Find Academic Supervisors

• Formal Methods
• Model Checking
• Satisfiability (SAT) Solving
• +4 more

Tommi Junttila serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, Finland, where he conducts cutting-edge research at the intersection of formal methods and computational logic. His academic profile demonstrates sustained contributions to theoretical computer science with practical applications in system verification and blockchain technology. His research program centers on advancing formal verification techniques, with core expertise in: Symmetry reduction algorithms for state space explosion SAT/SMT solving with specialized parity and XOR reasoning Bounded model checking of timed and asynchronous systems Blockchain protocol verification (notably DeFi lending pools) Canonical labeling tools for graph automorphism detection Recent work shows increasing focus on decentralized finance applications while maintaining foundational contributions to solver technology. Analysis of his publication trajectory (2011-2022) reveals consistent innovation in SAT solving methodologies, with symmetry reduction and parity reasoning forming persistent research threads. His 2022 work on DeFi lending pools represents a strategic expansion into blockchain verification, leveraging established formal methods expertise for emerging financial technologies. Tool development (bliss, PySMT) demonstrates commitment to practical research impact. No scientific awards were documented in the source materials. Similarly, no information regarding student supervision, grant funding, or laboratory affiliations was present in the provided text. His independent tool development (including bliss for graph canonical labeling and PySMT for SMT solver interfaces) indicates significant technical leadership within the formal methods community.

• Artificial Intelligence
• Programming Languages
• Formal Methods
• +3 more

Gagandeep Singh is an Assistant Professor in the Department of Computer Science at the University of Illinois. His research focuses on trustworthy AI, programming languages, and formal methods, with a particular emphasis on ensuring safety, robustness, and ethical alignment in machine learning systems. He holds a Ph.D. from ETH Zurich and has received multiple awards, including the ACM SIGPLAN Doctoral Dissertation Award. His work spans neural network verification, adversarial robustness, and federated learning, with applications in security, reliability, and ethical AI. He has contributed to journal editorships, conferences, and has taught courses on formal methods and trustworthy AI systems. Education: Ph.D. in Computer Science, ETH Zurich (2014–2020) Masters in Computer Science, ETH Zurich (2012–2014) Bachelor's in Computer Science and Engineering, IIT Patna (2008–2012) Research Interests: Artificial Intelligence Safety & Ethics Formal Verification of Neural Networks Adversarial Machine Learning Federated Learning Programming Languages & Static Analysis Recent Articles: His most recent work addresses challenges in AI alignment, robustness certification, and federated learning optimization, reflecting a focus on both theoretical foundations and practical applications. Awards & Honors: ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award (2021) ETH Medal for Best Master's Thesis (2014) Teaching & Advising: He has taught courses such as 'Formal Software Development Methods' and 'Trustworthy AI Systems', and his research group explores topics like LLM robustness and certified neural networks.

Aaron PotechinView profile

Associate Professor

• Computational Complexity Theory
• Discrete Mathematics
• Algorithm Design
• +4 more

Aaron Potechin is an Associate Professor in the Department of Computer Science at the University of Chicago. His research focuses on computational complexity theory, discrete mathematics, and the sum-of-squares (SoS) hierarchy. He has contributed to areas such as constraint satisfaction problems (CSPs), space complexity via switching networks, and the analysis of random matrices. Potechin’s work bridges theoretical computer science with mathematical foundations, including algorithm design and logic. He holds an NSF grant for investigating the SoS hierarchy (CCF 2008920) and has been affiliated with the Theoretical Computer Science Group at UChicago. His academic journey includes a postdoctoral fellowship at KTH Royal Institute of Technology and notable awards like the Churchill Scholarship and NSF Graduate Research Fellowship. His research interests span: (1) the SoS hierarchy’s applications in combinatorial optimization, (2) approximability of CSPs, and (3) switching networks for space complexity. Recent work includes lower bounds for non-Gaussian component analysis and densest k-subgraph problems. Education Highlights: Recipient of the Barry M. Goldwater Scholarship (2008), Churchill Scholarship (2008), and NSF Graduate Research Fellowship. Grants: NSF Grant CCF 2008920 for SoS research. Awards: 2010 Machtey Award, 2009 George B. Covington Prize. Potechin teaches graduate courses on the SoS hierarchy, communication complexity, and statistical physics methods, alongside undergraduate courses in discrete mathematics and algorithms. His lab focuses on advancing theoretical computer science through rigorous mathematical frameworks.

Dave TompkinsView profile

Associate Professor

• Computer Science
• Artificial Intelligence
• Algorithms
• +3 more

Dave Tompkins is an Associate Professor in the Department of Computer Science at the University of Waterloo and serves as Associate Director of Undergraduate Studies. He holds a Ph.D. from the University of British Columbia (2010), an M.A.Sc. (2000), and dual B.Sc./B.E.Sc. degrees from the University of Western Ontario (1994/1996). His teaching focus is on the core computer science curriculum, emphasizing foundational courses. His research interests span SAT solving, stochastic local search (SLS) algorithms, algorithm configuration, and image compression techniques. Dr. Tompkins has contributed extensively to the field of SAT solving through publications on methods like Adaptive Novelty+, Dynamic Scoring Functions, and UBCSAT tool development. His work also includes studies on variable selection heuristics and algorithm adaptation strategies. Notably, he has explored warped landscapes in SAT solving and reactive scaling techniques. Earlier research involved bi-level image compression using JBIG2 standards, addressing challenges in lossy and lossless coding. His publication trends reflect a strong emphasis on algorithm design and analysis, particularly in combinatorial optimization and constraint satisfaction problems. While no scientific awards are explicitly listed, his contributions to SLS methodologies and SAT solving have likely impacted the field significantly. No grants or advising records are documented in the provided materials.

• Algorithms
• Graph Theory
• Computational Complexity
• +2 more

Manuel Sorge is a Research Fellow in the Faculty of Informatics at Vienna University of Technology, affiliated with the Algorithms and Complexity group (E192-01). His research focuses on parameterized algorithms, graph theory, and computational complexity, with particular emphasis on graph modification problems, clustering algorithms, and combinatorial optimization. His work spans several areas including graph drawing, algorithmic game theory, and machine learning. Research interests include: Parameterized complexity and kernelization Graph algorithms and combinatorial optimization Algorithmic graph theory and discrete mathematics Computational geometry and graph drawing Supervises master's students in algorithm design and complexity theory. Current projects include Parameterized Graph Drawing (2023–2027).

Phokion KolaitisView profile

Professor

• Principles of database systems
• Computational complexity
• Logic in computer science

Phokion Kolaitis is a Professor of Computer Science at the University of California Santa Cruz (UC Santa Cruz) and a Principal Research Staff Member (part-time) at the IBM Almaden Research Center. He has held leadership roles as Chair of UC Santa Cruz's Computer Science Department (1997-2001, 2014-2015) and Senior Manager at IBM's Computer Science Principles and Methodologies Department (2004-2008). His research bridges database systems, computational complexity, and logic in computer science, with significant contributions to schema mappings, data exchange, and constraint satisfaction problems. His work spans foundational studies in database theory, logic, and algorithms, influencing both academic research and industrial applications. Kolaitis has received numerous accolades, including two ACM PODS Alberto Mendelzon Test-of-Time Awards, IBM Research Division Outstanding Innovation and Technical Achievement Awards, and Best Paper Awards at top conferences like ICDT and FCT. He is a Fellow of AAAS and ACM, a Foreign Member of the Finnish Academy of Science and Letters, and holds an Honorary Doctorate from the University of Athens, Greece. 2017 Best Paper Award (FCT 2017) 2015 IBM Outstanding Innovation Award 2014 Honorary Doctorate (University of Athens) 2008 IBM Outstanding Technical Achievement Award Fellow of AAAS and ACM Foreign Member, Finnish Academy of Science Kolaitis collaborates extensively, co-authoring with researchers like D. Burdick, R. Fagin, and M.Y. Vardi. At UC Santa Cruz, he advises on database systems and logic, while his part-time role at IBM focuses on theoretical advancements in data management and complexity.

• Constraint Programming
• Graph Algorithms
• Combinatorial Optimization
• +2 more

Dr. Patrick Prosser is an Honorary Lecturer in the School of Computing Science at the University of Glasgow. His research focuses on constraint programming, graph algorithms, and combinatorial optimization, particularly addressing challenging problems like subgraph isomorphism and maximum clique detection. Recent work develops specialized solvers for graph problems and investigates empirical hardness in optimization. He has created novel approaches for parallel constraint solving and solution-biased search techniques. Dr. Prosser's Glasgow Subgraph Solver represents a significant contribution to handling difficult variants of subgraph isomorphism problems. Publications explore parallel branch-and-bound methods, the impact of search tree structure on algorithm efficiency, and symmetry breaking techniques in graph representation. His work has applications in graph databases and combinatorial problem solving.

Rico ZenklusenView profile

Professor

• Combinatorial Optimization
• Theoretical Computer Science
• Network Optimization
• +3 more

Prof. Rico Zenklusen is a Full Professor at the Department of Mathematics and Deputy Head of the Institute for Operations Research at ETH Zurich. He previously held positions at Johns Hopkins University and conducted postdoctoral research at MIT and EPFL. His research focuses on Combinatorial Optimization, including algorithm design for complex optimization problems using structures like matroids, submodular functions, and polyhedral methods, with applications in Theoretical Computer Science and Graph Theory. Education: PhD in Mathematics from ETH Zurich Master's degree in Mathematics from EPFL Research Interests: Combinatorial Optimization, Network Design, Submodular Maximization, Matroid Theory, and Applications in Operations Research. His work emphasizes efficient algorithms for optimization problems with real-world applications, such as train engine scheduling, operating room management, and rolling stock scheduling for railways. Grants & Awards: ERC Consolidator Grant (ICOPT) Swiss National Science Foundation Support Students & Advising: Advises PhD and master’s students in optimization and related fields. Current and past students include Adam Kurpisz, Etienne Bamas, and Vera Traub. Visit the group page for project opportunities. Labs & Collaborations: Leads the Zenklusen Group, collaborating on interdisciplinary projects with industries like BLS Cargo (train engine optimization) and the SBB (rolling stock scheduling). Active in ETH Zurich's AI Center for complex system research.

• Computer Science
• Algorithms
• Constraint Satisfaction Problems (CSP)
• +4 more

Dr. Oliver Kullmann is an Associate Professor in the Department of Computer Science at Swansea University, Faculty of Science and Engineering. His work focuses on theoretical computer science, particularly in satisfiability (SAT) solving, constraint satisfaction problems, and algorithm design. He has contributed to advancements in SAT-solving techniques like Cube-and-Conquer and has published extensively on topics such as quantified Boolean formulas, minimal unsatisfiable subformulas (MUSes), and parallel computing. Research Interests: Dr. Kullmann’s research spans algorithmic approaches to SAT and CSP, including the development of efficient solving methods, formal verification, and parallel computing optimizations. His work emphasizes practical applications of theoretical results, such as the Boolean Pythagorean Triples problem and GPU-accelerated algorithms for combinatorial problems like the N-Queens puzzle. Publications highlight his contributions to SAT theory and applications, with a focus on foundational aspects like autarkies, clause-set minimization, and hypergraph-based techniques. His work also integrates interdisciplinary methods, such as leveraging linear algebra and graph theory for SAT decision problems. Dr. Kullmann supervises PhD and MRes students in areas including SAT solving algorithms, general-purpose programming languages, and verification tools. He teaches modules such as Algorithms (CS-270) and the Logic and Computation Project (CS-700). His research platform, the OKlibrary, supports holistic SAT-solving research.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Henry A. Kautz is a Professor at the University of Rochester, New York, USA, with a distinguished career in artificial intelligence, machine learning, and social media analysis. His research spans interdisciplinary applications in healthcare informatics, urban mobility, and computational modeling of human behavior. Research Focus : Integrates AI with public health surveillance (e.g., mental health monitoring via search engine logs), social media analysis for disease transmission modeling, and multimodal data interpretation for activity recognition. Key Contributions : Pioneered nEmesis for foodborne illness detection via social media, developed Markov logic frameworks for complex event recognition, and advanced privacy-preserving systems for cognitive disabilities support. His recent work explores hardware-accelerated SAT solving, explainable AI for deep learning, and longitudinal behavioral analysis during the COVID-19 pandemic. Publications emphasize practical AI applications in health, urban planning, and computational epidemiology.

• Proof Complexity
• Computational Complexity
• Algorithmics
• +5 more

Ilario Bonacina is a Lecturer at the Department of Computer Science within the College of Computer Science of Barcelona (FIB) at the Polytechnic University of Catalonia . His research focuses on Proof Complexity , Computational Complexity , and Algorithmics , particularly in Polynomial Calculus and MaxSAT Optimization . Education: Laurea in Mathematics, Laurea Specialistica in Mathematics, and Doctorate from UPC. Research Interests include proof systems (Sherali-Adams, Nullstellensatz), algebraic methods, and theoretical computer science. Recent publications analyze complexity bounds and optimization algorithms. Scientific Awards include recognition for Best Italian PhD Theses in Theoretical Computer Science. He participates in competitive R+D+i projects like the State Plan for Scientific Research and Innovation.

• Quantum Computing
• Geospatial Information Processing
• Text Mining

Siya BAO is an Assistant Professor at Waseda University's Faculty of Science and Engineering, focusing on quantum computing applications in combinatorial optimization and geospatial information processing. Currently affiliated with the Green Computing Systems Research Organization, her work bridges theoretical computer science with practical implementations in travel planning, indoor localization, and financial forecasting. Key research interests include: Quantum Computing Geospatial Information Processing Text Mining Based on 15 recent publications, her research trajectory demonstrates: Quantum annealing solutions for multi-day trip planning Machine learning applications in financial time series analysis Advancements in smartphone-based pedestrian dead reckoning Innovations in QUBO model formulation for constrained optimization Scientific recognition includes: DICOMO 2023 Outstanding Paper Award IEEE ICSC 2020 Best Student Opponent 2017 Telecommunications Advancement Foundation Travel Aid Research activities span: Principal investigator for JSPS-funded geospatial optimization projects (2021-2024, 2024-2027) Contributing author to Springer's 'Machine Learning for Indoor Localization and Navigation' (2023) Extensive conference presentation history including IPSJ-ONE 2024 invited talk on quantum computing applications

Guy BreslerView profile

Associate Professor

• Information Theory
• Statistical Inference
• Computational Complexity
• +2 more

Guy Bresler is Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, with affiliations in LIDS, Theory of Computation, Center for Statistics, FODSI, and IDSS. He researches the interface of information theory, statistics, theoretical computer science, and probability, developing mathematical models to understand computational tractability of high-dimensional inference. His research characterizes relationships between combinatorial structure and computational tractability in inference problems, with applications in statistical physics, random graphs, and learning theory. Recent work establishes fundamental limits in statistical estimation and develops efficient algorithms for high-dimensional problems. Publications demonstrate consistent focus on computational-statistical tradeoffs across random graphs, Markov chains, neural networks, and graphical models. Techniques involve information-theoretic analysis, reduction methods, and phase transition characterization. He advises doctoral students Kiril Bangachev, Alina Harbuzova, Chenghao Guo, and Brice Huang. Previously advised researchers include Enric Boix-Adserà, Dheeraj Nagaraj, and Mina Karzand. He co-organized the Simons Institute program on computational aspects of high-dimensional inference.

Youngtak SohnView profile

Assistant Professor

• Probability Theory
• Mathematical Statistics
• Theoretical Computer Science
• +5 more

Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University since July 2024. Previously, he was a postdoctoral researcher at MIT Mathematics, mentored by Elchanan Mossel and Nike Sun through the NSF/Simons Collaboration on Theoretical Foundations of Deep Learning. He earned his PhD in Statistics from Stanford University (2021) under Amir Dembo and a BSc in Mathematics from Seoul National University (2016). His research focuses on probability theory and its intersections with mathematical statistics, theoretical computer science, and statistical physics. Key areas include high-dimensional statistics, random constraint satisfaction problems, spin glass theory, and the analysis of stochastic networks. Recent work explores phase transitions in estimation problems, universality of max-margin classifiers, and solution geometry of random constraint satisfaction problems. He has taught courses at Brown (APMA 2640) and MIT (Theoretical Computer Science seminar), and served as a teaching assistant for numerous Stanford courses including stochastic processes, statistical inference, and regression analysis.

Manuel BodirskyView profile

Professor

• Constraint Satisfaction Problems (CSP)
• Algebraic Methods in Computer Science
• Computational Logic
• +5 more

Prof. Manuel Bodirsky is a Professor of Algebra and Discrete Structures at Technische Universität Dresden since August 2014. He leads the Algebra and Discrete Structures group within the Faculty of Computer Science and is affiliated with the International Center for Computational Logic (ICCL). His research focuses on constraint satisfaction problems (CSP), algebraic methods in computer science, computational logic, Ramsey theory, model theory, and discrete mathematics. Notable projects include exploring the algebraic tractability of CSPs and applying universal algebra to classify computational complexity. His work frequently intersects with combinatorics, graph theory, and theoretical computer science. Recent publications address advanced topics like temporal CSPs, spectrahedral shadows, and resilience problems using valued CSP frameworks. He maintains active collaborations in computational algebra and logic, contributing to both theoretical foundations and algorithmic applications. Education: Not explicitly listed in provided texts but inferred to include advanced studies in mathematics and computer science. Research interests span foundational areas such as: Constraint satisfaction problem complexity classification Applications of universal algebra to computational problems Model theory and finite structures Combinatorial properties of graphs and tournaments Algorithmic approaches to algebraic and logical systems His recent articles emphasize methodological innovations, including reductions to semidefinite programming, Ramsey-theoretic techniques, and gadget-based transformations. Despite no listed academic awards in the provided data, his prolific publication record reflects significant contributions to theoretical computer science and discrete mathematics. No student advisees or grant details were explicitly mentioned, though his group likely engages in funded research projects given the institutional context.