جست‌وجوی استاد و پژوهشگر

• Cyber-Physical Systems
• Hybrid Systems Verification
• Formal Methods
• +۳ مورد دیگر

Stefan Alexander Schupp is a researcher affiliated with the Department of Cyber-Physical Systems at Technische Universität Wien (TU Wien). His work focuses on formal verification, hybrid systems, and controller synthesis for cyber-physical systems. Department: Cyber-Physical Systems (E191-01) Research Affiliation: TU Wien Network Lab Research interests include: Hybrid and stochastic system verification Controller synthesis for real-time systems Formal methods in software engineering Design and analysis of safety-critical systems Automated model checking techniques Flowpipe construction for reachability analysis Recent publications highlight collaborations with researchers from multiple institutions, covering topics such as: Hyperproperty verification in distributed systems Metric Temporal Logic (MTL) controller synthesis Adaptive simplex architectures for bounded-liveness properties Stochastic hybrid automata analysis tools HyPro verification tool developments Probabilistic reachability optimization

Mohamed Faouzi Atigمشاهده پروفایل

استاد

• Model Checking
• Program Verification
• Concurrency
• +۷ مورد دیگر

Mohamed Faouzi Atig is a Professor in Computer Systems at the Department of Information Technology, Uppsala University. His career spans roles as Senior Lecturer (2018-2021), Associate Senior Lecturer (2014-2018), and Researcher (2012-2018) at the same institution. He obtained his Doctoral Degree in Computer Science from the University of Paris Diderot-Paris 7 (2010) and a Master in Engineering from Tunisia Polytechnic School (2005). Current Role: Professor in Computer Systems Institution: Uppsala University Research Focus: Model checking, verification of infinite-state systems, weak memory models, automata theory, string constraints, concurrent program analysis His research explores formal methods for concurrent programs, weak memory models (TSO/PSO/POWER), automata theory for verification, and SMT solvers for string constraints. Recent work integrates graph neural networks with word equation solving and advances stateless model checking techniques. Key article trends include Weak Memory Model Verification (TSO, PSO, POWER) Stateless Model Checking Algorithms String Constraint Solvers (TRAU, Norn) Timed Automata and Multi-Pushdown Systems Mohamed Faouzi Atig has led collaborations on fence insertion procedures, timed pushdown automata, and database-driven system verification. His contributions are recognized through publications in top-tier conferences and journals.

• Theoretical computer science
• probabilistic programming
• probability theory
• +۲ مورد دیگر

Dr Fredrik Dahlqvist serves as a Lecturer in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he contributes to the Centre for Fundamental Computing and AI. His academic profile reflects a strong commitment to theoretical foundations in computing with direct applications to artificial intelligence research. His research spans theoretical computer science with focused expertise in probabilistic programming, probability theory, category theory, and mathematical logic. Dahlqvist investigates the mathematical frameworks governing probabilistic computation, developing rigorous methodologies for uncertainty quantification in computational systems. His work establishes formal connections between abstract category theory and practical probabilistic programming implementations, creating bridges between pure mathematics and applied AI. Recent publications (2023-2024) demonstrate cohesive thematic development across probabilistic programming semantics, neural network optimization, and formal verification. Key trends include error analysis in probabilistic floating-point systems, neural network pruning techniques based on geometric similarity, and categorical foundations for graded computation. His research consistently merges theoretical depth with practical AI challenges, particularly in verification of probabilistic models and optimization of learning architectures. Dr Dahlqvist has not received any documented scientific awards or fellowships according to available sources. He actively supervises two PhD students: Gregor Meehan researching "Representation Learning For Musical Audio Using Graph Neural Network-Based Recommender Engines" and Niki Omidvari conducting theoretical work in computer science foundations. Dahlqvist currently holds a £15,000 grant from the Academy of Medical Sciences for "Learning from Each Other: Neural Networks and Finite Automata" (March 2025-March 2026), investigating formal connections between neural architectures and automata theory. As an integral member of Queen Mary's Centre for Fundamental Computing and AI, Dahlqvist collaborates within a multidisciplinary research environment focused on advancing theoretical underpinnings of artificial intelligence. His work contributes to the Centre's mission of developing mathematically rigorous frameworks for next-generation AI systems through category-theoretic approaches and probabilistic reasoning.

Robert Johnsonمشاهده پروفایل

مدرس ارشد

• extremal combinatorics
• graph theory
• probabilistic combinatorics
• +۳ مورد دیگر

Dr. Robert Johnson is a Senior Lecturer in Pure Mathematics at Queen Mary University of London, affiliated with the School of Mathematical Sciences. He leads the Communication and Public Engagement for the Centre for Combinatorics, Algebra and Number Theory. His research focuses on extremal combinatorics, graph theory, and probabilistic combinatorics, with particular emphasis on extremal problems on graphs, set systems, permutations, and discrete hypercube structures. Robert Johnson earned his PhD from the University of Cambridge in 2003 and joined Queen Mary in 2004 after a postdoctoral position at the London School of Economics. His work has contributed significantly to combinatorial optimization, Ramsey theory, and hypercube graph analysis. He has supervised multiple PhD students, including Trevor Pinto (2016), A Nicholas Day (2017), and Natalie Behague (2020), with current advisees Belinda Wickes and Asier Calbet Ripodas. His research explores topics such as resistor network optimization, synchronizing automata, and extremal set systems, with applications in discrete mathematics and theoretical computer science. Notable contributions include studies on multicolour Ramsey numbers of odd cycles and Turán properties in hypercube intersection graphs. Johnson's publications span over two decades, covering areas like permutation correlation, hypercube saturation, and Kneser graph Hamiltonicity. He is actively involved in mentoring and welcomes inquiries from prospective PhD candidates. His affiliations include the Combinatorics group at Queen Mary’s School of Mathematical Sciences and the Centre for Combinatorics, Algebra and Number Theory, where he drives public engagement initiatives.

Franz Baaderمشاهده پروفایل

استاد

• Logic in Computer Science
• Knowledge Representation
• Description Logics
• +۴ مورد دیگر

Prof. Franz Baader is a Full Professor for Automata Theory and Director of the Institute of Theoretical Computer Science at TU Dresden, Germany. He has held academic leadership roles including Dean of the Faculty of Computer Science (2012–2015). His research focuses on Logic in Computer Science, Knowledge Representation, and Description Logics. He has published extensively in top venues and received prestigious awards like the Herbrand Award (2020). His contributions include foundational work on automated deduction and term rewriting. Education: Dr.-Ing. in Computer Science (1989, FAU Erlangen-Nürnberg). Professional experience includes roles at DFKI, RWTH Aachen as Associate Professor, and visiting scholarships at NICTA (Australia). He is a Fellow of EurAI (2004). Research Interests: His work bridges theoretical computer science and practical knowledge representation systems. Key areas include description logics, automated reasoning, and formal methods. He has pioneered techniques for ontology repair and explanation generation in DL systems. Recognition: Over 250+ publications, H-index 69 (Google Scholar). Member of editorial boards for 6 journals, former President of CADE Inc., and active in international conferences like IJCAR and RTA.

Andrei Bulatovمشاهده پروفایل

استاد

• Constraint problems
• Computational complexity
• Combinatorics
• +۳ مورد دیگر

Andrei Bulatov is a Professor of Computing Science at Simon Fraser University (SFU), affiliated with the School of Computing Science within the Faculty of Applied Sciences. His research focuses on computational complexity, constraint satisfaction problems (CSP), combinatorics, and universal algebra. Bulatov earned his Ph.D. and M.Sc. in Mathematics from Ural State University, Russia, in 1995 and 1991, respectively. His work bridges theoretical computer science and algebra, with notable contributions to the complexity classification of CSPs. He has received a Best Paper Award at FOCS 2002 for his dichotomy theorem on three-element set constraints. Bulatov’s research also explores counting CSPs, algorithms for satisfiability, and applications of algebraic methods in discrete mathematics. He is part of the Algorithms & Theory Group and the Computational Logic Laboratory at SFU. Bulatov’s publications span journals like Journal of Computer and System Sciences, Theoretical Computer Science, and SIAM Journal on Computing, addressing topics from graph theory to randomized algorithms. His academic service includes roles in professional organizations and editorial work. Bulatov’s teaching includes courses such as Discrete Mathematics (MACM 101) and Directed Reading (CMPT 894).

Thomas Wiesمشاهده پروفایل

استاد

• Program Analysis
• Program Verification
• Automated Deduction
• +۳ مورد دیگر

Thomas Wies is a Professor of Computer Science at the Courant Institute, New York University, and Chair of the Computer Science Department. His research focuses on program analysis and verification, automated deduction, and concurrency. University : New York University School : Courant Institute Department : Computer Science Academic Rank : Professor His research explores foundational techniques for verifying concurrent systems, including separation logic, shape analysis, and SMT-based methods. Key trends in his recent work include advancements in symbolic verification for concurrent data structures, multiparty protocol analysis, and linearizability proof tools. Scientific awards he has received include: Best Paper Award, ISSRE 2019 Best Paper Award, ACM SIGPLAN OOPSLA 2014 He has advised numerous PhD students and co-advised alumni, including Devora Chait-Roth, Mark Goldstein, Ekanshdeep Gupta, and others. His teaching portfolio includes graduate and undergraduate courses on programming languages, concurrency, and program verification. Professional roles include organizing conferences like VerifyThis 2025 and chairing program committees for VMCAI and ESOP.

Laurent Doyenمشاهده پروفایل

پژوهشگر

• Game Theory
• Automata Theory
• Formal Verification
• +۷ مورد دیگر

Laurent Doyen is a CNRS Researcher at the Laboratoire Méthodes Formelles (LMF), ENS Paris-Saclay. He holds a PhD from the Université Libre de Bruxelles (2006) and an HDR from ENS Cachan (2012). His research focuses on formal methods, game theory, automata, and verification of quantitative and probabilistic systems. PhD: Université Libre de Bruxelles, 2006 HDR: ENS Cachan, 2012 Research Interests: Laurent's work centers on algorithms and tools for the verification and synthesis of reliable software, hardware, and embedded systems. His primary areas include game and automata theory, with a focus on discrete quantitative and probabilistic systems, timed and hybrid systems. He investigates synchronization, mean-payoff objectives, and imperfect information in games, contributing significantly to theoretical foundations and practical tools. The recent publications highlight a strong trend in stochastic games, synchronization in Markov decision processes, and quantitative verification. His work spans theoretical computer science, formal methods, and practical applications in system design, often appearing in top venues like LICS, ICALP, and CONCUR. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Laurent has advised several PhD students including Mahsa Shirmohammadi, Julien Reichert, Thomas Soullard, and Pranshu Gaba. He leads and participates in multiple research projects such as QuaVerif (PI), IFCPAR SMILeS (coPI), Cassting, ARiSE, and Quasimodo. He has been a Rutherford Visiting Fellow at the University of Warwick and is involved in various academic communities like GAMES, CFV, and GDR-IM. Labs and Teams: He is a key member of the Laboratoire Méthodes Formelles (LMF) at ENS Paris-Saclay and has been associated with the LSV (Laboratoire Spécification et Vérification) in the past. He contributes to the development of tools like Alaska and Alpaga for automata analysis and model checking.

Elena Umiliمشاهده پروفایل

استادیار

• Neurosymbolic AI
• Deep Learning
• Deep Reinforcement Learning
• +۳ مورد دیگر

Elena Umili is an Assistant Professor (RTD-A) at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome, specializing in Neurosymbolic AI research. Her work bridges deep learning with symbolic reasoning systems. She received her PhD in Engineering in Computer Science from Sapienza University of Rome in 2023 under the supervision of Prof. Giuseppe De Giacomo and Prof. Roberto Capobianco. Her doctoral thesis focused on 'Discovering Logical Knowledge in Non-Symbolic Domains'. Her research interests center on Neurosymbolic AI integration , particularly: Combining deep machine learning with symbolic reasoning Temporal logic specifications in neural systems Automata learning through neural relaxations Non-Markovian reinforcement learning tasks Visual grounding of logical specifications Analysis of her recent publications reveals a strong focus on neural-symbolic integration where she develops frameworks like DeepDFA and Neural Reward Machines. Her work consistently addresses the challenge of incorporating logical constraints into deep learning systems, with applications spanning robotics, sequence generation, and visual reasoning. The research demonstrates increasing sophistication in handling temporal logic specifications within neural architectures. She is an active member of research groups focused on Artificial Intelligence and Knowledge Representation, as well as Artificial Intelligence and Robotics at Sapienza University. While no formal advisees or major awards are currently documented in her public profile, her recent publications indicate significant contributions to the neurosymbolic AI field through top-tier conferences including ECAI, KR, and specialized workshops. Her work shows strong potential for future impact in bridging the gap between neural and symbolic AI paradigms.

Tingjian Geمشاهده پروفایل

استاد

• Data Management
• Uncertain Data
• Scientific Data
• +۶ مورد دیگر

Tingjian Ge is a Professor at the Miner School of Computer & Information Sciences , part of the Kennedy College of Sciences at the University of Massachusetts Lowell. He serves as the Graduate Coordinator for MS Programs and specializes in data management systems, focusing on probabilistic and uncertain data, scientific data, and data security and privacy. Education : B.E. (Tsinghua University), M.S. (University of California, Davis), Ph.D. (Brown University) His research spans data management, with emphasis on uncertain data processing, graph stream analysis, and blockchain applications. Recent work includes methods for real-time predictive event monitoring, fairness-aware analytics in dynamic networks, and security enhancements for blockchain systems. Key publication trends highlight contributions to resource optimization in graph streams, recommender systems, and cybersecurity. His expertise intersects database theory, privacy-preserving techniques, and networked data analysis.

Thomas W. Repsمشاهده پروفایل

استاد

• Program analysis
• Abstract interpretation
• Model checking
• +۷ مورد دیگر

Thomas W. Reps is the J. Barkley Rosser Professor & Rajiv and Ritu Batra Chair in the Computer Sciences Department of the University of Wisconsin-Madison, where he has been a faculty member since 1985. He is also President of GrammaTech, Inc., which he co-founded in 1988 to commercialize research on programming environments. Reps received his Ph.D. in Computer Science from Cornell University in 1982, winning the ACM Doctoral Dissertation Award. His research spans program analysis, abstract interpretation, model checking, programming languages, and computer security. Reps's most influential work includes pioneering program slicing techniques with Susan Horwitz, development of the Cornell Program Synthesizer and Synthesizer Generator with Tim Teitelbaum, and significant contributions to interprocedural dataflow analysis. His recent work extends into quantum computing and advanced program synthesis techniques. The analysis of his publications reveals consistent focus on program analysis techniques, with recent papers exploring quantum circuit verification, abstract transformer synthesis, and program synthesis. His work bridges theoretical foundations with practical applications, often addressing scalability challenges in program analysis. NSF Presidential Young Investigator Award (1986) Packard Fellowship (1988) ACM Fellow (2005) ACM SIGPLAN Programming Languages Achievement Award (2017) Elected foreign member of Academia Europaea (2013) Multiple best paper awards including ETAPS (2004, 2008) Reps has advised numerous students, several of whom have received departmental and national recognition. His work on the Wisconsin Program-Slicing Tool led to commercial products through GrammaTech. He has served on advisory boards for significant projects including Year 2000 remediation for the Department of Defense and F/A-22 avionics software integration.

• Probability Theory
• Statistical Physics
• Mathematical Physics
• +۴ مورد دیگر

Hugo Duminil-Copin is a Permanent Professor at the Institut des Hautes Études Scientifiques (IHES) since 2016. His research lies at the intersection of probability theory and statistical physics, focusing on critical phenomena in lattice models such as Ising, Potts, percolation, and self-avoiding walks. Fields of Interest: Probability Theory, Statistical Physics, Percolation, Critical Behavior, Mathematical Physics Scientific Awards : Fields Medal (2022) Dobrushin Prize (2019) ERC Starting Grant 'CriBLaM' (2017) European Mathematical Society Prize (2016) Recent Publications highlight his work on critical exponents, conformal invariance, and phase transitions across diverse models (Ising, random-cluster, six-vertex) using probabilistic and geometric methods. He has also advanced the understanding of Gaussian free fields and their connection to percolation theory.

David N. Jansenمشاهده پروفایل

دانشیار

• Computer Science
• Formal Methods
• Model Checking
• +۵ مورد دیگر

David N. Jansen is an Associate Professor at the Institute of Software , Chinese Academy of Sciences , Beijing, China. Previously, he held an Assistant Professor position at Radboud University Nijmegen (2007–2016) and a Postdoc role at RWTH Aachen University (2007). His work bridges academic research, software development, and international collaboration. Education : PhD in Computer Science from University of Twente (1998–2003); Diploma in Mathematics (with minors in Computer Science and Comparative Linguistics) from University of Bern (1990–1997); Certificate of Proficiency in English from University of Cambridge (2000). Research interests include stochastic model checking , UML extensions for probabilistic systems , Markov reward models , and formal verification of embedded systems . He has contributed to tools like MRMC and TCM , focusing on efficient data structures and lumping techniques for system validation. Publication trends show expertise in formal methods , probabilistic verification , and equivalence relations . His work spans automated analysis of probabilistic programs , stuttering equivalence algorithms , and applications to real-world systems like the European Train Control System. Leadership and Service : He has served on doctoral advisory boards (SIKS), supervised student projects (e.g., XML/XMI interface for TCM), and volunteered in Christian organizations. His technical skills include C, C++, UML, model checking, and database systems like MySQL.

Manfred Jaegerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +۵ مورد دیگر

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

• Theoretical Computer Science
• Formal Methods
• Artificial Intelligence
• +۳ مورد دیگر

M.Sc. Pascal Esser is a researcher at the Department of Informatics at Technical University of Munich (TUM). He specializes in theoretical computer science, formal methods, and machine learning, with a focus on neural networks and verification techniques. His teaching responsibilities include courses on theoretical computer science fundamentals such as Petri Nets, Automata and Formal Languages, Logic, and Model Checking. He has contributed to research in representation learning, graph neural networks, and probabilistic models, as evidenced by his recent publications. Esser is involved in the development of tools like Automata Tutor and has collaborated on projects such as PaVeS and ConVeY. His work bridges formal methods and artificial intelligence, emphasizing rigorous theoretical foundations while exploring practical applications in neural network verification and algorithm design. Education: Master of Science in Computer Science (degree details unspecified). Research Interests: Formal verification, machine learning theory, neural networks, representation learning, graph algorithms, and theoretical computer science. Professional Activities: Active in teaching advanced undergraduate and graduate courses since 2020, with a focus on foundational topics in informatics and emerging areas like neural network verification. Egger's research trends emphasize interdisciplinary approaches, combining insights from statistical learning theory with algorithmic analysis to address challenges in modern AI systems. His publications highlight advancements in understanding model dynamics, kernel-based methods, and graph neural network architectures. While no specific grants or awards are listed, his sustained academic contributions indicate active engagement in the informatics research community. He is part of a research group at TUM including notable figures like Javier Esparza and Jan Křetínský, contributing to tools and frameworks for automata theory and model checking. His work often intersects with practical software implementations such as the Automata Tutor educational platform and Strix verification tools.