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Jieh HsiangView profile

Professor

• Automated Reasoning
• Programming Language Logic
• Artificial Intelligence
• +5 more

Jieh Hsiang is a Distinguished Professor at National Taiwan University , with affiliations in the Department of Computer Science and Information Engineering, the Digital Archives and Automatic Inference Laboratory, and the Digital Humanities Research Center. He holds concurrent roles at the Institute of Information Science, Academia Sinica, and the Higher Education Research & Development Office, National Taiwan University. Education PhD in Computer Science, University of Illinois at Urbana-Champaign (1979–1982) BS in Mathematics, National Taiwan University (1972–1976) Research Interests Hsiang's work spans automated reasoning , digital libraries , digital humanities , and information retrieval . His research focuses on integrating computational methods with cultural heritage preservation , particularly through tools like DocuSky and databases such as the Taiwan Historical Digital Library . He explores AI applications in patent analysis , historical text mining , and semantic relationships in legal documents . Recent Trends in Publications His recent articles highlight advancements in BERT and GPT-2 fine-tuning for patent classification , LARGE language models for legal automation , and GIS-based analysis of historical archives . Themes include digital preservation , AI-driven legal text analysis , and cross-disciplinary computational tools for humanities scholars. Scientific Awards 2019 Ministry of Science and Technology Distinguished Research Fellow 2009 National Taiwan University Outstanding In-House Service Award 2008 Chinese Library Association Special Contribution Award 2006 IEEE Test-of-Time Award 1997 & 1999 National Science Council Outstanding Research Award 1997 Ministry of Education Outstanding Industrial-Academic Collaboration Award 1998–2001 Founder and First Chair of IFIP WG1.6 Labs and Collaborations Hsiang leads the Digital Archive and Automatic Inference Laboratory , developing platforms like DocuSky for digital humanities, Taiwan Historical Digital Library , and QGIS Cloud Maps for spatial analysis. His team collaborates internationally on projects involving historical document digitization , patent automation , and cross-domain knowledge integration .

Ruben MartinsView profile

Assistant Professor

• Constraint Programming
• Program Synthesis
• Formal Verification
• +3 more

Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.

Diptarka ChakrabortyView profile

Assistant Professor

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +5 more

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

• Machine Learning
• Theoretical Computer Science
• Data Science
• +3 more

Lev Reyzin is a Professor of Mathematics, Statistics, and Computer Science at the University of Illinois Chicago (UIC) and Director of the IDEAL Institute. He specializes in the theory of machine learning, data science, and artificial intelligence, with affiliations to theoretical computer science and mathematical foundations. Prior roles include a Simons Postdoctoral Fellowship at Georgia Tech and an NSF Computing Innovation Fellowship at Yahoo! Research. He earned his Ph.D. from Yale University (NSF doctoral fellowship) and a bachelor’s degree from Princeton University. Research interests focus on algorithmic learning theory, computational complexity, and applications of machine learning to real-world problems. Notable work includes contributions to statistical learning algorithms, adversarial bandits, and graph theory. His research has been funded by NSF grants (e.g., ECCS-2217023, CCF-2307106), DOD programs, and others since 2015. He has received awards at leading conferences (ICML, COLT, AISTATS). Reyzin holds editorial roles, including Editor-in-Chief of Mathematics of Data, Learning, and Intelligence and Chair positions in major conferences like FOCS 2024 and ALT 2017. His leadership in academic organizations highlights his influence in shaping theoretical computer science and machine learning research.

Manuel PenschuckView profile

Research Fellow

• Algorithm Engineering
• Random Graphs
• Parallel and Distributed Computing
• +3 more

Manuel Penschuck is a Research Fellow at the Institute of Computer Science , Goethe University Frankfurt, Germany. His research focuses on algorithm engineering, graph theory, and scalable network generation, with emphasis on parallel computing, I/O-efficient algorithms, and random graph models. He actively contributes to conferences like ESA, SEA, and IPDPS, and has co-authored publications in top venues including LIPIcs , IEEE Transactions , and SIAM . His work includes engineering algorithms for non-linear preferential attachment , parallel shuffling , and hyperbolic graph generation . He has co-organized program committees for ESA, EuroPar, and SEA, and his collaborations span institutions such as MPI-INF, TU Darmstadt, and Australian National University. Recent publications highlight advances in uniform graph sampling, geometric network models, and distributed systems. His research integrates theoretical rigor with practical implementation, addressing challenges in big data and high-performance computing. He is a key contributor to the Networkit toolkit for large-scale network analysis.

Gaurav RattanView profile

Assistant Professor

• Graph Theory
• Machine Learning on Graphs
• Graph Neural Networks
• +5 more

Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.

Hideyuki SuzukiView profile

Professor

• Nonlinear Dynamics
• Hybrid Systems
• Many-Body Dynamics
• +5 more

Hideyuki Suzuki is a Professor at the Department of Information and Physical Sciences , Graduate School of Information Science and Technology , Osaka University , where he has been employed since April 2016. His research spans nonlinear dynamics , hybrid systems , and many-body dynamics , with applications to power systems , brain modeling , and epidemic networks . 2001 : Ph.D. in Mathematical Engineering and Information Physics, University of Tokyo 1998 : M.Eng. in Mathematical Engineering and Information Physics, University of Tokyo 1996 : B.Sc. in Mathematics, University of Tokyo His research interests focus on nonlinear dynamical systems , particularly those with discontinuities or large-scale interactions , such as chaotic billiards , hybrid systems , and spatio-temporal chaos . He explores computational applications in machine learning (e.g., chaotic Boltzmann machines ), epidemiology (e.g., vaccine allocation models ), and power grid stability . The 15 most recent articles highlight his work in photonic computing , nonlinear sampling algorithms , and chaotic dynamics in engineering and biology . Key trends include interdisciplinary applications of nonlinear mathematics to renewable energy , neuroscience , and epidemic spread . Scientific awards include: 2022 Osaka University Prize 2018 JSIAM Best Paper Award for Hamiltonian Monte Carlo (2017) He leads the Nonlinear Mathematics Course laboratory, which accepts graduate and undergraduate students. His team investigates hybrid dynamical systems , chaotic computation , and real-world modeling in fields like traffic dynamics and epidemic networks . Research is supported by grants from JST CREST and ALCA-Next programs.

John FrancoView profile

Professor

• Cybersecurity
• Formal Verification
• Satisfiability Algorithms
• +4 more

Prof. John Franco is a Professor of Computer Science at the University of Cincinnati, serving as Director of the National Center of Academic Excellence in Cyber Operations. His roles include Editor-in-Chief of the Journal on Satisfiability, Boolean Modeling, and Computation , and Vice Chair of the SAT Association. He has held visiting scientist positions at institutions in Germany (FAW Ulm, Universität Paderborn) and spent sabbatical leave at Fort George G. Meade. Education: Ph.D. in Computer Science, Rutgers University, 1981 M.S. in Electrical Engineering, Columbia University, 1971 B.S. in Electrical Engineering, City College of New York, 1969 Research Interests: Franco’s work focuses on satisfiability (SAT) algorithms, formal verification, cybersecurity, and computational complexity. He has pioneered probabilistic analysis of SAT-solving heuristics and contributed to the integration of SAT techniques into network security and formal methods. His research bridges theoretical computer science with practical applications in cyber defense and algorithm optimization. Grants & Funding: Franco has secured over 18 grants from agencies like the NSA, NSF, and ONR. Notable projects include Satisfiability Algorithm Research (NSA grants since 1999), cybersecurity curriculum development (NSA 2018–2022), and Ohio Cyber Range initiatives. He has led or collaborated on federal and state-funded projects totaling millions in funding. Labs/Teams: Directs the National Center of Academic Excellence in Cyber Operations, a collaborative effort with local defense contractors. His work involves partnerships with institutions like Wright State Applied Research Corporation and Riverside Research Institute.

Johannes Peter WallnerView profile

Associate Professor

• Knowledge Representation and Reasoning
• Artificial Intelligence
• Argumentation
• +7 more

Johannes Peter Wallner is an Associate Professor at Graz University of Technology (TU Graz), working in the Institute of Software Engineering and Artificial Intelligence within the Faculty of Computer Science and Biomedical Engineering. He leads the Knowledge Representation and Reasoning (KRR) research group and has previously been a researcher at TU Wien's DBAI group and the Constraint Reasoning and Optimization group at the University of Helsinki. Dr. Wallner's research focuses on knowledge representation and reasoning, artificial intelligence, argumentation, abduction, belief change, inconsistency handling and measurement, computational social choice, computational complexity, Boolean satisfiability, and answer set programming. His work bridges theoretical foundations with practical applications, particularly in developing computational models for argumentation systems. He has made significant contributions to structured argumentation frameworks, including assumption-based argumentation and ASPIC+. His recent publications demonstrate a strong trend toward advancing algorithmic approaches to probabilistic argumentation, abstraction techniques in argumentation systems, and applications of argumentation in domains like healthcare. He has been particularly active in exploring the computational complexity of various argumentation semantics and developing efficient algorithms for reasoning tasks. Dr. Wallner has received multiple prestigious awards including being selected for the IJCAI 2024 Early Career Track (only 12 researchers globally selected), being named a Top Scholar by ScholarGPS in 2024 (top 0.5% worldwide in AI), and receiving the AI 2000 Most Influential Scholar Honorable Mention in Knowledge Engineering in 2021 and 2022. As Principal Investigator, Dr. Wallner has secured significant research funding from the Austrian Science Fund (FWF), including two major projects: "A Novel Computational Workflow for Argumentation in AI" (grant P 35632, 358,848 €) and "Extending Belief Change to Advance Dynamics in Argumentation" (grant P30168-N31, 353,438 €). He is highly active in the academic community, serving on program committees for major AI conferences including AAAI, IJCAI, KR, and ECAI, and was a member of the Program Committee Board of IJCAI (2022-2024). He leads the Knowledge Representation and Reasoning (KRR) research group at TU Graz, which develops both theoretical foundations and practical implementations for computational argumentation systems. The group has contributed to several software systems including CEGARTIX (a SAT-based argumentation system), Vispartix (visualization of argumentation frameworks), ADFsys (an ASP-based argumentation system for abstract dialectical frameworks), and others that implement various argumentation frameworks.

• Formal Methods
• Satisfiability (SAT)
• Satisfiability Modulo Theories (SMT)
• +11 more

Enric Rodríguez Carbonell is a faculty member at the Department of Computer Sciences within the Faculty of Computer Science at Universitat Politècnica de Catalunya (UPC). He is a key member of the LOGPROG - Lògica i Programació research group, focusing on formal methods, automated reasoning, and combinatorial optimization. Research Interests: His work centers on satisfiability (SAT), satisfiability modulo theories (SMT), and their applications in program verification, constraint solving, and optimization. He investigates techniques such as conflict-driven learning, invariant generation, and Max-SMT for proving termination and safety. His research bridges theoretical foundations with practical applications in software analysis and industrial problem-solving. Publication Trends: His recent publications (2020–2024) show a sustained focus on enhancing SAT and SMT solvers, particularly in pseudo-Boolean reasoning, integer linear programming, and multi-conflict analysis. Earlier works established contributions in non-linear arithmetic, termination proofs, and efficient encodings for cardinality constraints, reflecting a long-term commitment to foundational and applied aspects of automated reasoning. Scientific Awards: Best student paper award at SAT 2024 Advising and Grants: Rodríguez Carbonell has contributed to multiple competitive R+D+i projects under Spain’s State Research Plans, indicating active grant involvement. He has co-authored doctoral theses and educational initiatives like Jutge.org, demonstrating engagement in academic supervision and pedagogical innovation. Labs and Teams: He is a core researcher in the LOGPROG group at UPC, which specializes in logic and programming, with strong collaborations across formal methods, verification, and constraint technologies.

• Formal Verification
• Probabilistic Systems
• Game Theory
• +5 more

Krishnendu Chatterjee is a Professor at the Institute of Science and Technology Austria (IST Austria) , Department of Computer Science. His research spans formal verification, probabilistic systems, game theory, and evolutionary dynamics, with over 300 peer-reviewed publications in top venues such as DISC, AAAI, LICS, PNAS, Nature , and Journal of the ACM . His research focuses on developing theoretical foundations and practical algorithms for analyzing complex systems, including Markov decision processes, stochastic games, probabilistic programs, and evolutionary models. He has made significant contributions to topics such as reachability analysis, termination of probabilistic programs, synthesis of controllers, and evolutionary game dynamics. Chatterjee's work is highly interdisciplinary, bridging computer science, mathematics, and biology. He has collaborated extensively with leading researchers worldwide and has been involved in editorial roles and program committees for major conferences in formal methods and theoretical computer science.

Ruzica PiskacView profile

Professor

• Software Verification
• Security and Applied Cryptography
• Automated Reasoning
• +4 more

Ruzica Piskac is a Professor of Computer Science at Yale University, where she leads the Rigorous Software Engineering (ROSE) group. She has made significant contributions to the fields of software verification, security, automated reasoning, and code synthesis, focusing on improving software reliability and trustworthiness through formal techniques. Dr. Piskac received her PhD from the Swiss Federal Institute of Technology (EPFL) in 2011, where her dissertation won the Patrick Denantes Prize. Prior to joining Yale, she led an independent research group at the Max Planck Institute for Software Systems in Germany (2012-2013). Her research spans several key areas: symbolic execution for Haskell (G2), privacy-preserving formal methods (PPFM), functional reactive synthesis, verification of configuration files, and analysis of software updates. Her work consistently bridges theoretical formal methods with practical applications in real-world systems. Dr. Piskac's recent publications demonstrate a strong trend toward applying formal verification techniques to emerging challenges including large language models, quantum computing security, legal accountability of automated systems, and cyber-physical systems. Her research increasingly intersects with AI, cryptography, and legal domains while maintaining strong foundations in formal methods. Her scientific achievements have been recognized with numerous prestigious awards: Multiple Amazon Research Awards Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation (SEIF) Patrick Denantes Prize for her PhD dissertation Dr. Piskac has graduated five PhD students, four of whom have gone on to become assistant professors of computer science. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and is on the Steering Committee of the Formal Methods in Computer-Aided Design conference. She leads the Rigorous Software Engineering (ROSE) group at Yale, which focuses on several key projects including: Symbolic Execution Engine for Haskell (G2) Privacy Preserving Formal Methods (PPFM) Functional Reactive Synthesis Verifications for Configuration Files Analysis of Software Updates and Configuration Files

• Knowledge representation and reasoning
• Planning
• Nonmonotonic reasoning
• +2 more

Yannis Dimopoulos is a Professor in the Department of Computer Science at the University of Cyprus. He has previously held research positions at the Max-Planck Institute for Computer Science in Saarbrücken and the University of Freiburg in Germany. He earned his B.Sc. and Ph.D. in Computer Science from the Athens University of Economics and Business. His primary research interests include: Knowledge representation and reasoning Planning Nonmonotonic reasoning Constraint satisfaction Machine learning His recent research, based on publications from 2017 to 2024, focuses on the theoretical and computational aspects of abstract argumentation, particularly control argumentation frameworks, probabilistic extensions, and the integration of argumentation with Boolean networks and negotiation under incomplete information. He has also contributed significantly to Answer Set Programming (ASP) for planning, developing the plasp 3 framework for effective ASP-based planning solutions. His scholarly work appears in top-tier venues such as AAAI, IJCAI, ECAI, KR, AAMAS, and journals like Artificial Intelligence and Autonomous Agents and Multi-Agent Systems. His most frequent collaborators include Pavlos Moraitis, Jean-Guy Mailly, Antonis C. Kakas, and Wolfgang Dvorák. No scientific awards or honors are mentioned in the provided text. Yannis Dimopoulos has advised or collaborated with several researchers, including Jean-Guy Mailly, Pavlos Moraitis, Nabila Hadidi, and Muhammad Adnan Hashmi, though formal student-advisor relationships are not explicitly detailed. His work often involves theoretical and computational modeling, and while specific grants or funding sources are not listed, his sustained publication record suggests active research support. He is actively involved in research teams and collaborations focused on argumentation, multi-agent systems, and automated reasoning, as evidenced by his extensive co-authorship network.

Mary Elizabeth KurzView profile

Associate Professor

• Scheduling
• Metaheuristics
• Industrial Engineering
• +5 more

Mary Elizabeth Kurz is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. Her research focuses on scheduling optimization, metaheuristics, and assembly line balancing for complex manufacturing systems. Education: B.S., Systems Engineering, University of Arizona (1995) M.S., Systems Engineering, University of Arizona (1997) Ph.D., Systems and Industrial Engineering, University of Arizona (2001) Research interests include: Development of heuristics and metaheuristics for scheduling Assembly line balancing with ergonomic constraints Flexible flowline scheduling with sequence-dependent setups Application of genetic algorithms and particle swarm optimization Recent work trends show applications in opioid crisis modeling, photolithography scheduling, and automotive configuration management. She has presented at INFORMS and IISE conferences, with special emphasis on multi-objective optimization and real-world industrial constraints. Scientific awards: Third Place Best Paper, ASME Manufacturing Engineering Division (2014) As an INFORMS member and Institute of Industrial Engineers Senior member, she has taught courses in operations research, decision support systems, and metaheuristics. Her work spans both theoretical and applied domains, with a strong focus on manufacturing system efficiency.

Zachary KincaidView profile

Associate Professor

• Program Analysis
• Logic
• Programming Languages
• +7 more

Zachary Kincaid is an Associate Professor in the Department of Computer Science at Princeton University. His research focuses on program analysis , logic , and programming languages , with emphasis on making analysis compositional and robust . PhD, University of Toronto (2016) BSc, Western University Research Interests Dr. Kincaid develops algebraic program analysis frameworks combining symbolic methods with abstract interpretation. His work addresses challenges in: Compositional analysis of concurrent and recursive programs Termination analysis for loops with complex control flow Non-linear numerical invariant generation Strategy synthesis for logical games Parameterized program verification Publication Trends His research output spans program analysis (2024-2010), formal verification (2018-2010), concurrency (2016-2010), and automated synthesis (2013-2012). Recent work (2024) explores polynomial ideals and nonlinear ranking functions , while foundational contributions include vector addition systems and recurrence-based invariants . Scientific Engagement Dr. Kincaid contributes to the academic community through: Program Committee service (PLDI, POPL, CAV, IJCAI, LICS, FMCAD, ESOP, etc.) Co-developing the Duet analyzer for unbounded concurrency Collaborative work with leading researchers (Tom Reps, Azadeh Farzan, Jason Breck) Advising & Grants He advises PhD students and leads research funded by the ONR grant N00014-19-1-2318 . Current advisees include Jake Silverman and Shaowei Zhu, while former student Charlie Murphy (PhD 2023) now holds a postdoctoral position at University of Wisconsin-Madison. Labs & Teams Dr. Kincaid co-developed the Duet program analyzer and contributes to tools like Srk and SimSat . His work integrates SMT solvers (MathSAT, Z3) and mathematical frameworks (rational vector addition systems, recurrence relations) for robust program analysis.