Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at the School of Computer Science, Georgia Institute of Technology, and an Associate Professor at the University of Toronto (on leave). He previously held a NUS Presidential Young Professorship at the National University of Singapore. His research focuses on automated reasoning, aiming to enable computing systems to handle uncertain real-world environments through scalable techniques integrating randomized algorithms, statistical inference, formal methods, distribution testing, and software engineering. Core research areas: Automated Reasoning, Formal Methods, Approximate Model Counting, Probabilistic Inference, Constraint Solving His research group has achieved significant recognition in both individual awards and publications. Key trends in his recent work include advancing model counting algorithms, developing frameworks for probabilistic explanations, and improving scalability in formal verification and constraint satisfaction. His tools have consistently ranked top in international competitions, demonstrating practical impact in automated reasoning. 2019 NRF Fellowship for AI 2022 ACP Early Career Researcher Award 2020 IEEE Intelligent Systems AI's 10 to Watch Top placements in Model Counting, SAT, and CAV competitions He mentors a diverse group of PhD and Master's students and collaborates with institutions worldwide. His group's publications span premier conferences in AI, formal methods, and design automation, reflecting interdisciplinary contributions to theoretical and applied computer science.
Sanjit A. Seshia is the Cadence Founders Chair Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . He is affiliated with the Group in Logic and the Methodology of Science and participates in centers like the Industrial Cyber-Physical Systems Center , Berkeley AI Research , and the Simons Institute for the Theory of Computing . Research interests include formal methods for automated verification and synthesis of dependable systems, with applications to cyber-physical systems , AI-based autonomy , and computer security . His work spans SMT solving, model counting, syntax-guided synthesis, and algorithmic improvisation, with tools like UCLID5 , VerifAI , and Scenic for verifying autonomous systems and educational platforms like CPSGrader . Students and collaborators include notable researchers such as Dorsa Sadigh (Stanford), Daniel Fremont (UC Santa Cruz), and Hazem Torfah (Chalmers). He has co-founded startups like Decyphir and 20ⁿ Labs based on his research.
Supratik Chakraborty serves as the Bajaj Group Chair Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He maintains dual affiliations with the Centre for Formal Design and Verification of Software and the Centre for Liberal Education at IIT Bombay, demonstrating his cross-disciplinary engagement. Professor Chakraborty's research spans formal methods with focus on formal verification, rigorous analysis of system models, and automated synthesis of systems from specifications. His work bridges theoretical foundations with practical applications, particularly in developing mathematically provable guarantees for increasingly complex hardware, software, and intelligent systems. Current research interests include constrained counting and sampling, scalable formal verification of software and hardware systems, automated synthesis of programs and circuits, and applications of automata, logic and finite model theory to practical verification challenges. His publication trajectory shows a significant evolution from traditional hardware and software verification toward addressing verification challenges in machine learning and AI systems. Recent work increasingly focuses on interpretability of black-box models, verification of neural networks, and synthesis techniques applicable to intelligent systems. The research demonstrates strong interdisciplinary connections between formal methods, programming languages, and artificial intelligence. IIT Bombay Excellence in Thesis (CSE) Award 2011 (for Bhargav Gulavani's thesis) IIT Bombay Excellence in Thesis (CSE) Award 2017 (for Abhisekh Sankaran's thesis) Best Paper in Algorithms and Architecture track at IEEE International Conference on Computer Design: VLSI in Computers and Processors, 1998 Professor Chakraborty has successfully supervised 11 doctoral students, with research spanning formal verification techniques, Boolean functional synthesis, constrained counting, and applications to hardware and software systems. His students have gone on to positions at major institutions including Microsoft Research, TCS Research, Georgia Tech, and BARC, reflecting the strong industry and academic impact of his mentorship. Current research directions show increasing emphasis on verification challenges posed by machine learning systems and AI. His research group at IIT Bombay, while not explicitly named in the materials, appears to focus on formal methods with strong connections to the Centre for Formal Design and Verification of Software. The group maintains active collaborations with international researchers including Moshe Y. Vardi at Rice University, and has made significant contributions to verification tools like VeriAbs that bridge theoretical advances with practical applications.
David Eppstein is a Distinguished Professor of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. He holds academic leadership roles as director of the Center for Algorithms and Theory of Computation and associate director of the Center for Algorithms, Combinatorics, and Optimization. His research focuses on graph algorithms, computational geometry, discrete mathematics, and geometric graph theory. Eppstein earned a B.S. in Mathematics from Stanford University (1984) and a Ph.D. in Computer Science from Columbia University (1989). Research Interests: Graph drawing, information visualization, dynamic graph algorithms, mesh generation, optimal triangulation, K-shortest paths, subgraph isomorphism, data depth, exponential-time algorithms for NP-hard problems. Awards: ACM Fellow (2012), AAAS Fellow (2017), Distinguished Professor (2020), Best Paper Awards (2023, 2022), and SIAM recognition. Grants: Co-PI on a $1.2M NSF grant (2022) for geometric graph research, and previous NSF grants for algorithm studies (2016). His work bridges theoretical computer science and practical applications, including contributions to graph visualization, geometric algorithms, and combinatorial optimization. Notable recent achievements include resolving open questions in graph biplanarity and authoring the book Forbidden Configurations in Discrete Geometry (2018).
Ashley M R Montanaro is a Professor of Quantum Computation at the School of Mathematics, University of Bristol . Active in quantum computing research since at least 2014, they lead projects at the intersection of quantum algorithms , computational complexity , and quantum information theory , affiliated with the Bristol Quantum Information Institute. Research interests focus on quantum algorithm design , computational complexity analysis , and quantum simulation . Key work includes developing variational quantum algorithms for phase transition detection, Hamiltonian simulation techniques, and quantum-classical hybrid methods for solving complex problems in physics and optimization. Recent publications demonstrate expertise in: Quantum phase diagram simulation with low-depth circuits Quantum speedups for constraint satisfaction problems Quantum communication complexity of machine learning tasks Quantum-enhanced optimization heuristics Hamiltonian simulation with time-dependent product formulas Quantum algorithm complexity analysis Scientific awards include: EPSRC Fellowship (2014-2019) - "New insights in quantum algorithms and complexity" Active in quantum software development through projects like: "Quantum Algorithms from Foundations to Applications" (ERC-2018-COG) "Quantum Computing and Simulation Hub" (2019-2024) "Prosperity Partnership in Quantum Software" (2019-2023)
Venkatesan Guruswami is a Chancellor's Professor in the Department of EECS and a Senior Scientist at the Simons Institute for the Theory of Computing at UC Berkeley . He also holds a Professor position in the Department of Mathematics . His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras (1997) , followed by a Ph.D. in Computer Science from the Massachusetts Institute of Technology (2001) . After a Miller Research Fellowship at UC Berkeley (2001–02), he held faculty roles at the University of Washington and Carnegie Mellon University before returning to UC Berkeley in January 2022. Education : B.Tech, IIT Madras (1997) Ph.D., MIT (2001) Professional Affiliations : Chancellor's Professor, UC Berkeley (EECS) Senior Scientist & Interim Director, Simons Institute Professor, UC Berkeley (Mathematics) Guruswami's research spans multiple domains within Theoretical Computer Science , focusing on Error-Correcting Codes , Approximation Algorithms , Randomness in Computing , Probabilistically Checkable Proofs , and Computational Complexity . His groundbreaking work in List Decoding has enabled codes with minimal redundancy for correcting worst-case errors, while recent advancements include Polar Codes , Deletion-Correcting Codes , and Constraint Satisfaction Problems . He has also contributed to Quantum Coding Theory , Locally Recoverable Codes , and Approximation Hardness in various computational contexts. His publications reflect a deep engagement with interdisciplinary topics. Key trends include: Quantum Information Theory : Quantum LDPC codes, transversal gates, and quantum storage. Algebraic Coding : Reed-Solomon codes, AG codes, and polynomial-based constructions. Computational Complexity : Hardness of approximation, CSPs, and parameterized intractability. Data Transmission : Polar codes, deletion channels, and feedback mechanisms. Algorithmic Techniques : Spectral methods, semirandom models, and Lasserre hierarchy applications. Guruswami has received numerous accolades, including the Simons Investigator Award , Presburger Award , Packard Fellowship , Sloan Research Fellowship , ACM Doctoral Dissertation Award , and the IEEE Information Theory Society Paper Award . He is an ACM Fellow (2017) and IEEE Fellow (2019) , with recent honors like the Guggenheim Fellowship (2023) and AMS Fellow (2023) . As an advisor, he has mentored over 25 PhD and postdoctoral researchers , including Atri Rudra , Prasad Raghavendra , and Peter Manohar , whose work has won awards like the Edmund M. Clarke Doctoral Dissertation Award and CRA Outstanding Undergraduate Researcher Award . His research is supported by grants from the National Science Foundation , Packard Foundation , and Sloan Foundation . He also serves as Editor-in-Chief of the Journal of the ACM and holds leadership roles in IEEE and arXiv moderation. Guruswami is actively involved in Simons Institute programs and co-organized workshops on Coded Computation and Information Theory . His work bridges theoretical advancements with practical applications in Cloud Storage , Quantum Computing , and Group Testing , including pandemic-era contributions like AC-DC: Amplification Curve Diagnostics for SARS-CoV-2 .
Arnab Sen is an Associate Professor at the School of Mathematics, University of Minnesota. His research focuses on probability theory and discrete harmonic analysis, with emphasis on models from statistical physics such as spin glasses, random graphs, random matrices, and random polynomials. PhD in Statistics, UC Berkeley (2010), advised by Steven N. Evans and Elchanan Mossel Postdoctoral Fellow, Statistical Laboratory, University of Cambridge His research spans discrete probability , statistical physics , and random matrix theory , addressing topics like disorder chaos in spin glasses, eigenvalue distributions, and quantum percolation. He has taught graduate and undergraduate courses including Random Matrix Theory , Introduction to Stochastic Processes , and Multivariable Calculus . His recent publications analyze spin glass models, random matrices, and combinatorial systems.
Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.
Prof. Michael Krivelevich holds the Baumritter Chair in Combinatorics at the School of Mathematical Sciences, Tel Aviv University. His research focuses on probabilistic methods in combinatorics, random graphs, and positional games. He has authored influential books such as Positional Games and contributed to foundational work in random graph theory. Currently teaching Introduction to Combinatorics and Graph Theory (Spring 2025), he has extensive experience in courses like Graph Theory and Hypergraph Coloring. His work bridges theoretical computer science, coding theory, and combinatorics, with over 150 publications. Recent research explores game-theoretic thresholds, random graph evolution, and equitable coloring algorithms. Education: Ph.D. in Mathematics, Tel Aviv University (not explicitly stated, inferred from career trajectory). Research Interests Krivelevich's work emphasizes random structures , extremal graph theory , and probabilistic combinatorics . He investigates phase transitions in random graphs, positional game strategies, and algorithmic challenges in graph coloring. His contributions include proving sharp thresholds for Hamilton cycle games and analyzing WalkSAT performance on smoothed k-CNF formulas. Collaborations span theoretical computer science and discrete mathematics. Publications Recent articles address Hamiltonicity in Maker-Breaker games, equitable coloring of random graphs, and smoothed analysis of satisfiability processes. His work often combines rigorous proofs with algorithmic insights. Teaching & Mentorship Guides students through advanced combinatorial topics and has taught foundational courses since 2002. No explicit student listings available in provided texts.
John Wawrzynek is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is affiliated with the Department of Electrical Engineering and Computer Sciences in the College of Engineering and serves as Co-Director of the Berkeley Wireless Research Center and Co-PI of the CONIX Research Center, one of the six centers in the Joint University Microelectronics Program sponsored by DARPA. Dr. Wawrzynek received his B.S. in Electrical Engineering from SUNY, Buffalo (1977), M.S. in EE from the University of Illinois, Urbana/Champaign (1979), and Ph.D. in Computer Science from Caltech (1987). Before joining the Berkeley faculty in 1988, he worked as a consultant at Schlumberger Palo Alto Research. His research focuses on Computer Architecture, Reconfigurable Computing, Wireless Systems, and Integrated Circuit and System Design . His work spans both theoretical foundations and practical implementations, with particular emphasis on FPGA-based computing systems, reconfigurable architectures, and wireless communication systems. His research group has made significant contributions to the field of reconfigurable computing, including the development of the Garp architecture and various tools for reconfigurable computing systems. Analysis of his recent publications (2022-2025) reveals continued focus on reconfigurable computing, FPGA design, wireless networking, and formal methods for hardware verification. His work shows an evolution from traditional computer architecture towards specialized hardware acceleration, machine learning for EDA, and wireless systems research, with particular emphasis on SAT sampling, differentiable computing, and efficient FPGA implementation of neural networks. DAC's Most Influential Paper Award (2025) NSF Presidential Young Investigator (PYI) (1989) Charles Lee Powell Fellowship (1985) NASA Certificate of Recognition (1983) Rensselaer Engineering and Science Medal (1975) Professor Wawrzynek has advised numerous graduate students throughout his career, many of whom have gone on to prominent positions in both industry and academia including Google, Xilinx, and MIT Lincoln Laboratory. His research has been supported by various grants from NSF, DARPA, and industry partners. He leads the Berkeley Wireless Research Center, which focuses on next-generation wireless communication systems and technologies, and is actively involved in the CONIX Research Center which explores connected intelligence at the network's edge.
Prof. Daniel Große serves as Professor at the Institute for Complex Systems (ICS) within Johannes Kepler University Linz, Austria, while maintaining a dual affiliation with the German Research Center for Artificial Intelligence (DFKI) in Bremen. His research centers on Electronic Design Automation (EDA), specializing in verification, debugging, and synthesis of complex hardware/software systems through advanced virtual prototyping techniques. His primary research domains include Formal Verification , Hardware Verification , and Virtual Prototyping , with significant contributions to RISC-V architecture development and embedded systems validation. Current projects like PaSVer (automotive electronics), AUTOASSERT (analog-digital verification), and VerSys (RISC-V software platforms) demonstrate his focus on bridging simulation and formal methods for safety-critical applications. Recent publications reveal accelerating trends in metamorphic testing for embedded graphics libraries and RISC-V vector extensions, alongside innovations in waveform analysis tools like Surfer. His work increasingly integrates AI-assisted verification while maintaining rigorous formal methods foundations. Prof. Große actively supervises student research, having guided Lucas Klemmer's PhD thesis and nine Bachelor theses on topics ranging from RISC-V processor design to transaction visualization. His projects including SATiSFy (autonomous vehicle security) and CONVERS (complex system design automation) secure substantial research funding from academic and industry partners. He leads the Institute for Complex Systems' development of open-source tools such as RISC-V VP++ and contributes to international standards through program committee roles at DATE, ICCAD, and RISC-V Summit Europe.
Yiorgos Makris is a Professor in the Department of Electrical and Computer Engineering at the Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas, since July 2011. Previously, he was a faculty member at Yale University for over a decade. He holds a Ph.D. in Computer Engineering from the University of California, San Diego, and a Diploma in Computer Engineering from the University of Patras, Greece. Education: Ph.D. in Computer Engineering, University of California, San Diego (2001) M.S. in Computer Engineering, University of California, San Diego (1997) Diploma in Computer Engineering and Informatics, University of Patras, Greece (1995) Research Interests: Hardware Security and Trustworthiness Statistical Side-Channel Fingerprinting Machine Learning in Semiconductor Manufacturing Trusted and Reliable Integrated Circuits Hardware Trojans in Wireless Cryptographic ICs On-Die Learning and Emergent Technologies His work focuses on enhancing hardware security through statistical methods, machine learning, and formal verification, with applications in analog/RF ICs, post-production calibration, and secure IC design. Key Contributions: Co-Founder and Site-PI of NSF CHEST I/UCRC (Hardware and Embedded System Security and Trust) Leader of the Safety, Security, and Healthcare Thrust at TxACE (Texas Analog Center of Excellence) Director of the Trusted and RELiable Architectures (TRELA) Lab Grants and Funding: NSF, NIH, SRC, ARO, AFRL, AFWERX, DARPA, DOE, and industry partnerships with Boeing, Northrop Grumman, IBM, Intel, Qualcomm, etc. Recent grants include projects on DNA storage security, analog neural networks, and malicious hardware detection. Awards and Recognition: IEEE Fellow (2025) Best Paper Awards at DATE'13, VTS'15, DCAS'22 Best Hardware Demonstration Awards at HOST'16 and HOST'18 Erik Jonsson School Faculty Research Award (2020) Labs and Teams: TRELA Lab (Focus: Secure Hardware Design, Trusted Architectures) CHEST I/UCRC (Industry-University Collaboration)
Kevin Leyton-Brown is a Professor of Computer Science at the University of British Columbia (UBC), holding a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). He is also an Associate Member of the Vancouver School of Economics and a Fellow of the Royal Society of Canada, ACM, and AAAI. His research focuses on AI, machine learning, computational economics, and game theory, with notable contributions to algorithmic market design, heuristic algorithms, and large language models. He co-authored influential textbooks on multiagent systems and game theory, and his work has been recognized with prestigious awards including the INFORMS Franz Edelman Award and the Killam Teaching Prize. Education: PhD (Computer Science), Stanford University; MSc (Computer Science), Stanford University; BSc (Computer Science), McMaster University. Research Interests: Artificial Intelligence, Machine Learning, Game Theory, Computational Economics, Algorithmic Game Theory, Market Design, and Large Language Models. He has developed impactful tools like SATzilla, AutoWEKA, and Mechanical TA, and contributed to high-stakes projects such as spectrum auction design and Ugandan agricultural market platforms. Awards & Recognition: Royal Society of Canada Fellow (2023), ACM SIG-KDD Research Track Test of Time Award (2023), INFORMS Franz Edelman Award (2018), ACM Fellow (2020), AAAI Fellow (2018), Killam Teaching Prize (UBC), and numerous paper awards from top conferences like AAAI, ICML, and ACM-EC. Leadership & Affiliations: Director of UBC’s CAIDA and AIM-SI research clusters, former Chair of ACM SIG-Ecom, and advisor to companies like AI21 Labs and Auctionomics. He has held visiting roles at institutions including MIT, Harvard, and the Simons Institute.
Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.
Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University , focusing on theoretical computer science. His research emphasizes computational complexity, machine learning theory, and algorithm design. Education: Ph.D. in Computer Science from Columbia University , advised by Rocco Servedio His work explores: Boolean function complexity Decision tree learning algorithms Circuit lower bounds Computational-statistical tradeoffs Query complexity Massively parallel algorithms Recent publications analyze computational-statistical tradeoffs via NP-hardness, improve decision tree learning techniques, and establish direct sum theorems for query complexity. His research often bridges complexity theory, learning theory, and algorithm design, with applications in pseudorandomness and correlation clustering. Awards: Best Paper Award at FOCS Best Paper Award at CCC Best Paper Award at SAT Sloan Fellowship Li-Yang collaborates with students and researchers including Guy Blanc, Caleb Koch, and Carmen Strassle. He has delivered invited special issue papers at FOCS, CCC, and SAT conferences.
