Find Academic Supervisors

• Automated Reasoning
• Knowledge Representation and Reasoning
• Computational Logic
• +5 more

Uwe Egly is an Associate Professor at the Department of Knowledge-Based Systems, Faculty of Informatics, Technische Universität Wien (TU Wien). His research focuses on automated reasoning, proof theory, knowledge representation, and computational logic, with a strong emphasis on quantified Boolean formulas (QBFs), argumentation frameworks, and applications of AI in engineering. He leads projects funded by the Austrian Science Fund (FWF) and the Vienna Science and Technology Fund (WWTF), including the Boolean project (2011–2019) and FAME (2011–2014). Egly is known for developing QBF solvers like DepQBF and contributing to SAT-solving techniques. He teaches courses such as Abstract Argumentation , Formal Methods in Computer Science , and Quantum Computing . His research interests span proof complexity, satisfiability checking, and AI-driven algorithms for path planning. He has advised numerous students on theses involving quantum algorithms, QBF solver optimizations, and argumentation frameworks. Egly’s work bridges theoretical computer science with practical applications, including contributions to deformation monitoring systems and circuit synthesis using SAT-based methods. He is involved in international workshops and conferences, such as SAT, FMCAD, and Dagstuhl Seminars, and has edited proceedings for events like SAT 2014 . His collaborations include projects on scenario-based testing of UML diagrams and semantics-aware model versioning. Egly’s interdisciplinary approach integrates logic, artificial intelligence, and computational methods to solve complex theoretical and applied problems.

• Theoretical Computer Science
• Proof Complexity
• Automated Reasoning
• +3 more

Leroy Nicholas Chew is a PostDoc Researcher and FWF Projektassistent at the Vienna University of Technology (TU Wien). He is affiliated with the Department of Algorithms and Complexity within the Faculty of Informatics. His roles include contributing to research projects such as QBFPC (2022–2025), Overcoming Intractability in the Knowledge Compilation Map, and REVEAL-AI (2020–2024). These projects reflect his focus on advancing theoretical computer science and automated reasoning methodologies. While specific educational details are not explicitly provided in the text, Leroy Nicholas Chew holds a PhD, as indicated by his role listing. His current position suggests a strong background in computer science and theoretical foundations, consistent with his research activities. His research interests span several key areas in theoretical computer science, including proof complexity, quantified Boolean formulas (QBF), automated reasoning, and knowledge compilation. He explores the hardness of computational problems in logical frameworks, such as analyzing resolution and CDCL proof systems, developing optimal dual proof systems for answer set programming (ASP), and investigating model counting techniques. His work often bridges foundational theory with practical applications in formal verification and algorithm design. Recent publications (2024) highlight advancements in circuits and proofs, model counting, and ASP-QRAT proof systems. Earlier work (2016–2022) addressed QBF resolution calculi, dependency schemes, and certification challenges. These trends underscore his specialization in formal methods and computational logic. No scientific awards are explicitly mentioned in the provided text. In addition to his research, Chew is involved in multiple funded projects. These include the FWF-supported QBFPC (2022–2025), which examines QBF proofs and certificates, and the REVEAL-AI project (2020–2024), focusing on overcoming intractability in knowledge compilation. While specific grant details beyond project funding are not mentioned, his participation underscores his role in collaborative, grant-funded research initiatives. No formal advisees are listed. Chew is part of the Algorithms and Complexity department at TU Wien, collaborating on projects that emphasize proof systems, formal verification, and algorithmic foundations. His work integrates theoretical insights with practical computational methods.

Mahdi ImaniView profile

Assistant Professor

• Machine Learning
• Control Theory
• Learning Theory
• +3 more

Mahdi Imani is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. He holds a PhD in Electrical Engineering from Texas A&M University (2019), and MSc and BSc degrees in Electrical and Mechanical Engineering from the University of Tehran (2014 and 2012, respectively). His research focuses on machine learning, control theory, Bayesian statistics, and signal processing, with applications in gene regulatory networks, network security, and human-AI collaboration. Dr. Imani has received prestigious awards, including the NIH NIBIB Trailblazer Award (2022), the NSF CISE Career Award (2020), and the Outstanding Associate Editor Award from IEEE Transactions on Neural Networks and Learning Systems (2023 and 2024). He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Vehicular Technology, and is a Senior Member of IEEE. His research projects include DARPA-funded work on verified probabilistic reasoning in mixed reality systems, NSF-funded statistical inference methods, and ONR-funded studies on human-AI team synergy. He leads a lab focused on developing scalable Bayesian methods and reinforcement learning techniques for complex systems.

Claudio ChamonView profile

Professor

• Condensed Matter Physics
• Topological Materials
• Quantum Computing
• +3 more

Claudio Chamon is a Professor at Boston University, specializing in condensed matter physics and quantum computing. His research focuses on electron fractionalization in topological systems, quantum spin liquids, and fractonic behavior. He holds a Ph.D. in Theoretical Physics from MIT, along with M.S. and B.S. degrees in Electrical Engineering and Aeronautics/Astronautics, also from MIT. His work bridges theoretical and experimental condensed matter physics, with contributions to topological materials, quantum phase transitions, and quantum information science. Key research areas include topology-driven fractionalization in graphene-like structures, non-Abelian gauge theories, and quantum computing applications such as encrypted operator computing. Chamon has pioneered studies on Majorana zero modes in nanowire networks and braiding non-Abelian anyons in photonic systems. His experimental collaborations aim to realize topological qubits and quantum spin liquids in programmable devices. He has received prestigious awards including the American Physical Society Fellowship, Alfred P. Sloan Fellowship, and NSF CAREER Award. Chamon’s recent work explores quantum circuit complexity, fracton dynamics, and secure computation on encrypted data, leveraging tensor networks and combinatorial symmetries.

David Mix BarringtonView profile

Adjunct Professor

• Computational Complexity
• Boolean Circuits
• Automata Theory
• +3 more

David Mix Barrington is an Adjunct Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His primary research focuses on computational complexity, including boolean circuits, automata, and logic. He has held significant administrative roles, such as Associate Chair for Academics in the College of Information and Computing Sciences, Chief Undergraduate Advisor, and Undergraduate Program Director. He is also a seasoned educator, teaching courses like COMPSCI 250 (discrete mathematics), COMPSCI 501 (formal languages and computability), and graduate seminars on complexity theory. His contributions extend to textbook development, notably Discrete Mathematics: A Foundation for Computer Science . Research Interests: Dr. Barrington’s work bridges theoretical computer science and mathematics, emphasizing foundational topics such as complexity theory, automata theory, and logic. His teaching spans undergraduate and graduate levels, with a focus on rigorous core courses and advanced theory. He has also contributed to interdisciplinary initiatives, including co-authoring the alternate history collaborative For All Nails . Advising & Leadership: Beyond teaching, Dr. Barrington has served in leadership roles to enhance academic programs and student advising at UMass Amherst. He coordinates the CICS Theory Seminar (COMPSCI 891M) and has led efforts to refine curricula and academic policies.

Chris UmansView profile

Professor

• Theoretical Computer Science
• Computational Complexity
• Derandomization
• +4 more

Chris Umans is a Professor of Computer Science in the Computing and Mathematical Sciences department at the California Institute of Technology (Caltech), affiliated with the Theory Group. He earned his Ph.D. from UC Berkeley in 2000 and joined Caltech in 2002 after a postdoc at Microsoft Research. His research focuses on theoretical computer science, particularly computational complexity, including derandomization, algebraic complexity, and matrix multiplication algorithms. Education: Ph.D. in Computer Science, UC Berkeley (2000); Postdoc, Microsoft Research (2000-2002). Research interests span computational complexity, explicit constructions, and hardness of approximation. His work often intersects algebraic methods and group theory to advance algorithm design, such as group-theoretic approaches to matrix multiplication. Professional Activities: Program committee member for FOCS 2024, STOC, SODA, and others. Vice-Chair of SIGACT (2021-24). Editor for Theory of Computing (ToC), ACM Transactions on Computation Theory (TOCT), and Computational Complexity (CC). Member of the ECCC scientific board. Research Trends: Recent articles explore fast matrix multiplication via matrix groups, algebraic problems over finite fields, and generalized DFTs for finite groups. His work bridges theoretical foundations with algorithmic innovations in algebraic structures. Grants & Labs: His research is supported by NSF grants focused on algebraic methods in complexity theory. He advises students in theoretical computer science and has contributed to collaborative projects on computational algebra and combinatorics.

Ciaran McCreeshView profile

Research Fellow

• Graph Theory
• Combinatorial Optimization
• Constraint Programming
• +5 more

Ciaran McCreesh is a Research Fellow in the School of Computing Science at the University of Glasgow. His research focuses on solving hard combinatorial problems in practice, particularly in graph theory and subgraph finding, leveraging symbolic AI techniques like constraint programming and Boolean satisfiability. He explores closing the gap between theoretical worst-case complexity and practical performance through empirical algorithmics and computational experiments. His work also addresses algorithm reliability via proof logging, parallel hardware exploitation (bit-parallelism, multi-core computing), and algorithm engineering to improve solver accessibility. Research interests: Combinatorial optimization, constraint programming, proof logging, parallel computing, algorithm reliability. Developed the Glasgow Subgraph Solver, a constraint programming-based tool for hard subgraph isomorphism problems. Publications span graph computation models, certified solvers, and algorithmic advancements. His work emphasizes practical algorithmic improvements with rigorous validation methods. Supervised students include Matthew McIlree and José Antonio Rodríguez Bacallado.

Sylvain SenéView profile

Professor

• Computer Science
• Theoretical Computer Science
• Discrete Mathematics
• +5 more

Sylvain Sené is a Professor of Computer Science at Aix-Marseille University (AMU), affiliated with the Department of Computer Science and Interactions (DII) and the Computer Science and Systems Laboratory (LIS). He leads the ANR-funded project FANs (Foundations of Automata Networks) and focuses on discrete mathematics, theoretical computer science, and computational properties of automaton networks. His research bridges abstract computational models with applications in biology, particularly gene regulatory networks and cellular reprogramming. Research Interests: Automaton networks, Boolean networks, and cellular automata Computational complexity and dynamical systems Discrete mathematics and theoretical computer science Applications in systems biology and genetic networks Key Projects: Principal investigator of the ANR project FANs (2019–2023), focusing on advancing automata network theory Collaborations with CNRS, Chilean universities, and international institutions Advising & Grants: Directed PhD students including Pacôme Perrotin and Martín Ríos Wilson Secured funding through ANR and other national/international grants Labs & Teams: LIS (Computer Science and Systems Laboratory), collaborating with interdisciplinary teams in biology and mathematics.

Friedrich SlivovskyView profile

Researcher

• Quantified Boolean Formulas
• Propositional Model Counting
• Knowledge Compilation
• +5 more

Friedrich Slivovsky is a researcher at the Institute of Logic and Computation within the Faculty of Informatics at Technische Universität Wien (Vienna University of Technology). His work focuses on theoretical and practical aspects of computational logic, with particular expertise in Quantified Boolean Formulas (QBFs), Propositional Model Counting (#SAT), and Knowledge Compilation. His research interests span the theoretical foundations and practical applications of computational logic. Slivovsky investigates the complexity of logical reasoning problems, develops efficient algorithms for solving them, and creates practical tools that implement these theoretical advances. His work bridges the gap between theoretical computer science and practical applications in areas like hardware verification, artificial intelligence, and electronic design automation. Analysis of his publication trends reveals a consistent focus on QBF solving techniques, with increasing emphasis on circuit minimization, proof complexity, and practical solver engineering. His recent work (2023-2024) shows a strong focus on circuit minimization techniques, combining QBF and SAT approaches to solve complex optimization problems in hardware design. Earlier work (2019-2021) emphasized dependency schemes, certification methods, and theoretical foundations of QBF solving. Slivovsky leads several significant software projects that have become important tools in the computational logic community: Qute : A dependency learning QBF solver with GitHub repository showing active development (latest commit December 2024) Unique : A preprocessor for (D)QBF that computes unique Skolem and Herbrand functions Pedant : A certifying DQBF solver These projects demonstrate his commitment to translating theoretical advances into practical tools that benefit the broader research community.

Ian DurhamView profile

Professor

• Quantum Physics
• Relativity
• Thermodynamics
• +3 more

Ian Durham is a Professor of Physics at Saint Anselm College, specializing in foundational questions in quantum mechanics, relativity, and the philosophy of science. He holds a Ph.D. in Mathematics from the University of St. Andrews, an MSc in Applied Physics from Johns Hopkins University, and a BSc in Mechanical Engineering from the University of Buffalo. His research explores quantum information theory, relativistic quantum systems, and the interplay between thermodynamics and quantum mechanics. Dr. Durham is an active member of professional societies including the American Physical Society (APS), where he served as Treasurer of the Topical Group on Quantum Information (GQI) from 2012-2014, and the Royal Astronomical Society (RAS). He co-founded the APS GQI's publication The Quantum Times . His recent work addresses topics like quantum superposition in consciousness, observer-independent frameworks in relativity, and the philosophical implications of quantum theory. His awards include election to the Foundational Questions Institute (FQXi) and recognition for editorial contributions to quantum research. Durham's publications span theoretical physics, philosophy of science, and educational strategies for teaching quantum concepts. Notable themes include quantum contextuality, entanglement's role in thermodynamics, and historical interpretations of physics.

Jin-Yi CaiView profile

Professor

• Computational Complexity Theory
• Theoretical Computer Science
• Algorithms
• +3 more

Jin-Yi Cai is a Professor in the Computer Sciences Department at the University of Wisconsin - Madison. He holds the Rajiv & Ritu Batra Chair in Computer Science and has authored over 100 research papers. His academic journey includes a Ph.D. from Cornell University in 1986 and faculty positions at Yale, Princeton, and SUNY Buffalo. Education: Fudan University (class of 77), Cornell University (Ph.D., 1986) Previous Institutions: Yale University (1986-1989), Princeton University (1989-1993), SUNY Buffalo (1993-2000) His research focuses on computational complexity theory, algorithms, and theoretical computer science. He has explored lattice problems, graph homomorphisms, and holographic algorithms. The 15 most recent publications highlight advancements in constraint satisfaction problems, planar graphs, and dichotomy theorems, reflecting his deep engagement with complexity classification. Scientific Awards: Presidential Young Investigator Award (1990) Alfred P. Sloan Fellowship (1994) Guggenheim Fellowship (1998) Morningside Silver Medal (2004) Humboldt Research Award ACM Fellow AAAS Fellow AMS Fellow Foreign Member of Academia Europaea Gödel Prize Fulkerson Prize He serves as Editor for several journals, including the Journal of Computer and Systems Sciences and Computational Complexity . His work bridges foundational theoretical research with practical algorithmic applications, influencing both academia and industry.

• Approximation Algorithms
• Probabilistically Checkable Proofs
• Hardness of Approximation
• +3 more

Amey Bhangale is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, Riverside. Prior to this, he held positions as a post-doctoral fellow at the Weizmann Institute of Science under Irit Dinur and a research fellowship at the Simons Institute. His research focuses on Approximation Algorithms , Probabilistically Checkable Proofs , Hardness of Approximation , and Analysis of Boolean Functions . Research Trends His recent work explores inapproximability bounds for constraint satisfaction problems, parallel repetition theorems, and additive combinatorics in finite fields. Notable collaborations include Subhash Khot, Dor Minzer, and Yang P. Liu. Teaching CS219: Advanced Algorithms (2025) CS141: Intermediate Data Structures and Algorithms (2024) CS218: Design and Analysis of Algorithms (2023) CS215: Theory of Computations (2021-2023)

Dan MikulincerView profile

Assistant Professor

• High-Dimensional Geometry
• Probability Theory
• Statistics
• +10 more

Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.

• Computer Engineering
• Digital Logic Design
• Signal Processing
• +3 more

Dragan S. Janković is a Full Professor at the Faculty of Electronic Engineering, University of Niš, specializing in Computer Science. His academic career spans over three decades with continuous research contributions since his doctorate in 2001. His educational background includes: Diploma in Electrical Engineering and Computing (1991) Master's in Electrical Engineering and Computing (1995) PhD in Electrical Engineering and Computing (2001) All obtained at the Faculty of Electronic Engineering, University of Niš. Professor Janković's research focuses on advanced computational methods in digital systems, with particular expertise in multi-valued logic, spectral transforms, and decision diagram techniques. His work bridges theoretical computer science with practical circuit design applications, emphasizing optimization algorithms for logic synthesis and signal processing. Current investigations involve recursive algorithm development for polarity matrices and wavelet-based circuit minimization. His publication record shows consistent contributions to high-impact journals, with research trends indicating sustained focus on computational logic structures and their engineering applications over the past 25 years. Key methodological contributions include novel tabular techniques for multi-valued logic and efficient implementations of decision diagram packages. Professor Janković actively participates in research projects with 5 national and 3 international collaborations currently underway, demonstrating ongoing engagement in the academic community.

Mitsunori OgiharaView profile

Professor

• Bioinformatics
• Computer Science
• Computer Theory
• +4 more

Mitsunori Ogihara is a Professor of Computer Science at the University of Miami's College of Arts and Sciences, with secondary appointments in Electrical and Computer Engineering (ECE), Molecular and Microbiology (MMI), College of Arts (CoA), and Human Genetics and Genomics (HGG). He serves as Director of Workforce Development and Education at IDSC and Director of Graduate Studies for the Computer Science department. Education Ph.D. in Computer Science, Tokyo Institute of Technology (1993) Research Interests Professor Ogihara maintains a diverse research portfolio spanning multiple domains of computer science. His work demonstrates significant contributions to: Theoretical Computer Science - with publications on computational complexity and dynamical systems Bioinformatics and Medical Data Science - pioneering work on multi-omics data integration for Type 1 Diabetes biomarker discovery Data Mining and Machine Learning - developing novel algorithms for knowledge extraction from complex datasets Music Information Retrieval - editing the seminal book "Music Data Mining" and applying computational methods to music analysis Digital Humanities - advancing NLP techniques for historical Japanese text processing Publication Trends Professor Ogihara's recent publications reveal a strong interdisciplinary trajectory , increasingly connecting computer science with biomedical applications and cultural analytics. His work on computational data augmentation for small biomedical datasets represents a significant methodological innovation with potential clinical impact. Simultaneously, he continues to advance foundational work in computational complexity while expanding applications in entertainment analytics and historical text processing, demonstrating remarkable intellectual breadth. Teaching and Mentorship Professor Ogihara has supervised numerous PhD students who have secured positions at leading technology companies (Google, Amazon, IBM) and academic institutions worldwide. His commitment to education extends to textbook authorship, including "Exploring Data Science with R and the Tidyverse" and his upcoming "An Introduction to Theory of Computation," which aim to make complex computational concepts accessible to undergraduate students.