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Jakob NordströmView profile

Professor

• Computer Science
• Algorithms and Complexity
• Proof Complexity
• +5 more

Jakob Nordström is a full-time Professor at the Department of Computer Science , University of Copenhagen , and holds a part-time affiliation with Lund University since 2020. His research lies at the intersection of theoretical computer science , proof complexity , and combinatorial optimization . His work focuses on SAT solving , Pseudo-Boolean optimization , and formal verification of algorithms. Recent research includes polynomial calculus lower bounds , dynamic programming certification , and MIP-based presolve techniques . Publications span journals like the Journal of the ACM and conferences including SAT and CADE . Scientific awards include the STOC '06 Best Student Paper Award and the Ackermann Award 2009 . He is a member of the Young Academy of Sweden (since 2018). Research funding comes from the Independent Research Fund Denmark and the Swedish Research Council . He has contributed to proof complexity , resolution trade-offs , and algebraic reasoning in combinatorial search. His work bridges theoretical lower bounds with practical Pseudo-Boolean solvers and certified algorithms .

• Cryptography
• Computational Complexity
• Pseudorandomness
• +5 more

Andrej Bogdanov is a Professor in the Department of Computer Science at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. With a prolific publication record spanning over two decades from 2002 to 2025, he has established himself as a leading researcher in theoretical computer science and cryptography. His research interests span multiple areas of theoretical computer science, with a particular focus on cryptography, computational complexity, pseudorandomness, and secret sharing. His work often bridges theoretical foundations with practical cryptographic applications, exploring the mathematical underpinnings of secure computation and cryptographic primitives. His research has evolved to address contemporary challenges in quantum computing security and machine learning evaluation. Bogdanov's publication record shows consistent contributions to top-tier conferences including FOCS, STOC, CRYPTO, TCC, and ITCS. His work demonstrates deep theoretical insights while maintaining relevance to practical cryptographic applications. Recent publications indicate expanding interests into quantum computing security and machine learning evaluation frameworks. Bogdanov has collaborated extensively with leading researchers in theoretical computer science, most notably with Alon Rosen (31 joint publications), as well as Siyao Guo, Yuval Ishai, and Chin Ho Lee. His collaborative work spans multiple institutions and reflects the interdisciplinary nature of modern theoretical computer science research. His academic contributions include foundational work on pseudorandom generators, secret sharing schemes, hardness amplification, and more recently, contributions to post-quantum cryptography and quantum security. His research has been supported by multiple grants that have enabled his team to explore the theoretical boundaries of cryptographic security.

Leslie G. ValiantView profile

Professor

• Complexity Theory
• Machine Learning
• Parallel Computation
• +4 more

Leslie G. Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences. He holds a Ph.D. from the University of Warwick (1974), with prior education at King's College, Cambridge, and Imperial College, London. His research spans complexity theory, learning algorithms, parallel computation, computational neuroscience, and evolutionary theory. Valiant's work bridges artificial and natural computational systems, addressing fundamental questions in both domains. His research interests include reconciling logical reasoning with statistical learning via 'robust logics,' exploring computational limits of cortical networks, and analyzing evolutionary processes quantitatively. He has authored seminal books like Circuits of the Mind and Probably Approximately Correct . His recent articles focus on holographic algorithms, neural computation, and evolutionary theory, reflecting his interdisciplinary approach. Notable awards include the Nevanlinna Prize (1986), the Turing Award (2010), and fellowship in the Royal Society. Valiant's contributions have redefined theoretical computer science and its intersection with biology and neuroscience.

Frederic GreenView profile

Researcher

• Theory of Computation
• Circuit Complexity
• Computational Complexity
• +3 more

Dr. Frederic Green is a Senior Research Scientist in the Department of Computer Science at Clark University. With degrees in both physics and computer science, his research spans theoretical and computational aspects of computer science with interdisciplinary connections to physics. His scholarly work focuses on circuit complexity, computational complexity theory, quantum computation, and the application of computational methods to theoretical physics problems. Dr. Green has published extensively on topics including quantum circuit design, complexity bounds for algebraic operations, and the theoretical foundations of computation. He maintains an active scholarly review practice, contributing critical analyses of foundational texts in computation theory, discrete geometry, and mathematical physics. Recent publications include reviews of major works in complexity theory, computational geometry, and quantum gravity.

Lulu QianView profile

Professor

• Biological Engineering
• Molecular Programming
• DNA Nanotechnology
• +6 more

Lulu Qian is a Professor of Bioengineering at the California Institute of Technology (Caltech), leading the Qian Lab. She holds a B.E. from Southeast University (2002) and a Ph.D. from Shanghai Jiao Tong University (2007). Prior to her current role, she served as an Assistant Professor at Caltech (2013–2018). Her research focuses on designing molecular systems with programmable behaviors, including biochemical circuits, DNA neural networks, and molecular robots. These systems aim to mimic biological intelligence and perform tasks like pattern recognition, decision-making, and spatial organization at the molecular level. Her lab’s key projects include developing DNA strand displacement cascades for scalable computation, creating DNA-based neural networks capable of associative memory, and engineering molecular robots that perform collective tasks like cargo sorting. Notable achievements include the world’s smallest DNA-based Tic-Tac-Toe game, fractal assembly of DNA origami arrays, and a DNA robot capable of sorting molecules. Qian’s work bridges molecular engineering, computer science, and synthetic biology to advance nanotechnology and programmable matter. Her lab actively collaborates with Caltech’s graduate programs in Bioengineering, Computer Science, and Chemical Engineering. Current and past students have contributed to projects ranging from programmable DNA circuits to bio-inspired robotics. The lab emphasizes interdisciplinary training, preparing students for careers in academic research and industry innovation.

• Algorithms and Complexity
• Discrete Mathematics
• Structural Graph Theory
• +2 more

Dr. Igor Razgon serves as an Associate Professor in the Department of Computer Science at Durham University, where he conducts foundational research at the intersection of discrete mathematics and theoretical computer science. His work bridges abstract graph theory with practical applications in artificial intelligence systems and database optimization. His research program centers on structural graph theory, algorithms and complexity, with particular emphasis on tree-width parameters, hypertree decompositions, and constraint satisfaction frameworks. This work establishes critical connections between graph structural properties and computational tractability boundaries, directly impacting query optimization in database management systems and knowledge representation in AI. Analysis of his 2021-2025 publications reveals consistent focus on dichotomy theorems, fractional decomposition methods, and width parameters for hypergraphs. His research demonstrates how structural graph properties determine computational complexity thresholds, with direct applications to database query optimization and constraint-based reasoning systems. Professional recognition includes: Nerode Prize (2020) for contributions to computational complexity theory While his publications indicate significant collaborative work with leading researchers in discrete mathematics, the available information does not specify doctoral advisees, grant funding sources, or laboratory affiliations. His research trajectory suggests ongoing investigation into structural parameters for combinatorial problem solving.

• Computational Complexity
• Cryptography
• Theoretical Computer Science
• +3 more

Manuel Blum is the Bruce Nelson Professor of Computer Science at Carnegie Mellon University's School of Computer Science, Department of Computer Science. He is renowned for foundational contributions to computational complexity, cryptography, and theoretical computer science. His work includes pioneering research on CAPTCHAs, human computation, and self-testing programs. Blum has collaborated extensively with figures like Luis von Ahn, contributing to projects like Peekaboom and Verbosity. His research also extends to philosophical inquiries into consciousness, blending computer science with neuroscience and philosophy. Blum's teaching includes courses on theoretical cryptography (15-503) and undergraduate complexity theory (15-455). He has advised students such as Brendan Juba and Matt Humphrey, whose work explores computational complexity and related fields. His research spans grants on machine learning, game theory, and computational complexity, including an NSF proposal on understanding concept understanding. Blum's contributions to security, randomness, and algorithm design have had lasting impacts on computer science.

Ryan O'DonnellView profile

Professor

• Algorithms and Complexity
• Security and Privacy
• Theoretical Computer Science
• +6 more

Ryan O'Donnell is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He specializes in theoretical computer science, with a focus on algorithms, complexity theory, quantum computing, and probability. His research includes Fourier analysis of Boolean functions, constraint satisfaction problems, and quantum information theory. He teaches courses such as 15251 (Fall 2025), 15754 (Spring 2025), and others. His work spans quantum tomography, pseudorandomness, and algorithmic design. Recent publications explore topics like quartic quantum speedups, uniformity testing, and explicit expanders. He advises PhD students William He, Noah Singer, and Jingxun Liang. His research interests bridge foundational theory and practical applications, including healthcare informatics and cryptographic tools. He actively contributes to academic conferences and program committees, reflecting his leadership in the field.

David D. GamarnikView profile

Professor

• Discrete Probability
• Optimization and Algorithms
• Quantum Computing
• +3 more

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

Lisa HellersteinView profile

Professor

• Approximation Algorithms
• Stochastic Combinatorial Optimization
• Machine Learning Theory
• +3 more

Lisa Hellerstein is a Professor in the Department of Computer Science and Engineering at the NYU Tandon School of Engineering. She holds an A.B. from Harvard University (1984) and a Ph.D. from the University of California, Berkeley (1989). Her research focuses on Approximation Algorithms, Stochastic Combinatorial Optimization, Machine Learning Theory, Boolean Functions, and Computational Complexity. Her work spans theoretical computer science with contributions to algorithmic efficiency, stochastic decision-making, and discrete mathematics. Notable publications include advancements in min-sum ordering problems, stochastic score classification, and approximation algorithms for Boolean function evaluation. She is affiliated with the Algorithms and Foundations Group at NYU Tandon, contributing to projects like 'Minimum Cost Strategies for Sequential Search and Evaluation' and 'Robust Uncertain Data Management.' Her research has appeared in top venues such as ITCS, ESA, SODA, and SIAM Journal on Computing, with a focus on theoretical guarantees and practical algorithmic solutions. She has advised numerous graduate students and holds grants related to computational learning and optimization frameworks.

Michael FourmanView profile

Professor

• Logic in Computer Science
• Artificial Intelligence
• Cognitive Science
• +3 more

Michael Fourman is a Professor of Computer Systems at the School of Informatics, University of Edinburgh, within the College of Science and Engineering. His research focuses on applications of logic in Computer Science, Artificial Intelligence, and Cognitive Science, with contributions to formal methods, programming language semantics, and hardware specification. He has advised numerous PhD students and collaborated on EPSRC-funded projects, including work on probabilistic reasoning and program development tools like PropPlan. His research interests bridge mathematical logic, computer science, and cognitive science, emphasizing formal representations and their practical applications. He has explored categorical semantics for programming languages, hardware design tools, and AI planning systems. Recent work includes integrating logical and probabilistic reasoning frameworks. Key tools developed include PropPlan (BDD-based planner) and PropSat , alongside contributions to formal verification and categorical logic. His publications span over four decades, addressing topics from intuitionistic logic to modern informatics challenges.

Andreas VenerisView profile

Assistant Professor

• Digital Systems Verification
• Circuit Synthesis
• Boolean Satisfiability
• +3 more

Andreas Veneris serves as an Assistant Professor in the Department of Informatics at the School of Information Sciences and Technology, Athens University of Economics and Business (AUEB). His academic career is rooted in computer engineering and theoretical computer science, with a focus on digital systems and computational methods. Education: Diploma in Computer Engineering and Informatics (1991), University of Patras M.Sc. (1992), University of Southern California Ph.D. (1998), University of Illinois at Urbana-Champaign Research Focus: Dr. Veneris specializes in the verification and synthesis of digital systems and circuits, with particular expertise in Boolean satisfiability solvers. His work bridges theoretical computer science with practical engineering applications, focusing on developing efficient algorithms and data structures for complex computational problems in hardware design and verification. His research intersects with formal methods, computational logic, and electronic design automation, contributing to advancements in reliable hardware systems development. Professional Activities: Based in the Antoniadou Wing, 3rd floor at AUEB, Dr. Veneris contributes to both undergraduate and postgraduate education in computer science. His work connects with various research groups and laboratories within AUEB's Department of Informatics, which participates in numerous national and European research projects. The department maintains strong industry connections and has produced notable alumni working at leading technology companies worldwide.

Szemerédi EndreView profile

Professor

• Discrete mathematics
• Combinatorics
• Theoretical computer science
• +1 more

Endre Szemerédi is a Hungarian mathematician and computer scientist, currently serving as a Permanent Research Fellow at the Alfréd Rényi Institute of Mathematics (Hungarian Academy of Sciences) and holding the New Jersey Professor of Computer Science position at Rutgers University since 1986. His career spans prestigious institutions including Stanford, Caltech, and the Institute for Advanced Study. Expertise: Discrete mathematics, combinatorics, theoretical computer science, and number theory. Education: M.Sc. (1965, Eötvös Loránd University), Ph.D. (1970, Moscow State University under Israel Gelfand). Research legacy encompasses foundational contributions to combinatorics, graph theory, and computational complexity. His work on Hamiltonian cycles in random graphs , parallel sorting networks , and the Blow-up Lemma revolutionized extremal combinatorics and algorithm design. Scientific honors include: 2012 Abel Prize and Széchenyi Prize 2008 Rolf Schock Prize and Steele Prize 1982 Member of Hungarian Academy of Sciences , 2010 Member of US National Academy of Sciences 1975 Pólya Prize , 1973 Rényi Prize , multiple Grünwald Prizes .

László BabaiView profile

Professor

• Theoretical Computer Science
• Discrete Mathematics
• Computational Complexity Theory
• +7 more

László Babai is the Bruce and Diana Rauner Distinguished Service Professor of Computer Science and Mathematics at the University of Chicago. He specializes in theoretical computer science, discrete mathematics, computational complexity theory, and combinatorics, with a focus on the interplay between these fields. His work often employs probabilistic methods and has contributed to breakthroughs such as the introduction of Las Vegas algorithms and holographic proofs. His research interests include algorithm design, computational group theory, and the analysis of Boolean circuits and branching programs. Babai is a prominent figure in the Theoretical Computer Science Group at UChicago, which bridges computer science with mathematics, physics, and statistics. Awarded numerous honors, Babai has received the Knuth Prize, Dijkstra Prize, Gödel Prize, and ACM distinctions. He also holds prestigious professorships and academic titles, including membership in the Hungarian Academy of Sciences and an honorary doctorate. His teaching excellence is recognized via the Quantrell Award. Babai’s contributions span foundational computer science theory, with impacts on cryptography, randomized algorithms, and structural group theory. His work remains influential in both academia and interdisciplinary research.

Janos SimonView profile

Professor

• Computational Complexity
• Algorithms
• Distributed Computing
• +1 more

Janos Simon is a Professor of Computer Science at the University of Chicago. His research focuses on computational complexity, algorithms, and distributed systems, with special interests in lower bound techniques and fault-tolerant models. He serves as Editor in Chief of the Chicago Journal of Theoretical Computer Science. His research explores diverse areas including combinatorial algorithms for optical networks, distributed computing in mobile networks, and biologically-inspired computational models. Work spans theoretical foundations to applied problems in vehicular networks and sensor systems. Professor Simon has supervised numerous PhD students in theoretical computer science and maintains active collaborations in fault-tolerant distributed computations and complexity theory.