Cayden Codel is a Researcher at Carnegie Mellon University's Computer Science Department , focusing on programming languages and formal verification. His work bridges theoretical logic with practical applications in automated reasoning and constraint solving. Research Interests: Programming Languages, Formal Verification, Satisfiability (SAT) Solvers, Satisfiability Modulo Theories (SMT), Automated Theorem Proving, Machine Learning Institution: Carnegie Mellon University (CMU) His publications emphasize formal verification for logical systems, SMT/SAT solvers , and reinforcement learning applications. Articles from 2024-2019 reveal a trajectory from foundational logic to real-world dataset design (e.g., Minecraft-based AI research). Thesis Advisors: Marijn Heule, Jeremy Avigad Contact: ccodel@andrew.cmu.edu
Sharon Shoham is a Professor at the School of Computer Science, Tel Aviv University, specializing in formal verification and program analysis. She leads research under a 5-year ERC grant on supervised verification of infinite-state systems and mentors PhD students, postdocs, and visiting scholars. Research Interests : Her work focuses on formal verification, program analysis, model checking, and verification of distributed systems. She explores inductive invariants, quantifier instantiation, and abstract interpretation. Recent Publications (2023-2025) highlight advancements in liveness properties , hyperproperty verification , quantifier elimination , and distributed protocol verification , often leveraging first-order logic and constraint-solving techniques. Scientific Awards : Distinguished paper awards at POPL 2025 and POPL 2024 Best paper and best student paper awards at DISC 2024, CAV 2019, and DISC 2018 Best paper awards at VMCAI 2016, ATVA 2009, SPIN'08, SAS'07, and FMCAD'07 Advising & Grants : Actively recruits strong PhD students and postdocs under an ERC grant. Collaborates with institutions like Technion and Tel Aviv University on verification frameworks and tools such as Ivy and mypyvy.
Prof. Sumit Kumar Jha is a Professor in the Department of Computer Science at Florida International University (FIU), specializing in artificial intelligence, formal methods, and computer architecture. His research focuses on AI-driven system design, in-memory computing, and robust machine learning systems. He leads over $17 million in active research projects from agencies like DARPA, AFRL, NSF, and DOE. Research interests include: Adversarial machine learning and model robustness Neuro-symbolic systems and program synthesis Analog/digital in-memory computing architectures Formal verification and safety-critical systems Explainable AI and model interpretability Recent work emphasizes secure LLM code generation, quantum computing applications, and fault-tolerant in-memory systems. His publications span top venues like ICML, ICLR, and DAC. Awarded FIU's Top Scholar Award (2024-25) and multiple best paper nominations. Active in NSF-funded initiatives including SPX (extreme-scale computing) and FMitF (formal methods in in-memory systems).
Alexander Nutz is a Researcher at the University of Freiburg's Department of Computer Science, affiliated with the Software Modeling and Verification Group. His primary research focuses on software model checking, satisfiability modulo theories (SMT), and Craig interpolation. He contributes to the development of verification tools such as SMTInterpol and the Ultimate Program Analysis Framework. Nutz has held teaching roles in courses like Automata Theory, Decision Procedures, and Program Analysis since 2012, collaborating extensively with colleagues on seminar leadership and course assistance. Education: PhD in Computer Science from the University of Freiburg (2019), focusing on 'Data Flow in Program Verification.' Professional activities include jury membership in the SV-COMP competition (2014-2016, 2018) and contributions to the AVACS research project. His work emphasizes program analysis, verification frameworks, and automated reasoning techniques. Research interests span formal methods, program analysis, and the application of SMT solving to real-world systems like smart contracts. Nutz's projects include enhancing verification tools for memory safety checks and integrating data flow graphs into verification processes. Key contributions: Development of Ultimate Kojak and Automizer tools, exploration of map abstraction techniques, and advancements in interpolation-based verification methods. His publications address challenges in non-linear arithmetic verification and automated reasoning for complex software systems.
Martin Riener is a Senior Lecturer at the Department of Theory and Logic, Faculty of Informatics, TU Wien. His research focuses on automated theorem proving, higher-order logic, and formal methods. He contributes to the GAPT framework for proof theory and collaborates on the Vampire theorem prover. He has worked on projects like CERESω cut-elimination and TLAPM for TLA+. Education: PhD (2017) and MSc (2011) in Computer Science from TU Wien. Research projects include an Austrian Science Fund (FWF) project (2010–2012) on proof-theoretic applications of CERES. Teaching includes courses on programming fundamentals, digital systems, and formal modeling. He is involved in outreach activities explaining computer science concepts through unplugged activities for diverse age groups. Software contributions include GAPT, Vampire, and TLAPS. Contactable via three email addresses and ORCID: 0000-0001-8836-7808 .
Johannes Schoisswohl is a PreDoc Researcher at TU Wien's Department of Formal Methods in Systems Engineering. His research focuses on automated reasoning, theorem proving, and formal methods in computer science. He is involved in projects such as ForSmart (2023–2027) and SFB SPyCoDe (2023–2026), exploring topics like quantifier elimination, unification algorithms, and decision procedures for arithmetic systems. His work bridges theoretical foundations with practical applications in automated deduction and formal verification. Education: Diplom-Ingenieur (Dipl.-Ing.) and Bachelor of Science (BSc). His academic contributions include seminal papers on conflict-driven quantifier elimination (VIRAS framework), superposition-based reasoning with delayed unification, and inductive benchmarks for automated systems. His research emphasizes improving automated reasoning tools for real-world verification challenges. Notable projects include contributions to the ALASCA system for quantified linear arithmetic and the development of reflection-based techniques for automating induction. His publications span conferences like CADE, LPAR, and CICM, showcasing advancements in formal methods and logic-based AI.
Florian Frohn is a tenured lecturer ("Lehrkraft für besondere Aufgaben") in the Programming Languages and Verification research group at the Department of Computer Science, RWTH Aachen University. He holds a Dr. rer. nat. (2018), MSc (2013), and BSc (2011) in Computer Science, all from German institutions, with his bachelor's studies completed part-time. His research interests center on formal methods for software verification, including automated termination and complexity analysis of imperative programs, satisfiability of Constrained Horn Clauses (CHCs), SMT solving with integer exponentiation, loop acceleration, term rewriting systems, and abstract interpretation. He is a key contributor to several influential tools: LoAT (Loop Acceleration Tool), AProVE (Automated Program Verification Environment), and SwInE; he also worked on Astrée and the CAGE toolchain developed under the DARPA STAC program. His recent publications (2023–2024) demonstrate continued leadership in top venues such as FM, IJCAR, FoSSaCS, and SAS, with work advancing loop acceleration, non-termination proofs, and SMT solving. These contributions reflect a strong trend in developing practical, automated techniques for program verification and analysis. IJCAR Best paper honourable mention (2024) EASST Award for best ETAPS paper (2020) iFM Best Tool Paper Award (2017) ISR Best Poster Award (2017) SEFM Recognition Award (2016) CADE Woody Bledsoe Travel Award (2016) Florian Frohn actively contributes to the research community through program committee roles for major workshops and conferences including TACAS, HCVS, WST, SMT, and LPAR. He has advised no listed students but has been involved in mentoring through research collaborations. His teaching portfolio includes courses on verification techniques, satisfiability checking, and advanced programming concepts. He leads the Termination and Complexity Competition (termCOMP) and participates in organizing key events in the formal methods community.
Kangjing Huang is a researcher at Purdue University specializing in programming languages and software engineering. Their work bridges theoretical foundations with practical tool development in program synthesis. Research focuses on program synthesis methodologies , particularly reconciling enumerative and deductive approaches. Key contributions include: Developing library-based synthesis techniques for practical code generation Creating frameworks for efficient search space navigation Integrating formal verification with synthesis pipelines Publication trends show consistent advancement in automated programming systems from 2020-2022, with growing emphasis on real-world applicability through library integration. Awards and teaching activities are not documented in available sources. Huang actively contributes to the programming languages research ecosystem through publications at premier venues including PLDI and SAS, with work centered on making program synthesis more scalable and practical for developer workflows.
Xiaokang Qiu serves as Associate Professor in Purdue University's Elmore Family School of Electrical and Computer Engineering, specializing in Programming Languages and Software Engineering with core expertise in program verification, program synthesis, and automated deduction. His research establishes critical bridges between enumerative and deductive synthesis methodologies, developing novel frameworks for verified program generation. Key contributions include string transformation synthesis with concurrency guarantees, bit-vector manipulation optimization via syntax-guided enumeration, and network design automation through comparative learning techniques. This work consistently advances formal verification foundations while addressing practical software engineering challenges. Publication trends from 2017-2025 reveal escalating complexity in synthesis targets—from basic data-structure manipulations to concurrent string operations and network configurations. His approach increasingly integrates machine learning elements with formal methods, demonstrating how query-based learning can drive near-optimal system design while maintaining provable correctness guarantees across diverse computational domains.
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.
Yexiang Xue is an Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He joined Purdue in Fall 2018. His research focuses on integrating machine learning and probabilistic reasoning to enable optimal decision-making in high-dimensional, uncertain environments. Key areas include computational sustainability, materials science, robotics, and medical AI. Education: PhD in Computer Science, Cornell University (2018), advised by Carla Gomes and Bart Selman. B.Sc. in Computer Science, Peking University, China (2011). Research Interests: Xue develops cross-cutting computational methods for scientific discovery, constraint-embedded machine learning, and AI-driven sustainability. His work spans symbolic regression, probabilistic models, and applications in robotics, healthcare, and materials science. Recent efforts include end-to-end physics model discovery and integrating decision diagrams into neural networks. Awards: NSF CAREER Award (2024). IAAI Innovative Application Award (2017) for Phase-Mapper AI platform in materials discovery. Advising & Grants: Mentored multiple undergraduates (e.g., Luming Tang, Runzhe Yang) pursuing graduate studies. His NSF CAREER grant supports AI-driven scientific discovery. Teaching & Service: Taught courses like Statistical Machine Learning (CS 578). Serves on AAAI, UAI, and IJCAI program committees. Media coverage includes NSF News, Science, and MIT Technology Review.
David Brumley is a Professor of Electrical and Computer Engineering at Carnegie Mellon University with a courtesy appointment in the Computer Science Department. He previously served as Director of CyLab, CMU's Security and Privacy Institute, from 2015 to 2017. His research focuses on developing systems that automatically check software for exploitable bugs using program analysis with security-specific properties. Brumley received his Ph.D. in Computer Science from Carnegie Mellon University, an MS in Computer Science from Stanford University, and a BA in Mathematics from the University of Northern Colorado. Before his academic career, he served as a Computer Security Officer for Stanford University from 1998-2002. Brumley's research focuses on software security techniques that provide users with guarantees. His work sits at the intersection of model checking, formal methods, compilers, and logic, all applied to security problems. He develops efficient symbolic execution, reasoning about bit-level arithmetic in finite fields, sound decompilation, and decision procedures. His research also extends to network security and applied cryptography, focusing on efficient protocols, signature schemes, and privacy-preserving cryptography. A key aspect of his work involves binary code analysis, which allows reasoning about the security of code that actually executes. Brumley's publication record shows a consistent progression from theoretical foundations in program analysis to practical security systems. His work spans symbolic execution, fuzzing, exploit generation, and binary analysis. The Mayhem Cyber Reasoning System, which won the DARPA Cyber Grand Challenge, represents the culmination of his research vision for automated vulnerability detection and patching. His publications demonstrate how theoretical advances in program analysis can be translated into real-world security tools. USENIX Security Best Paper Awards (2003, 2007) International Conference on Software Engineering Distinguished Paper Award (2014) NSF CAREER Award (2010) United States Presidential Early Career Award for Scientists and Engineers (PECASE) (2010) Sloan Foundation Award (2013) DARPA Cyber Grand Challenge Winner ($2,000,000) (2016) Brumley has mentored numerous PhD students who have gone on to successful careers in academia and industry, including co-founders of ForAllSecure. He served as faculty mentor for the CMU Hacking Team Plaid Parliament of Pwning (PPP), which has been ranked #1 internationally and won DefCon 2013. His research has been supported by significant grants including DARPA programs and the NSF CAREER award. He also runs PicoCTF, an annual computer security contest for high school students that has become one of the largest cybersecurity education initiatives of its kind. Brumley leads the development of security systems through both academic research and commercialization. He is the CEO of ForAllSecure, which commercializes the Mayhem system developed through his academic research. His work bridges the gap between theoretical security research and practical security tools used by industry, creating a pipeline from academic innovation to real-world impact.
Maria Paola Bonacina is a Full Professor at the Department of Computer Science, University of Verona. She leads the ARLette (Automated Reasoning Laboratory) research group and co-leads the Artificial Intelligence research group. Her office is located at Ca' Vignal 2, Floor 1, Room 73. Her research focuses on symbolic reasoning and automated deduction within artificial intelligence, specializing in conflict-driven theorem proving, satisfiability modulo theories (SMT), formal verification of software/hardware systems, proof interpolation, and parallel reasoning algorithms. Key applications include program verification, model checking, and intelligent system design. She serves on departmental governance bodies including the PhD Council for Computer Science and Department Council. Her teaching includes graduate courses on Automated Reasoning, Software Verification, and Planning & Reinforcement Learning.
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.
Olivier Bailleux is a Research Professor at the University of Burgundy within the Faculty of Science and Technology . His work focuses on computational logic and optimization. Teaching: C/C++ programming, constraint programming, logical programming Research: SAT resolution, constraint decomposition, genetic algorithms Team: Data Science Research Interests: SAT solvers, constraint programming, and algorithmic optimization. His projects explore efficient translations of complex constraints into Boolean models. Publications span topics like Pseudo-Boolean encoding, DPLL/CDCL algorithm comparisons, and minimal resolution refutations, reflecting interdisciplinary work in logic and artificial intelligence.
