Florentina Voboril is a Research Fellow in the Algorithms and Complexity group at Technische Universität Wien. Her research explores how Large Language Models can solve Constraint Programming instances efficiently, with additional focus on SAT-solving and algorithm design. She investigates applications of AI in combinatorial optimization problems and develops innovative approaches to algorithmic challenges. Voboril has developed methods for SAT-based local improvement in string problems and generates streamlining constraints using LLMs. Her work bridges theoretical computer science with practical applications in computational biology and AI-assisted programming.
Prof. Pedro Barahona is a former Visiting Professor at the International Center for Computational Logic (ICCL) , affiliated with the Faculty of Computer Science at TU Dresden. His research focuses on translating Constraint Satisfaction Problems (CSP) into Satisfiability (SAT) frameworks, with contributions to encoding techniques and algorithmic optimization. His work bridges formal logic, artificial intelligence, and computational complexity. Key publications (2013-2014) address SAT-based approaches for CSP constraints, hierarchical hybrid encodings, and efficient logical translations. Though listed as a former member, his contributions remain part of the ICCL's research portfolio. No awards or grants are explicitly noted in the provided materials.
Yang Liu is a computer scientist specializing in theoretical computer science, particularly in the design and analysis of parameterized and exact algorithms. He has been affiliated with institutions such as Texas A&M University and has collaborated extensively with researchers like Jianer Chen and Songjian Lu. His work primarily focuses on NP-hard graph problems, including feedback vertex set, multiway cut, matching, and packing, where he has contributed improved fixed-parameter tractable algorithms and kernelization techniques. His research lies at the intersection of algorithms, complexity theory, and combinatorics. Key areas include: Fixed-parameter tractability (FPT) Kernelization and measure-and-conquer methods Graph partitioning and structural graph theory Randomized and deterministic exact algorithms Algebraic methods in polynomial testing His publications in top journals such as Journal of the ACM , Algorithmica , and Theoretical Computer Science demonstrate a consistent contribution to foundational algorithmic research. The article trends show a strong focus on solving hard combinatorial problems through novel algorithmic frameworks, often improving time complexity or kernel bounds. Notable scientific contributions include: A landmark 2008 JACM paper proving that the Directed Feedback Vertex Set problem is fixed-parameter tractable. Improvements in kernel sizes for feedback vertex and packing problems. Applications of color-coding and iterative expansion in 3D-matching. While no explicit information about advisees or grants is available, his long-term collaboration network suggests a role in mentoring and team-based research. He has not been associated with any lab or research center in the provided data.
Katalin Fazekas is an Assistant Professor in the Formal Methods in Systems Engineering group at TU Wien. Her research focuses on improving incremental reasoning methods for SAT/SMT solvers and advancing formal verification techniques. She holds a PhD from the Johannes Kepler University Linz, supervised by Armin Biere. Education: PhD in Logical Methods in Computer Science (LogiCS), JKU Linz (2016–2021) Research Interests: Incremental SAT/SMT solving Formal verification of distributed systems Algorithm optimization for constraint solving Automated reasoning and proof generation Key Projects: INCR (2021–2024) : Austrian Science Fund (FWF) project on scalable verification via incremental reasoning REVEAL-AI and SLIM : Collaborative projects on AI-driven formal methods Awards: Hertha Firnberg Fellowship (FWF), 2021–2024 Tools Developed: CaDiCaL 2.0: Advanced SAT solver QSM: Quantified symmetric minimization framework for distributed protocols Lab/Affiliations: FORSYTE research group, TU Wien.
Clark Barrett is a Professor in the Department of Computer Science at Stanford University, where he conducts research in formal methods, automated reasoning, and verification. He is affiliated with several research centers including the Stanford Center for Automated Reasoning, Stanford Center for AI Safety, Stanford Agile Hardware Project, and Stanford Center for Blockchain Research. His research focuses on developing formal methods and tools for verifying complex systems, with particular emphasis on satisfiability modulo theories (SMT), verification of neural networks, hardware design verification, and security. His work bridges theoretical foundations with practical applications across multiple domains. Over the past decade, Barrett's research has evolved from foundational work in SMT solving to increasingly diverse applications including neural network verification, hardware verification, and AI safety. His recent publications demonstrate a strong focus on practical verification techniques for real-world systems, particularly in the areas of hardware design, neural networks, and programming languages. The trend shows an expansion from core verification techniques to broader applications in AI safety and secure systems design. 2021 CAV (Computer Aided Verification) Award Barrett leads several major research initiatives and collaborates extensively with industry partners. His work on the Marabou neural network verification framework, SMT-LIB standard, and Symbolic QED verification methodology have had significant impact in both academic and industrial settings. He has supervised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. He is a key member of the Stanford Center for Automated Reasoning, which develops foundational technologies for automated reasoning, and the Stanford Center for AI Safety, where he focuses on formal methods for ensuring the safety and reliability of AI systems. His work on the Stanford Agile Hardware Project aims to revolutionize hardware design through formal methods and verification techniques.
Dr. Yuliya Lierler is a Professor in the Department of Computer Science at the University of Nebraska Omaha's College of Information Science & Technology. She has been a faculty member since 2012, reaching the rank of full professor, and was appointed to the Cheryl Prewett Diamond Professorship in 2020. Her work focuses on artificial intelligence, particularly in knowledge representation, automated reasoning, and declarative problem solving. PhD in Computer Science (University of Texas at Austin, 2010) Dr. Lierler's research bridges logic programming with practical AI applications, including natural language understanding, constraint satisfaction, and SMT-based solvers. She is a co-director of the NLPKR lab and has authored over 70 peer-reviewed publications in venues like Artificial Intelligence Journal and AAAI. Her contributions include open-access textbooks and tools like text2alm for semantic information extraction. Her recent publications explore advancements in answer set programming (ASP) semantics, automated reasoning frameworks, and hybrid knowledge representation systems. She has served as program co-chair for major conferences like ICLP (2022) and PADL (2017), and received awards such as the IS&T Outstanding Research Award (2024). Dr. Lierler also mentors students and leads initiatives in teaching innovation through online education and professional development programs. Mentor of the Year Award, Aksarben Foundation (2025) IS&T Outstanding Research and Creativity Award (2024) Best Student Paper Award (with Amelia Harrison, 2016) Dr. Lierler contributes to academic service through leadership roles in international conferences and program committees. Her lab, NLPKR, focuses on integrating natural language processing with formal logic, while her teaching emphasizes formal methods and AI foundations.
Madalina Raschip is an Associate Professor at the Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi , Romania. Her academic work spans constraint satisfaction, evolutionary computation, hybrid metaheuristics, and data mining. She earned her PhD at the same university and completed post-doctoral studies at the University of Neuchatel, Switzerland, supported by a Sciex Fellowship. Research Interests : Constraint satisfaction, evolutionary algorithms, hybrid optimization, data mining, graph neural networks, and automated algorithm selection. Teaching : Offers courses in Artificial Intelligence, Data Structures, Deep Learning in NLP, and Experimental Analysis of Algorithms. Publications : Focus on hybrid metaheuristics, constraint programming, sorting networks, and biomedical applications. Her work includes collaborations on medical data analysis, ant colony systems, and SAT solving. Awards : Sciex Fellowship for postdoctoral research. Research Groups : Member of the Evolutionary Computing Research Group and DECO (Data Engineering for Constraints Optimization).
Prof. Dr. Rolf Drechsler is a Full Professor at the University of Bremen since 2001 and Director of the Cyber-Physical Systems Group at DFKI Bremen since 2011. He leads the Computer Architecture working group in the Faculty of Mathematics and Computer Science and previously held adjunct/visiting roles at Duke University, IIT Kharagpur, and ISI Kolkata. Education: Diploma in Computer Science, Goethe University Frankfurt (1992) PhD (summa cum laude), Goethe University Frankfurt (1995) Habilitation, Albert-Ludwigs-University Freiburg (1999) His research focuses on formal verification , design automation , and data structures for circuit/system design , with applications in RISC-V processors , quantum computing , and in-memory computing . Recent studies explore LLM integration in hardware verification and polynomial methods for arithmetic circuits. Key awards include IEEE/ACM Best Paper Awards (ICCAD 2018, DATE 2025), the Berninghausen Prize (2018), IEEE Fellow (2015), and ACM Fellow. He co-founded the Graduate School "System Design" and led editorial roles at IEEE/ACM journals.
Guido Tack is an Associate Professor in the Department of Data Science & Artificial Intelligence at Monash University's Faculty of Information Technology. He leads development of the MiniZinc constraint modeling language and is a core developer of Gecode, a leading constraint programming library. His research focuses on combinatorial optimization, constraint solver architecture, and industrial applications, extending to programming languages and computational logic. Education: PhD (Dr.-Ing.) in Computer Science from Saarland University (2009), Diplom Informatiker (MSc equivalent) from Saarland University (2003). Professional experience includes postdoctoral roles at NICTA, Saarland University, and KU Leuven before joining Monash in 2012 as a Lecturer and Monash Larkins Fellow. Research interests span constraint programming, optimization algorithms, and their applications. Recent projects include improving the MiniZinc ecosystem, optimizing metering systems for smart water networks, and quantum information technology. He has secured over 13 major research grants, including leadership in the ARC Training Centre for Optimization Technologies. Teaching: Chief Examiner for FIT5170, FIT9135, FIT5216; Lecturer for FIT1047, FIT4010, and others. Editorial Roles: Co-Editor-in-Chief of Constraints journal (2015), active in ACM and Association for Constraint Programming. Awards: 2024 Eureka Prize finalist, 2017 FIT Dean's Award, 2010 Doctoral Research Award. Publications span constraint solving techniques, optimization algorithms, and educational tools. Recent work emphasizes real-time path planning, bi-objective search, and energy-efficient systems. He contributes to both academic conferences and industry-focused projects, bridging theoretical research with practical applications.
Sebastian Krieter is a researcher in software engineering and product-line systems, affiliated with Otto-von-Guericke University Magdeburg, Germany. He completed his PhD in 2022 with a dissertation focused on efficient interactive and automated product-line configuration. His research spans software variability, feature modeling, configuration management, and automated software configuration. Research Interests: Software Product Lines and Variability Management Feature Modeling and Analysis Automated Configuration and Sampling Algorithms Combinatorial Testing and Feature Interaction Knowledge Compilation and SAT-based Analysis His recent work includes developing methods for T-wise interaction coverage, improving sampling efficiency in configurable systems, and integrating AI techniques into software variability management. He has contributed to the development of tools like FeatureIDE and UVL (Universal Variability Language). Scientific Contributions: Over 70 peer-reviewed publications in top-tier venues such as ICSE, SPLC, GPCE, and ASE. Key organizer of workshops including the 1st International Workshop on Reverse Variability Engineering (Re: Volution). Active contributor to open-source tools and datasets for benchmarking feature model analyses. Collaborations: He has collaborated extensively with Thomas Thüm, Gunter Saake, Thomas Leich, and a network of international researchers in software engineering and AI.
Igor Razgon is a Senior Lecturer and Programme Director for M.Sc. Advanced Computing Technologies at the Department of Computer Science and Information Systems , Birkbeck University of London. His research focuses on parameterized algorithms , combinatorial optimization , and computational complexity . Research Trends : Recent work explores width parameters in graph algorithms (e.g., mim-width), structural properties of graphs without large bicliques, and complexity analysis of constraint satisfaction problems. Awards : Recipient of the EATCS-IPEC Nerode Prize in 2020 for foundational work on directed feedback vertex sets. Collaborations : Co-investigator on two EPSRC-funded projects and frequent collaborator with researchers in graph theory (e.g., D. Marx, V. Lozin, B. O'Sullivan). Contact : Office at Room MAL260, Birkbeck University of London, or via email: igor@dcs.bbk.ac.uk
Olha Matsyi serves as an Assistant Professor (post-doc) within the Division of Applications of Contemporary Mathematical Analysis at Lodz University of Technology, with contact details including email olha.matsyi@p.lodz.pl and phone (+48) 42 631-36-17. Her research focuses on Operations Research and Mathematical Optimization, specializing in combinatorial algorithms for location theory, knapsack problems, community detection, and VLSI routing. She employs metaheuristic and bio-inspired methods to solve NP-hard optimization challenges, bridging theoretical mathematics with engineering applications in healthcare logistics and crisis management. Publications from 2020-2025 demonstrate increasing emphasis on real-world implementations like mobile medical service optimization and decision support systems, alongside theoretical advances in continuous coverage and constrained classification. Collaborative work with researchers such as Oksana Pichugina highlights her interdisciplinary approach to algorithm design. No scientific awards were documented. Information regarding student mentorship, research grants, or specific laboratory teams was not provided in the source material.
Holger H. Hoos is a Professor at the University of British Columbia's Computer Science Department, affiliated with the Institute for Computational Intelligence and Cognitive Systems (ICICS) and the Peter-Wall Institute for Advanced Studies. As of 2017, his primary affiliation shifted to Leiden University (Netherlands), though he remains connected to UBC. His research focuses on algorithmics, bioinformatics, computational intelligence, and computer music, with significant contributions to stochastic local search, empirical algorithmics, and SAT solving. Education: Ph.D. (Dr.rer.nat) in Computer Science from TU Darmstadt (1998), postdoctoral fellow at UBC. He has led the BETA Lab and SALIERI Project, exploring biomolecular structure prediction and music algorithms. Research interests span stochastic algorithms, RNA structure prediction, automated algorithm configuration, and interdisciplinary applications like flow cytometry analysis. His work bridges theoretical foundations with empirical methods, emphasizing practical problem-solving. Awards include the AAAI Fellowship, multiple best paper awards (GECCO 2020, IJCAI-JAIR 2010), and leadership roles in conferences like AAAI and LION. He has advised over 30 students, contributing to impactful projects in bioinformatics and AI. Labs/Teams: Founder of the BETA Lab (Bioinformatics, Theoretical and Empirical Algorithms), member of the Laboratory for Computational Intelligence (LCI), and collaborator on SALIERI (Computer Music).
Henry Kautz is a Professor in the Department of Computer Science at the University of Rochester, where he has held leadership roles including Director of the Institute for Data Science (2014–Present) and Chair of the Department of Computer Science (2007–2014). Previously, he served as Director of Intelligent Systems at Kodak Research (2006–2007), and held academic positions at the University of Washington and AT&T Laboratories. He currently serves as a Fellow of the AAAS, ACM, and AAAI. His research focuses on artificial intelligence, pervasive computing, and data science, with applications in public health monitoring, natural language processing, and social media analysis. He has over 20,000 citations with an h-index of 65 and i10-index of 124. His research interests include data mining social media for disease tracking, grounded language learning through text-video alignment, and knowledge representation systems combining logic and probability. Notable projects include deploying the 'nEmesis' system to prevent foodborne illness via social media analysis and developing tools like OPUS for social media user studies. His work bridges theoretical AI advancements with real-world societal challenges, emphasizing ethical and practical applications of technology. Dr. Kautz has received prestigious awards including the AAAS and ACM Fellowships, reflecting his contributions to advancing AI and computational science. His research trends show a strong focus on leveraging big data and machine learning for societal good, particularly in health surveillance, mental health monitoring, and urban mobility analysis. Collaborations with interdisciplinary teams highlight his commitment to applying AI to solve complex global problems. In terms of leadership, he has directed major initiatives in data science education and research, fostering innovation at the University of Rochester. His career spans academia and industry, with impactful contributions to both theoretical and applied domains of computer science.
Dr Joan Espasa Arxer is a Lecturer in the School of Computer Science at the University of St Andrews, specializing in the AI group. His research focuses on Automated Planning, Constraint Programming, and applications of logic in computer science. He holds a position in the Department of Computer Science and teaches courses such as CS4402 - Constraint Programming and CS4303 - Videogames, while supervising projects across various academic levels. Research interests include Classical and Numeric AI Planning, Boolean Satisfiability (SAT), Satisfiability Modulo Theories (SMT), Planning as Satisfiability, Constraint Programming, and Automated reformulation of models. His work addresses challenges in planning domain modeling, benchmark instance generation, and cross-paradigm problem solving. Recent publications explore lifted planning with constraints, international planning competitions, and modeling pipelines in AI. He collaborates on tools like the 2023 International Planning Competition dataset and frameworks for generating benchmark instances. Advises PhD students Mustafa Abdelwahed and Carla Davesa Sureda. Engages in community outreach through events like 'Doors Open @ Computer Science' and contributes to open-source tools for planning and constraint satisfaction.
