute of Science, advised by Ramesh Hariharan), BSc (Jadavpur University). Research highlights include fastest algorithms for graph connectivity, learning-augmented approximation methods, and online algorithms. Funded by NSF, ARO, Google, and others. Current projects explore network reliability, hypergraph algorithms, and algorithmic fairness. His lab collaborates across theory, AI/ML, and databases at Duke. Recent Grants: NSF CCF-2006512, CCF-1618286, CCF-1350537 Labs/Teams: Duke Algorithms Lab, Theory Group, Collaborations with CS-Econ and AI/ML groups Publications span 150+ papers, with 5+ journal articles in SIAM Journal of Computing and ACM Transactions. Recent focus on integrating machine learning into classical algorithms to improve worst-case performance bounds. Advised 10+ PhD students, many now in academia (e.g., UI Chicago, UT Dallas) and industry (Google, Microsoft).
Monika Henzinger is Professor at the Institute of Science and Technology Austria (ISTA), heading the research group of Theory and Applications of Algorithms. She also serves as Vice President for Technology Transfer at ISTA since 2024. Previously, she held professorships at the University of Vienna (2009-2023) and EPFL, Switzerland (2005-2009), was Director of Research at Google (1999-2005), and served as Assistant Professor at Cornell University. Professor Henzinger's research centers on efficient algorithms and data structures with three main thrusts. First, she investigates dynamic settings where program inputs are repeatedly updated, seeking solutions faster than restarting computations. Second, she develops privacy-preserving algorithms that add minimal noise to protect input data while maintaining efficiency. Third, she translates theoretically optimal algorithms into practical implementations for dynamically changing inputs. Her work consistently addresses resource conservation in data processing, particularly computing time and memory space, while exploring the theoretical limits of possible savings. Henzinger's recent publications (2024-2025) reveal strong trends in dynamic algorithms, differential privacy, and graph theory. Her research consistently bridges theoretical computer science with practical applications, focusing on algorithms that adapt to changing inputs while preserving computational efficiency and data privacy. She has made significant contributions to problems like dynamic matching, minimum cut computation, and privacy-preserving data analysis across various domains. Professor Henzinger has received numerous prestigious awards and honors: Wittgenstein Award (2021) Two ERC Advanced Grants (2014, 2021) Carus Medal of the German Academy of Sciences Leopoldina (2019) SIGIR Test of Time Award Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) CAREER Development Award of the National Science Foundation Best paper Award at the Symposium on Discrete Algorithms (2024) Professor Henzinger currently advises PhD students Bardiya Aryanfard, Antoine El-Hayek, and Roodabeh Safavi Hemami, along with postdocs Anamay Chaturvedi and Niklas Hahn. Her research is supported by multiple significant grants including an ERC Advanced Grant for 'Design and Evaluation of Modern, Fully Dynamic Data Structures,' the FWF Wittgenstein Prize, and the WEAVE Project on 'Static and dynamic hierarchical graph decompositions.' She also serves as Principal Investigator for the FWF project 'Fast algorithms for a reactive network layer,' providing substantial funding for her innovative work in algorithms and data structures. Professor Henzinger leads the Theory and Applications of Algorithms research group at ISTA, which focuses on developing practical algorithms for dynamic environments. Her team investigates resource conservation in data processing, specializing in dynamic algorithms that efficiently handle changing inputs, privacy-preserving algorithms that minimize noise while protecting data, and translating theoretical algorithms into practical implementations. The group maintains a strong presence in theoretical computer science through regular publications in top conferences and journals, and collaborates extensively with institutions worldwide to advance algorithmic research.
Chandra Chekuri is the Paul and Cynthia Saylor Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, situated within the Grainger College of Engineering. He has been actively contributing to theoretical computer science for over two decades, with significant leadership roles including serving as Editor-in-Chief of the prestigious SIAM Journal on Computing since May 2025. His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras in 1993, followed by a Ph.D. in Computer Science from Stanford University in 1998. Prior to joining UIUC, he spent eight years as a Member of Technical Staff at Bell Labs, Lucent Technologies. Chekuri's research focuses on theoretical computer science with particular emphasis on the design and analysis of algorithms, discrete and combinatorial optimization, approximation algorithms, mathematical programming, and graph theory. His work explores fundamental connections between discrete structures and optimization problems, with applications spanning network design, data analysis, and computational complexity. His recent publications demonstrate a continued focus on hypergraph algorithms, submodular function optimization, and graph partitioning problems, showing how theoretical insights can yield practical algorithmic improvements. His approach often combines continuous relaxations with discrete rounding techniques to develop approximation algorithms for NP-hard problems. As Editor-in-Chief of SIAM Journal on Computing, Chekuri leads one of theoretical computer science's most respected publications, which covers analysis and design of algorithms, algorithmic game theory, computational complexity, and other mathematical aspects of computer science. His editorial leadership follows previous service as Associate Editor for several major journals including SIAM Journal on Computing, Mathematics of Operations Research, and Mathematical Programming. ACM Fellow (January 2024) Scott Fisher Teaching Award (for year 2022-23) from CS Department Chekuri has advised numerous PhD students to completion, including Kent Quanrud, Vivek Madan, Shalmoli Gupta, and Chao Xu, with several currently in progress such as Tanvi Bajpai, ElFarouk Harb, Rhea Jain, and Weihao Zhu. His teaching portfolio includes graduate courses on Randomized Algorithms, Approximation Algorithms, Algorithms for Big Data, and Combinatorial Optimization. He has served as Director of the Graduate Program in the Department of Computer Science from May 2014 to August 2017, demonstrating significant administrative leadership within the department.
Jason Li is an Assistant Professor in the Department of Computer Science at Carnegie Mellon University's School of Computer Science. He teaches advanced algorithms courses including 15-754 Spectral Graph Theory (Spring 2025), 15-451 Design and Analysis of Algorithms (Fall 2024), and 15-850 Advanced Algorithms (Spring 2024). His research focuses on fast graph algorithms , particularly solving longstanding open problems through modern algorithmic techniques. Key research themes include preconditioning and locality , which serve as reductions from worst-case to well-behaved and local instances respectively. His work has produced breakthroughs in deterministic global minimum cut algorithms, all-pairs minimum cut (Gomory-Hu trees), and near-optimal parallel shortest path algorithms. Analysis of his recent publications reveals a consistent trend toward almost-linear time algorithms for fundamental graph problems, with significant contributions to dynamic graph algorithms, minimum cut variants, and parallel computation. His work frequently appears in top venues including STOC, FOCS, and SODA, often with multiple best paper recognitions. EATCS Distinguished Dissertation Award (2021) Best Paper Award at SODA 2024 Invited to HALG 2024 Invited to TALG and JACM for SODA 2024 paper Machtey Best Student Paper at FOCS 2019 Professor Li actively advises graduate students including Henry Fleischmann and George Li. His research is supported by collaborations with leading institutions and frequent invitations to present at major conferences. He maintains an open-door policy for CMU students and collaborators, though notes the high volume of research inquiries he receives weekly.
Satish Rao is a Professor in the Computer Science Division at the University of California, Berkeley. He is affiliated with the Simons Institute for the Theory of Computing and the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB). His research focuses on algorithms, combinatorial optimization, graph theory, and theoretical computer science with applications to computational biology and machine learning. Rao has held teaching roles for courses such as CS 70 (Discrete Mathematics and Probability Theory) and CS 270 (Spring 2024). He has been recognized with prestigious awards including ACM Fellow (2013), the Delbert Ray Fulkerson Prize (2012), and the Okawa Research Grant (1999). Research Interests: Algorithm design, graph algorithms, combinatorial optimization, computational biology, and machine learning. Key Contributions: Pioneering work on metric embeddings, approximation algorithms, and network flow problems. Notable publications include foundational papers on tree metrics, distributed object location, and electrical flow-based optimization. Rao’s work bridges theoretical computer science with practical applications, including contributions to phylogeny estimation, anomaly detection, and parallel computing frameworks like the BSP model.
Karan Singh serves as an Assistant Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, where he develops theoretically rigorous algorithms for machine learning systems with emphasis on reinforcement learning and control theory. His work synthesizes techniques from online learning, optimization, and statistics to address complex interactive learning challenges. His academic journey includes: PhD in Computer Science from Princeton University under Elad Hazan Postdoctoral research at Microsoft Research (Redmond) Bachelor's degree in Computer Science from Indian Institute of Technology (IIT) Kanpur Singh's research program centers on three interconnected pillars: Algorithmic Reductions : Creating efficient methods to solve complex learning problems (e.g., reinforcement learning) using solvers for simpler tasks, yielding breakthroughs in online boosting and RL with concave rewards Nonstochastic Control : Establishing an algorithmic foundation for control theory through provably efficient instance-optimal algorithms that extend online learning to stateful systems Privacy-Preserving Online Learning : Investigating fundamental limits of regret minimization under differential privacy constraints while maintaining performance His approach consistently bridges theoretical computer science and practical control applications. Analysis of his 15 most recent publications reveals a clear evolution toward integrating algorithmic reductions with nonstochastic control frameworks. Recent work (2023-2025) demonstrates increasing focus on sample efficiency in agnostic boosting, privacy-aware optimization without smoothness assumptions, and competitive ratio analysis in online control. A unifying thread is the development of regret-optimal algorithms for linear dynamical systems under adversarial disturbances. His contributions have earned significant recognition: Best Paper Award at OptRL workshop (NeurIPS 2019) Spotlight Prize from New York Academy of Sciences' ML Symposium (2018) Multiple oral presentations at NeurIPS/ICML (acceptance rate Though specific student advisees aren't listed, Singh's extensive publication record with junior co-authors indicates active mentorship. His research has secured substantial support including a US patent (11,138,513 B2) for dynamic learning systems and collaborations through CMU's Machine Learning and Optimization group. Current projects involve interdisciplinary work on differentiable control libraries (Deluca) and medical applications like mechanical ventilation control. Singh leads research within CMU's Machine Learning and Optimization ecosystem, collaborating across computer science and engineering departments. His team develops foundational tools like the Deluca differentiable control library while pursuing real-world applications in healthcare systems, demonstrating strong cross-disciplinary integration.
Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .
Debmalya Panigrahi is a Professor of Computer Science at Duke University and serves as Associate Chair in the Department of Computer Science since 2025. He received his Ph.D. in Theoretical Computer Science from the Massachusetts Institute of Technology (MIT), advised by David Karger, and also studied at the Indian Institute of Science and Jadavpur University. Ph.D., MIT, 2012 Indian Institute of Science Jadavpur University His research focuses on algorithms design and analysis, particularly in graph algorithms (minimum cuts, vertex connectivity, max-flows) and algorithms under uncertainty (online algorithms, learning-augmented frameworks). He also works on approximation algorithms, algorithmic game theory, and practical applications in advertising, AI, and network design. Recent publications address problems like network unreliability estimation, convex paging with fairness constraints, and hypergraph reliability. His work combines theoretical rigor with practical impact, including patents and prototypes. NSF CAREER Award He has received grants from the National Science Foundation (including multi-objective optimization projects), Google Inc., and the Indo-US Science and Technology Forum. He mentors graduate and undergraduate students, including current PhD candidates Ruoxu Cen and Anish Hebbar. Panigrahi is affiliated with Duke's theory group and collaborates with CS-econ, AI/ML, and database groups. He recently returned from a sabbatical at Berkeley (Simons Institute and UC Berkeley) and maintains strong ties with industry through roles at Google Research and Microsoft Research.
David P. Woodruff is a Professor in the Department of Computer Science at Carnegie Mellon University, part of the Theory Group within the School of Computer Science. He is actively involved in academic leadership roles, including chairing the CATCS (Conference on Theoretical Computer Science) and serving as PC chair for SODA 2024 and ICALP 2022. His research focuses on algorithms, data streams, machine learning, numerical linear algebra, sketching, and sparse recovery. He has been recognized with awards such as the Herbert Simon Award for teaching and the PODS Best Paper Award. Woodruff has advised numerous students and postdocs, including notable scholars like Ainesh Bakshi, Rajesh Jayaram, and Hongyang Zhang. His work often addresses foundational challenges in theoretical computer science, with contributions to distributed computing, streaming algorithms, and privacy-preserving techniques. He has published extensively in top conferences like NeurIPS, ICML, FOCS, and STOC, covering topics ranging from low-rank approximation to adversarial robustness in data streams. His teaching includes courses like Algorithms for Big Data and core algorithms courses, reflecting his commitment to both research and education. Collaborations span academia and industry, with applications in genomics and secure computation. Woodruff is a key contributor to the Foundations of Data Science program at the Simons Institute.
Fabio Furini is an Associate Professor at the Department of Computer Science, Automatics, and Management (DIAG) at Sapienza University of Rome since September 2021. Prior to this position, he served as a CNR researcher at IASI-CNR in Rome (2020-2021), Maître de Conférences at Université Paris-Dauphine, France (2013-2019), postdoctoral researcher at Université Paris-13, France (2012-2013), and research fellow at the University of Bologna (2011-2012). His educational background includes a Ph.D. in Control Engineering and Operations Research from the University of Bologna in 2011. He further obtained the Habilitation à Diriger des Recherches (HDR) in France in 2017 and the National Scientific Qualification for Full Professor in Operations Research in Italy in 2019. Fabio Furini conducts theoretical and methodological research on Combinatorial Optimization and Operations Research. His primary focus is on developing exact algorithms based on decomposition and reformulation techniques for integer linear programming problems. His research spans various applications including network optimization, graph theory, and combinatorial problems such as the maximum clique problem, bin packing problem, and vertex separator problem. His work often bridges theoretical developments with practical applications in transportation, logistics, and network security. His recent publications demonstrate a strong focus on exact algorithms for combinatorial optimization problems, particularly in network interdiction, bin packing with temporal constraints, and graph-based problems. His work consistently combines integer programming techniques with combinatorial search methods to develop novel formulations and efficient solution approaches that advance the state-of-the-art in these domains. Among his notable scientific awards are the Prime d'encadrement doctoral et de recherche (PEDR), which he received annually from 2014 to 2020, recognizing him among the top 15% of researchers in the French university system. He also holds the prestigious Habilitation à Diriger des Recherches from France (2017) and the National Scientific Qualification for Full Professor in Operations Research from Italy (2019). Fabio Furini has been actively involved in supervising PhD students and has served as principal investigator for numerous national and international research projects. His extensive network includes over 60 co-authors across European and American universities. He is also a member of the editorial boards for three prestigious international journals: Omega, Annals of Operations Research, and Discrete Applied Mathematics. His research activities include collaborations with various institutions across Europe and the United States, including Imperial College London and the University of Colorado. These collaborations have resulted in a robust research program focused on advancing the theoretical foundations and practical applications of combinatorial optimization.
James B. Orlin is the E. Pennell Brooks (1917) Professor in Management and a Professor of Operations Research at the MIT Sloan School of Management. He specializes in network and combinatorial optimization with applications spanning transportation, computer science, operations, and marketing. BA in Mathematics, University of Pennsylvania MA in Mathematics, California Institute of Technology MMath, University of Waterloo PhD in Operations Research, Stanford University His research focuses on designing efficient algorithms for network optimization problems, including shortest path, max flow, and min cost flow. He has contributed to algorithmic theory in logistics, telecommunications, and inventory management, with work on stochastic demand models and data-driven inventory policies. Recent publications include advancements in directed shortest path algorithms, robust submodular function maximization, and energy storage problem complexity. His seminal textbook Network Flows: Theory, Algorithms, and Applications (1993) remains a foundational reference. Leonard G. Abraham Prize Khachiyan Prize Test of Time Award As a mentor, he has advised numerous researchers through collaborative publications and teaching. His work addresses both theoretical algorithm development and practical implementation across diverse domains including airline scheduling, logistics, and network design.
Emily Kyle Fox is an Associate Professor in the Department of Computer Science at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC, 2013), followed by postdoctoral positions at Duke University and Brown University’s Institute for Computational and Experimental Research in Mathematics (ICERM). Her research focuses on algorithmic foundations with an emphasis on computational geometry, topology, and graph algorithms, particularly leveraging topological methods to design efficient algorithms for complex problems. Education Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) M.S. in Computer Science, University of Illinois at Urbana-Champaign (2010) B.S. in Computer Science, University of Illinois at Urbana-Champaign (2008) Research Interests Computational Geometry & Topology Graph Algorithms & Optimization Algorithm Design for Surface-Embedded and Geometric Networks Applications of Topology in Algorithm Development Publications Trends Her work includes breakthroughs in geometric transportation problems, minimum cut algorithms on hypergraphs/surface graphs, and efficient approximation schemes for transshipment and Fréchet edit distance. Recent contributions emphasize deterministic algorithms with near-linear time complexity and applications of topology to graph algorithm design. Awards NSF CAREER Award (2020) Best Teacher in Computer Science (UTD, 2020) Stutzke Dissertation Completion Fellowship (UIUC, 2013) Grants & Affiliations Dr. Fox secured a $586,654 NSF CAREER grant (2020) for topology-driven algorithm design. She serves on UTD’s Graduate Admissions Committee and actively contributes to the Algorithms and Theory Group. Labs/Teams Active in the Algorithms and Theory Group at UTD, focusing on foundational algorithm research with geometric and topological applications.
Dr. Kiril Kuzmin is a Lecturer in the Department of Computer Science at Georgia State University, where he teaches Data Structures, Algorithms, Data Science, and Machine Learning. He holds a Ph.D. in Computer Science (2024) with a concentration in Bioinformatics from Georgia State University and a Ph.D. in Mathematics (2009) from the National Academy of Sciences of Belarus. His academic journey includes roles as Assistant and Associate Professor at Belarusian State University and a postdoctoral fellowship at the University of Turku, Finland. Dr. Kuzmin’s research focuses on Bioinformatics, Machine Learning, Discrete Optimization, and Graph Theory. He has published over 50 papers, with notable contributions in stability analysis of combinatorial optimization problems and applications of machine learning in genomics. His work includes predicting host specificity of coronaviruses and developing algorithms for heterogeneous genomic population analysis. Dr. Kuzmin has received the Scopus Award in Mathematics (2013) and served as PI/co-PI on three international projects. His teaching spans Java programming, Data Structures, and advanced mathematical courses like Calculus and Algebra. He is affiliated with Georgia State’s bioinformatics research group and has actively contributed to academic and political advocacy in Belarus.
Yuri Faenza is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia Engineering, with affiliations to the Data Science Institute (DSI) and Foundations of Data Science Center. His research bridges Discrete Optimization, Operations Research, and Computer Science, focusing on algorithmic theory and applications to Market Design (particularly School Choice) and Machine Learning. Key research areas include Knapsack , Matching , and Extended Formulations , with methodological work on polytope structures, greedy algorithms, and stochastic or semi-random models. His recent publications emphasize stable matching, optimization under uncertainty, and the intersection of discrete mathematics with data science. Scientific awards include the NSF CAREER award Meta Research Award . He has served on program committees for major conferences like ALGA, EC, APPROX, and IPCO, and is an Associate Editor for journals including Mathematical Programming and Discrete Optimization . Prior to Columbia, he held postdoctoral positions at the University of Brussels, EPFL, and University of Padua.
