Find Academic Supervisors

• Formal Methods
• Model-Based Testing
• Cyber-Physical Systems
• +5 more

Paolo Arcaini is a Project Associate Professor at the National Institute of Informatics (2019–present) and previously held academic positions at Charles University (Assistant Professor, 2015–2018) and the University of Bergamo (Postdoctoral Fellow). His research spans formal methods, model-based testing, and quantum software engineering, with a focus on cyber-physical systems, autonomous vehicles, and machine learning validation. He has received multiple best paper awards, including at SSBSE (2021), AITest (2020), and Mutation (2017). Educational Background: PhD in Computer Science, University of Milan (2010–2012, advisor: Elvinia Riccobene) Postdoctoral Fellow, CNR-IDPA (2013, advisor: Gloria Bordogna) Research Interests: Dr. Arcaini's work bridges abstract state machine modeling, hybrid system falsification, and quantum program testing. His recent projects address robustness in neural networks, search-based scenario generation for autonomous systems, and quantum annealing applications to test case optimization. Article Trends: His publications demonstrate expertise in hybrid system falsification using Monte Carlo tree search mutation analysis for regular expressions and neural networks quantum software testing via combinatorial and search-based methods tool development for NuSMV/Yices integration and quantum noise mitigation multi-objective optimization for autonomous delivery systems formal methods in medical software validation Scientific Recognition: Distinguished Paper Award, SSBSE 2021 Best Paper Award, AITest 2020 Best Paper Award, Mutation 2017 Teaching: He has taught courses on Formal Methods for Cyber-Physical Systems (NII 2019) Model-Based Testing (Charles University 2015–2018) Java programming labs and advanced topics Tool Development: Dr. Arcaini has created several tools including MutRex/TearRex for regular expression testing NuSeen (Eclipse-based NuSMV environment) ATGT Boolean for SAT-based testing CRAG for combinatorial road geometry generation QUIET for quantum noise mitigation BQTmizer for quantum test case optimization

• Software Engineering
• Formal Verification
• Model-Driven Engineering
• +4 more

Petra Kaufmann is an academic at TU Wien's Institut für Softwaretechnik und Interaktive Systeme. Her research focuses on formal verification techniques for software models, model-driven engineering, and model versioning. She has contributed to advancing methodologies for consistency checking in multiview models, semantic-aware merging of UML diagrams, and SAT-based model debugging tools. Her work integrates formal methods like OCL and CTL with practical software development challenges. Kaufmann collaborates on educational initiatives, such as modernizing UML teaching tools, and explores model versioning solutions for collaborative software projects. Key research areas include: Formal verification of software models Model consistency and versioning UML-based system modeling Automated debugging and conflict resolution SAT-based analysis tools Her recent publications emphasize semantic-aware approaches to model evolution and merging, addressing challenges in maintaining consistency across state machines and sequence diagrams. She has advised students like Robert Bill on software model checking and model-driven engineering topics. Her work bridges theoretical formal methods with practical software development workflows, particularly in collaborative environments.

Gregor BehnkeView profile

Assistant Professor

• AI Planning
• Automated Decision Making
• Complexity Theory
• +8 more

Gregor Behnke is an Assistant Professor (Universitair Docent) at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, Faculty of Science. He heads the research group on Planning and Symbolic AI within the Theoretical Computer Science unit. His primary affiliation is with the Institute for Logic, Language and Computation, where he conducts research at LAB42, Science Park 900, Amsterdam. Dr. Behnke's research focuses on solving optimization and decision problems using symbolic AI methods, with particular emphasis on AI planning, automated decision making, complexity theory, and combinatorial optimization. His specific interests include Hierarchical (HTN) Planning, Planning using Symbolic Methods (SAT, BDDs), Lifted Planning, Theory of Planning and its Complexity, Domain and Action Model Learning, and Epistemic Planning. He is particularly interested in developing practically efficient solutions to complex planning problems. His recent publications demonstrate a strong focus on computational complexity analysis of planning problems, verification methods for hierarchical planning, and domain learning techniques. His work bridges theoretical foundations with practical implementations, as evidenced by his development of planning systems. ICAPS 2018 Best Student Paper Award for 'A Generic Method to Guide HTN Progression Search with Classical Heuristics' ICTAI 2018 CV Ramamoorthy Best Paper Award for 'Plan and Goal Recognition as HTN Planning' TCST 2018 Best Paper Award for 'Towards a Companion System Incorporating Human Planning Behavior -- A Qualitative Analysis of Human Strategies' Dr. Behnke supervises two PhD students: P. Maurice Dekker (funded by a startersbeurze, working on Bounded Rationality in Planning since January 2024) and João Sá (funded by an NWO Open Competitie M, working on SAT planning since March 2024). He is the main developer (together with Daniel Höller) of the HTN planning system PANDA, which has two maintained versions: PANDA3 (Java/Scala) and pandaPI (C++). His research group currently consists of himself and his two PhD students. His laboratory work centers around the development and analysis of planning algorithms, particularly focusing on HTN planning systems and their verification. The group works on both theoretical aspects of planning complexity and practical implementations of planning systems.

Katalin FazekasView profile

Assistant Professor

• Formal Verification
• SAT Solving
• SMT Solving
• +3 more

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.

Teodora BalutaView profile

Assistant Professor

• Computer Security
• Machine Learning
• Differential Privacy
• +3 more

Teodora Baluta serves as an Assistant Professor and the Alan and Anne Taetle Early Career Professor at the School of Cybersecurity and Privacy, Georgia Institute of Technology, where she leads research at the critical intersection of computer security and machine learning. Her work focuses on establishing rigorous security analyses for machine learning systems through algorithmically sophisticated yet practically applicable approaches. Her academic foundation was built at the National University of Singapore (NUS) through graduate studies supervised by Professors Prateek Saxena and Kuldeep S. Meel: National University of Singapore (NUS) - Graduate Studies Dr. Baluta's research program spans computer security, machine learning, and formal methods with concentrated expertise in differential privacy, security verification for neural networks, and causal reasoning applications. She investigates membership inference attacks, model unlearning mechanisms, and security challenges in large language models while maintaining strong connections to real-world security problems through publications in premier venues like CCS, NDSS, SAT, FSE, and OOPSLA. Analysis of her publication trajectory (2017-2025) reveals an evolving research focus from foundational security work on taint analysis and insider threats toward cutting-edge investigations into AI security. Recent publications demonstrate increasing specialization in large language model security, privacy-preserving techniques for graphs, and causal approaches to security verification, establishing her as a leader in securing next-generation AI systems. Her research excellence has been recognized through: Google PhD Fellowship EECS Rising Stars 2023 Dean’s Graduate Research Excellence Award President’s Graduate Fellowship Microsoft Research PhD Fellowship Finalist, Asia-Pacific While current student mentorship details are not publicly specified, her Early Career Professorship indicates an active research group development phase. Her doctoral work at NUS was conducted within the KISP lab and MeelGroup, and she now establishes her independent research direction at Georgia Tech's School of Cybersecurity and Privacy. Current research activities center on advancing security frameworks for machine learning systems, with particular emphasis on developing formal verification methods for neural networks and privacy-preserving techniques applicable to real-world deployment scenarios.

Corina PasareanuView profile

Researcher

• Model Checking
• Symbolic Execution
• Compositional Verification
• +6 more

Corina Pasareanu is a Principal Scientist at Carnegie Mellon University's CyLab and Technical Professional Leader for Data Science at NASA Ames Research Center (working through KBR). She maintains strong affiliations with both CMU's School of Computer Science and NASA Ames, leading cutting-edge research at the intersection of formal verification, AI safety, and software security. She earned her Ph.D. in Computer Science from Kansas State University in 2001, following MS (1995) and BS (1994) degrees in Computer Science from University Politehcnica of Bucharest. Her academic foundation has propelled her to become a leading expert in verification techniques for complex software systems. Dr. Pasareanu's research program focuses on model checking, symbolic execution, compositional verification, and probabilistic software analysis, with growing emphasis on ensuring safety and reliability of AI systems. Her work bridges theoretical formal methods with practical applications in autonomous systems and security-critical domains. Recent efforts target verification challenges in large language models and vision-based autonomous systems, developing techniques to provide mathematical guarantees of system behavior despite AI component uncertainties. Analysis of her publication trends reveals a strategic evolution from foundational verification techniques toward increasingly complex AI systems, with strong emphasis on practical applications in safety-critical contexts. Her work consistently connects theoretical advances in formal methods with real-world security and safety challenges. Her scientific contributions have earned exceptional recognition: ACM Fellow and IEEE ASE Fellow ETAPS Test of Time Award (2021) ASE Most Influential Paper Award (2018) ESEC/FSE Test of Time Award (2018) ISSTA Retrospective Impact Paper Award (2018) Multiple historical impact awards including ACM Impact Paper Award (2010) and ICSE Most Influential Paper Award (2010) Dr. Pasareanu actively mentors the next generation of computer scientists, currently advising PhD students Aymeric Fromherz (with Bryan Parno), Yoshiki Takashima, Zichao Zhang, and Chi Zhang (all with Limin Jia), plus postdoc Ravi Mangal. She has secured substantial research funding from NSF, DARPA, AWS, NASA, and industry partners for projects including 'LLM Self-Defense Against Adversarial Attacks,' 'Trinity: Neurosymbolic Learning and Reasoning,' and 'HUGS: Human-Guided Software Testing.' Her leadership extends to Program Co-Chair for ICSE 2025 and multiple other major conferences, plus service on steering committees for ICSE, ETAPS, TACAS, and ISSTA. As Principal Scientist at CMU CyLab, she leads research teams developing verification techniques for AI systems, with particular focus on autonomous vehicles and large language models. Her NASA Ames work applies formal methods to space-related autonomous systems, while her collaborations with industry partners translate theoretical advances into practical tools. She remains at the forefront of addressing verification challenges for increasingly complex AI technologies, with upcoming keynotes at CAV 2025 and FormaliSE 2025 demonstrating her continued leadership in the field.

Percy Shuo LiangView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +5 more

Percy Shuo Liang is an Associate Professor of Computer Science and Courtesy Associate Professor of Statistics at Stanford University. He directs the Center for Research on Foundation Models (CRFM) and is affiliated with Human-Centered Artificial Intelligence (HAI), the Artificial Intelligence Lab, and the Natural Language Processing Group. His research focuses on foundational aspects of machine learning, natural language processing, and reproducible research methodologies. He co-developed CodaLab Worksheets, a platform for experiment reproducibility, and leads the Marin community for open foundation model development. Education: B.S. (2004) and MEng (2005) in Electrical Engineering and Computer Science from MIT, advised by Michael Collins. Ph.D. (2011) in Computer Science from Berkeley under Michael Jordan and Dan Klein. Postdoctoral researcher at Google (2012). Research interests include foundation models, copyright challenges in AI, data weighting strategies, model interpretability, and ethical AI. Over 30 students and postdocs have been advised, many now holding prestigious academic and industry roles. Awards include the Presidential Early Career Award (2019), Sloan Fellowship (2015), and ACM ICPC World Finals 2nd place (2002). Labs/Teams: CRFM, HAI, AI Lab, NLP Group, Machine Learning Group. Notable contributions include CodaLab Worksheets and Marin platform. Active in programming contests and piano competitions (KDFC Classical Star Search winner, 2008).

• Formal Verification
• Automated Reasoning
• SAT and SMT Solving
• +7 more

Bernd Becker is a Professor in the Department of Computer Science at the Faculty of Engineering, University of Freiburg, Germany. He has been actively publishing in the fields of formal methods, hardware verification, and automated reasoning since the 1980s, with a sustained record of high-impact publications up to 2025. His research interests span formal verification, SAT and SMT solving, hardware testing, probabilistic systems, and embedded systems security. His work bridges theoretical advances in automated reasoning with practical applications in processor design, RISC-V verification, and safety-critical systems. The recent articles highlight a strong focus on applying formal methods to emerging challenges in hardware and AI, including POMDPs for robot planning, RISC-V security, GPU verification, and energy-efficient monitoring. There is a clear trend toward integrating symbolic computation with machine learning and probabilistic reasoning. Bernd Becker has collaborated extensively with researchers such as Ralf Wimmer, Ilia Polian, and Matthias Sauer, indicating leadership in a large, interdisciplinary research group. His work has been supported by numerous grants, though specific details are not listed in the source text. He has contributed to major conferences such as DATE, FMCAD, and SAT, and has mentored several students and researchers, though specific names are not provided. He is involved in projects related to secure scan networks, physical unclonable functions, and smart home systems.

• Probabilistic Verification
• Formal Methods
• Artificial Intelligence Safety
• +7 more

Dr. Sebastian Junges is an Assistant Professor at the Software Science Group of Radboud University (Nijmegen, Netherlands) since September 2021. He previously held postdoctoral and research assistant positions at UC Berkeley and RWTH Aachen University respectively, working under prominent researchers like Sanjit Seshia and Joost-Pieter Katoen . Education: Ph.D. in Computer Science, RWTH Aachen University (2020) M.Sc. in Computer Science, RWTH Aachen University (2015) B.Sc. in Computer Science, RWTH Aachen University (2012) His research focuses on trustworthy algorithms for system dependability , particularly in safety-critical domains like autonomous systems and infrastructure automation. Key technical contributions include: Verification algorithms for Markov decision processes (MDPs) Parameter synthesis in probabilistic models Finite state controller generation Runtime monitoring with imprecise sensors Recent work (2022-2024) has explored multi-objective verification , neuro-symbolic reasoning , and scalable analysis of probabilistic models . His methodological approach combines formal methods with machine learning and automated reasoning techniques, often implemented in the state-of-the-art Storm probabilistic model checking framework. Selected scientific contributions include: Keynote speaker at MODEM 2024 Invited tutorial presenter at Dagstuhl 2024 Co-organizer of multiple VeriProp workshops (2021-2024) Program committee member for TACAS 2026, CONCUR 2024, and CAV 2024 He actively supervises Bachelor and Master student projects in algorithm development for probabilistic systems, including topics on: MDP model checking acceleration Runtime monitoring frameworks Syntax-guided controller synthesis State estimation for fault trees Probabilistic inference transpilation His academic service includes: Radboud Science Faculty Diversitee Committee Steering Committee of ETAPS (2025-2026) Program committee roles across 15+ top venues

• Modal Logic
• Temporal Logic
• Automated Reasoning
• +5 more

Cláudia Nalon is a full professor at the Department of Computer Science at the University of Brasília, where she has established herself as a leading researcher in modal logic and automated reasoning. Her academic career spans over two decades with continuous contributions to theoretical foundations and practical implementations of reasoning systems. Research Profile Professor Nalon specializes in proof methods for combined modal logics, particularly those allowing interaction between different logical components. Her PhD work focused on resolution-based methods for interactions between linear-time temporal logic and multi-modal S5. She has developed tableau-based methods for temporal logics of knowledge with both no learning and perfect recall scenarios. Her expertise extends to the theoretical foundations and implementation of reasoning tools for normal modal logics, including systems with symmetry, reflexivity, seriality, transitivity, and Euclideaness. More recently, she has pioneered resolution methods for Coalition Logics, a non-normal modal logic for reasoning about cooperative agency. Publication Trends Analysis of her 15 most recent publications (2020-2024) reveals consistent focus on modal logic reasoning with particular emphasis on resolution methods, reduction techniques, and model construction. Her work demonstrates a strong connection between theoretical foundations and practical implementations, with numerous papers presenting evaluated theorem-proving techniques and their applications. The publications span major venues in automated reasoning including IJCAR, TABLEAUX, and CADE. Professional Service Editorial board of The IfCoLog Journal of Logics and their Applications (since 2015) Editorial board of Annals of Mathematics and Artificial Intelligence (2025-2027) Steering committee for LSFA (2018-2021), TABLEAUX (2016-2023), CADE (2021-2024) Executive committee of the Brazilian Logic Society (2017-2021) Program committees for over 30 international conferences from 2010 to 2025 Mentorship and Software Development Professor Nalon has supervised multiple student projects resulting in significant software implementations for modal logic reasoning. She has personally developed several theorem provers including ltl2snf (for temporal logic translation), KSP (for multimodal K), and CLProver (for Coalition Logic). Her mentorship has produced numerous student-led implementations that extend these systems, demonstrating her commitment to training the next generation of researchers in formal methods. Community Building Professor Nalon has been instrumental in establishing research communities focused on logic and automated reasoning in Brazil. She served as co-chair for TABLEAUX 2017 in Brasília and has organized multiple editions of the Brazilian Logic Workshop (WBL). Her work with the LSFA workshop series has fostered a strong international community of researchers in logical and semantic frameworks.

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

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

Georg WeissenbacherView profile

Professor

• Formal Methods in Systems Engineering
• Software Verification
• Concurrency
• +5 more

Georg Weissenbacher is a Professor at Vienna University of Technology , affiliated with the Network Lab and the research area of Formal Methods in Systems Engineering . His academic work focuses on software verification , formal methods , and concurrent systems . University: Vienna University of Technology Research Area: Formal Methods in Systems Engineering Academic Rank: Professor Education: D.Phil. Univ.Prof. Dipl.-Ing. Research Interests : Weissenbacher's work centers on formal verification techniques for software and hardware systems. Key areas include: Hyperproperties : Analyzing properties of systems involving multiple execution traces, such as security and robustness. Model Checking : Applying bounded model checking to automotive software (e.g., AUTOSAR components) and concurrent systems. Symbolic Execution : Leveraging symbolic execution for bug detection in programs with complex behaviors like speculative execution. Concurrency Bugs : Formalizing heisenbugs and developing methods to extract safe thread schedules from incomplete model checking results. Automated Reasoning : Improving interpolation-based techniques and SAT solvers for program analysis and fault localization. Publication Trends : His recent articles (2024-2025) emphasize symbolic execution for hyperbug detection, machine learning in security verification, and automotive software validation. Earlier works (2023-2021) explore model checking , mutation testing , and concurrent system analysis . Projects : He leads initiatives such as: Abstraction-based Parameterized TLA Checker Bit-level Accurate Reasoning and Interpolation Tools for Concurrent and Distributed Systems LogiCs-Stipendien (Formal Methods in Computer-Aided Design) Collaboration and Supervision : Weissenbacher co-edits conference proceedings (e.g., FMCAD 2023 , CAV 2018 ) and supervises students in formal verification, including Andreas Fellner (model-based mutation testing), Emmanuel Pescosta (speculative non-interference), and Tobias Nießen (hyperproperty counterexamples).

• Formal Methods
• Software Verification
• Service-Oriented Computing
• +4 more

Carlos Gustavo Lopez Pombo is an Adjunct Associate Professor in the Department of Computing and Software at McMaster University. His research focuses on formal methods and software engineering, with particular emphasis on verification techniques for service-oriented systems. Research Interests: Dr. Pombo specializes in formal verification methods, temporal logic, and quality-of-service analysis in distributed systems. His work bridges theoretical computer science and practical software engineering challenges, developing tools for automated analysis of software specifications and service-oriented architectures. Publication Trends: His recent work (2023-2025) focuses on quality-of-service verification in choreographic systems and dynamic service contracts, while earlier publications established foundations in specification languages (Alloy, Z Notation) and verification algorithms (SAT-based bounded verification).

Jean-Marie LagniezView profile

Associate Professor

• Artificial Intelligence
• Constraint Programming
• SAT Solving
• +3 more

Jean-Marie Lagniez is an Associate Professor at the IUT de Lens, Université d'Artois, France. He holds a post-graduate degree in Algorithmic for Inference and Decision from the Informatics Graduate School of Artois University and completed his PhD at the CRIL Laboratory (Centre de Recherche en Informatique de Lens). His research focuses on integrating local search and backtracking methods in SAT and CSP, improving CDCL solvers, and model counting. He has held postdoctoral positions at the Institute for Formal Models and Verification (Johannes Kepler University Linz, Austria) and CRIL. His work spans constraint programming, artificial intelligence, and formal logic. Education: Post-graduate degree in Algorithmic for Inference and Decision, Université d'Artois. PhD in Computer Science, CRIL Laboratory, Université d'Artois. Research Interests: Jean-Marie's research emphasizes enhancing SAT solving techniques, constraint satisfaction, model counting, and parallel computing. He explores applications in belief merging, modal logic, and algorithmic improvements for efficient problem-solving. His contributions include distributed model counters (DMC) and hybrid approaches combining local search with CDCL. Publications: His work in 2018-2017 includes advancements in consensus-finding, distributed SAT solvers, and modal logic satisfiability, published in venues like IJCAI, CP, and AI Communications. These studies highlight his expertise in bridging theoretical foundations with practical algorithmic solutions.

• Cybersecurity
• Formal Methods
• Attack Trees
• +5 more

Stefano M. Nicoletti is a Researcher at the University of Twente, holding dual affiliations with the Digital Society Institute and the Formal Methods and Tools department. His academic profile features an h-index of 31 with 26 documented research outputs spanning from 2020 to projected publications in 2025, demonstrating consistent scholarly productivity in formal methods for cybersecurity. Dr. Nicoletti's research expertise encompasses several critical domains in cybersecurity: Attack tree modeling and ontological frameworks Formal verification of security properties Risk management systems using ontology-based approaches Boolean logic applications in security analysis Quantification of MITRE ATT&CK campaigns Interoperability in security modeling frameworks His recent scholarly trajectory shows increasing focus on applying formal methods to practical security challenges in critical infrastructure, particularly smart grids and aerospace systems. The WATCHDOG framework represents his innovative approach to risk assessment through object-oriented disruption graphs, while his work on ontological lenses for attack trees addresses fundamental issues of adequacy and interoperability in security modeling. Professional recognition includes: Best Paper Award at the 21st International Conference on Software Engineering and Formal Methods (2023) Dr. Nicoletti maintains active collaborations with prominent researchers including M. Stoelinga, G. Guizzardi, and E.M. Hahn. His research bridges theoretical computer science with practical security applications, contributing significantly to both academic knowledge and potential real-world security solutions through datasets like Quantitative Comparisons of MITRE ATT&CK Campaigns and experimental reproduction packages for attack tree analysis.