Find Academic Supervisors

• Formal Verification
• Model Checking
• Parity Games
• +5 more

Tom van Dijk serves as an Assistant Professor in the Formal Methods and Tools group at the University of Twente, where he conducts cutting-edge research in formal verification and develops practical software tools for the verification community. His work bridges theoretical computer science with real-world applications in model checking and synthesis. His educational background includes: PhD in Computer Science (2016), University of Twente. Thesis: Sylvan: Multi-core Decision Diagrams MSc in Computer Science (2012, cum laude), University of Twente. Thesis: The parallelization of binary decision diagram operations for model checking BSc in Computer Science (2010), University of Twente. Thesis: Analysing And Improving Hash Table Performance Van Dijk's research centers on formal verification with specialization in parity games and binary decision diagrams . He pioneers multi-core parallel algorithms for symbolic model checking, focusing on practical implementations that scale to industrial problems. His work integrates SAT/SMT solving , reactive synthesis , and algorithm optimization to advance the state-of-the-art in verification tooling. His publication trajectory reveals consistent innovation in parity game solving, evolving from foundational work on tangle-based algorithms to recent contributions in reproducibility and AI-aided verification. The 2024 papers demonstrate expanding scope into educational technology while maintaining core focus on game-solving efficiency and synthesis techniques. His key recognitions include: Dutch national M&I Informatie Scriptieprijs 2012 (2nd place) for MSc thesis Best paper award at SPIN 2017 for distributed BDD research Van Dijk actively mentors students and seeks collaborations: Student Supervision : Welcomes BSc/MSc students for projects on parity games and BDDs Tool Development : Maintains open-source research tools (Sylvan, Oink, Knor) Community Service : Serves on 20+ program committees including CAV and TACAS As core member of the Formal Methods and Tools group, he contributes to major verification frameworks including LTSmin, Storm, and IscasMC. His Lace work-stealing framework underpins parallelization in multiple verification tools, while ongoing projects focus on polynomial-time parity game solutions and AI integration in verification workflows.

• Nonlinear Systems
• Control Theory
• Reachability Analysis
• +3 more

Jianqiang Ding is a Doctoral Researcher at Aalto University's School of Engineering, Department of Electrical Engineering and Automation. His work focuses on nonlinear systems and control theory, particularly in reachability analysis for dynamical systems verification and controller synthesis. His research interests include: Reachability analysis for safety-critical systems Formal verification of nonlinear control systems Developing computational tools like PyBDR for set-boundary based analysis Controller synthesis for multi-input multi-output systems Image processing techniques for discernible mosaics Recent publications demonstrate expertise in: Formal methods for system verification Control theory applications in engineering Computational tool development for control systems His collaborative work appears in venues like Formal Methods Symposium and Computer Aided Verification Conference, with particular emphasis on mathematical rigor in control design.

Clark BarrettView profile

Professor

• Formal Methods
• Automated Reasoning
• Verification
• +7 more

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.

Rupak MajumdarView profile

Researcher

• Formal Verification
• Computer-Aided Verification
• Reactive Systems
• +10 more

Rupak Majumdar is a Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS), with a distinguished career in formal verification, control systems, and programming languages. His research spans reactive, real-time, hybrid, and probabilistic systems, focusing on verification and synthesis problems in distributed and concurrent environments. Majumdar earned his B.Tech. in Computer Science from IIT Kanpur (where he received the President’s Gold Medal) and his Ph.D. from UC Berkeley (awarded the Leon O. Chua Award). His work has been recognized through prestigious honors including an NSF CAREER Award, Sloan Fellowship, ERC Synergy Grant, and Most Influential Paper Awards from PLDI and POPL. Research Areas: Formal Verification, Hybrid Systems, Stochastic Systems, Programming Languages, Automata Theory Leadership: Scientific Director, MPI-SWS; ISEC 2026 PC Chair His publications address critical problems in software verification, with recent work focusing on probabilistic systems, distributed protocol testing, and reinforcement learning for formal methods. Awards and grants reflect his impact on computer science and software engineering. Current students include Mahmoud Salamati, Ashwani Anand, V.R. Sathiyanarayana, and Mohammad Khoshechin, while graduated advisees hold positions at institutions like Amazon, Google, and academic research centers. Scientific Awards: President’s Gold Medal (IIT Kanpur) Leon O. Chua Award (UC Berkeley) NSF CAREER Award Sloan Foundation Fellowship ERC Synergy Award Distinguished Alumnus Award (IIT Kanpur) Most Influential Paper Awards (PLDI, POPL) Best Paper Awards (SIGBED, EAPLS, SIGDA)

Eva DarulovaView profile

Associate Professor

• Programming Languages
• Software Verification
• Program Synthesis
• +3 more

Eva Darulova is an Associate Professor at the Department of Information Technology, Uppsala University, and Adjunct Faculty at Max Planck Institute for Software Systems (MPI-SWS). Previously held tenure-track faculty at MPI-SWS and completed her PhD at École Polytechnique Fédérale de Lausanne (EPFL) under Viktor Kuncak. Current research: Applications in numerical/embedded domains, program synthesis, software verification, approximate computing Key projects: Blossom (fuzzing), Lassie (HOL4 tactics), Pine (floating-point loops), LTLTalk (robot instruction), Incarnate (3D printing), Icing (compiler semantics), Flipper (NLP interface), FloVer (error checking), Daisy (accuracy analysis), Rosa (real compiler) Teaching: Program Design (2021/22), Program Synthesis (2021), Program Analysis (multiple years), Advanced Program Analysis (2019), Static Program Analysis (2017), Approximate Computing seminar (2016) Service: Artifact evaluation chair (ASPLOS'22), PC member (ECOOP'22, ESEC/FSE'22, EMSOFT'21, PLDI'21) Research Trends: Focus on numerical computing verification, approximate computing frameworks, and program synthesis techniques. Publications span TACAS, EMSOFT, ISSTA, POPL, CAV, FMCAD, TOPLAS, with emphasis on floating-point error analysis, mixed-precision optimization, and compiler verification. Scientific Recognition: Nominated for EAPLS best paper award (TACAS'21). Co-advisor for Anastasiia Izycheva and mentor for multiple MSc/BSc students including Mustafa Hafidi, Joachim Bard, Nikita Zyuzin, Youcef Merah, Safya Alzayat, Anastasiia Izycheva. Collaborations: Works with researchers at EPFL, MPI-SWS, University of Saarland, University of Washington, and Saarbrücken/Kaiserslautern institutions.

Bruno BuchbergerView profile

Research Professor

• Symbolic Computation
• Computer Algebra
• Automated Theorem Proving
• +3 more

Roles & Affiliations: Bruno Buchberger is a Research Professor at the Research Institute for Symbolic Computation (RISC) and the University of Linz (Johannes Kepler University), Austria. He is also the Honorary Professor at the Technical University of Vienna and the founder of the Softwarepark Hagenberg. He has held visiting positions at institutions worldwide, including Kyoto University, Texas A&M, and the University of Timisoara. Head of Softwarepark Hagenberg Member of Academia Europaea, London Corresponding Member of the Bavarian Academy of Science Education: PhD in Mathematics (1966), University of Innsbruck, under Wolfgang Gröbner Matura Exam (1960), Realgymnasium Angerzellgasse, Innsbruck Research Focus: Buchberger is renowned for inventing the Gröbner Bases theory and developing the Theorema system for natural-style mathematical reasoning. His work bridges symbolic computation, automated theorem proving, and algorithm synthesis. Key areas include: Algorithmic Mathematics Computer Algebra Systems Formal Methods Mathematical Theory Exploration Article Trends: Recent works explore automated theorem proving, AI integration in mathematics education (e.g., ChatGPT analysis), and algorithmic methods for special functions (e.g., Ramanujan-Sato series). His contributions emphasize interdisciplinary applications in software science and computational mathematics. Awards: ACM Kanellakis Award (2007) Austrian Cross of Honors (2003) Austrian of the Year (2010) Multiple honorary doctorates (Bath, Nijmegen, Timisoara) Grants & Teams: Led the Gröbner Bases Special Semester 2006 at RICAM. Involved in the SFB13 consortium for Scientific Computing. Collaborated with the Japanese Society for Symbolic Computation and the Radon Institute. Labs & Initiatives: Founded RISC (1987) and Softwarepark Hagenberg (1991), catalyzing Austria’s tech ecosystem. Key projects include the Theorema system and educational programs like the Informatics Master’s program in Hagenberg.

Nikolaj PopovView profile

Research Professor

• Program Verification
• Symbolic Computation
• Formal Methods
• +3 more

Nikolaj Popov is a Research Professor at the Research Institute for Symbolic Computation (RISC) of Johannes Kepler University, Linz, Austria. His work focuses on program verification, formal methods, and automated reasoning, particularly within the Theorema system. He specializes in verifying functional and recursive programs, combining symbolic computation with logical techniques to ensure correctness and termination. Education: PhD in Computer Science, RISC, Johannes Kepler University (2008) Research Interests: Popov’s research bridges theoretical computer science and practical applications, emphasizing automated theorem proving, program analysis, and formal verification. He develops methods for verifying complex program structures like mutual recursion and nested recursion, leveraging computer algebra systems. His work also explores the integration of formal methods into software engineering workflows. Key Contributions: Popov has co-authored foundational papers on verification condition generation, termination proofs, and automated debugging frameworks. His collaborations with Tudor Jebelean and others have advanced Theorema’s capabilities in handling functional programs and bridging logical and algebraic methodologies. Labs/Teams: Core member of RISC, contributing to its mission in symbolic computation and mathematical theory exploration.

• Synthetic Biology
• Biomedical Engineering
• Systems Biology
• +7 more

Professor Richard Ian Kitney is Professor of Biomedical Systems Engineering in the Department of Bioengineering within Imperial College London's Faculty of Engineering. He serves as Chairman of the Institute of Systems and Synthetic Biology and Co-Director of the EPSRC National Centre for Synthetic Biology and Innovation. His institutional affiliations include the Centre for Synthetic Biology, Centre for Translational Nutrition and Food Research, and the Industrial Biotechnology Hub CsynBI. His academic qualifications include a DSc (Eng) from the University of London and a PhD in Electrical Engineering from Imperial College London. He holds professional designations as Chartered Engineer (CEng), Fellow of the Institution of Electrical Engineers (FIEE), and Chartered Scientist (CSci). Kitney's research focuses on synthetic biology, systems biology, and biomedical engineering, with emphasis on mathematical modeling, medical imaging, and engineering biology principles. He pioneered the integration of automation and standardization in biological design, establishing Imperial College as a global leader in synthetic biology through the development of the Imperial College Hub for Synthetic Biology and SynbiCITE, the UK's national industrial translation center. His work bridges fundamental research with industrial applications in biomanufacturing and sustainable solutions. Analysis of his recent publications reveals a decisive shift toward engineering biology as a systematic discipline, characterized by automation-driven workflows, biofoundry infrastructure development, and applications addressing global challenges including pandemic response and sustainable vaccine production. Key trends include standardization of biological parts, computational design tools, and policy frameworks enabling the advanced bioeconomy. His distinguished honors include: Order of the British Empire (OBE) for services to Information Technology in Biomedicine (2001) Fellowship in the World Technology Network (1999) Academician of the International Academy of BioMedical Engineering (2003) Honorary Fellowships of The Royal College of Physicians and Surgeons (2006) Fellowships of The Royal Academy of Engineering and Royal Society of Edinburgh Kitney has secured transformative funding through leadership of national initiatives including the EPSRC National Centre for Synthetic Biology and Innovation and SynbiCITE. He chaired The Royal Academy of Engineering Inquiry into Synthetic Biology and co-authored the UK Roadmap for Synthetic Biology, establishing critical policy frameworks. His grants portfolio emphasizes industrial translation, biofoundry development, and building the UK's engineering biology capability. He directs the Imperial College Synthetic Biology Hub (CsynBI) and The Foundry DNA synthesis facility, which form the core of Imperial's engineering biology infrastructure. These centers operate as integrated platforms connecting academic research with industrial applications through standardized workflows, automated design-build-test cycles, and collaborative projects with pharmaceutical and biotechnology companies.

• Computer Vision
• Medical Imaging
• Deep Learning
• +4 more

Zhifeng Wang is a Professor at the National University of Defense Technology's School of Computer Science, specializing in computer vision, medical imaging, and artificial intelligence. His research spans multiple application domains with a recent focus on medical image synthesis, 3D reconstruction, and fluid dynamics simulation. Research interests include developing advanced AI techniques for medical imaging applications, particularly in vascular structure analysis and synthesis. His work bridges computer vision with healthcare applications, focusing on creating anatomically accurate 3D representations without invasive procedures. He has made significant contributions to diffusion models, neural radiance fields (NeRFs), and meta-learning frameworks for cross-domain applications. His publication record shows a clear progression from foundational work in graph algorithms and communications systems to cutting-edge medical AI applications. Recent work demonstrates expertise in combining state space models with diffusion techniques for medical image generation, showing particular strength in maintaining anatomical continuity in 3D vascular structures. While specific awards aren't documented in the provided information, his publications in top venues including CVPR 2025 indicate significant recognition in the computer vision community. His collaborative work spans multiple institutions and research domains, suggesting an active research group with diverse funding sources. His laboratory appears to focus on medical imaging AI, with particular emphasis on non-invasive diagnostic techniques. Current projects involve developing sophisticated diffusion models that can generate high-fidelity angiographic images from non-contrast inputs, potentially reducing patient exposure to harmful contrast agents.

• Wireless Communications
• 5G/6G Channel Modeling
• UAV Communications
• +5 more

Ziwei Huang is an Assistant Professor at Southeast University's School of Information Science and Engineering, Department of Communication Engineering. With a prolific publication record from 2019-2025, Huang has established expertise in wireless communications, particularly in 5G/6G channel modeling, UAV communications, and vehicular networks. Recent work demonstrates a strategic expansion into AI/ML applications, computer vision, and large language models, showing interdisciplinary research growth. Huang's research interests focus on wireless communications channel modeling with particular emphasis on non-stationary characteristics, spatial consistency, and trajectory modeling for next-generation communication systems. Key areas include UAV communications , vehicular networks , intelligent sensing-communication integration , and multi-modal data fusion . Recent work has expanded into fundus image processing , text-to-image synthesis , and hallucination mitigation in vision-language models , demonstrating a strategic expansion into AI applications while maintaining core expertise in communications. The publication trends reveal a clear evolution from traditional wireless channel modeling (2019-2021) toward more integrated sensing-communication systems (2022-2023), with a significant pivot toward AI/ML applications in 2024-2025. Huang's work spans both theoretical channel modeling and practical implementation, with increasing emphasis on cross-disciplinary applications. The research shows strong collaboration patterns with Xiang Cheng and Lu Bai, suggesting membership in a well-established research group at Southeast University. Huang has contributed to numerous high-impact publications in IEEE journals including IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, and IEEE Communications Surveys & Tutorials, as well as top conferences like AAAI and ACL. The research demonstrates consistent funding support through collaborative projects focused on next-generation wireless communication systems. The work spans multiple laboratories and research teams, including wireless communications research groups at Southeast University, collaborations with medical imaging researchers for fundus analysis, and partnerships with AI research teams working on vision-language models. Recent publications suggest active participation in interdisciplinary research initiatives bridging communications engineering with artificial intelligence.

Arie GurfinkelView profile

Professor

• Automated Program Analysis
• Software Model Checking
• Automated Reasoning
• +4 more

Arie Gurfinkel is a Professor at the University of Waterloo, holding a joint appointment in the Department of Electrical and Computer Engineering and the Cheriton School of Computer Science. His research focuses on automated program analysis, software model checking, automated reasoning, and abstract interpretation. He develops tools like SeaHorn, Avy, and others to enhance the verification and testing of complex computer systems. His work emphasizes formal methods, machine learning integration, and hardware/software verification. Recent publications highlight advancements in interpolation-based model checking, constrained Horn clauses, and algorithm selection for hardware verification. Gurfinkel's contributions include open-source tools and frameworks widely used in academic and industrial verification efforts. He actively seeks motivated graduate students interested in logic, automated reasoning, and formal methods. His research has been presented in top-tier conferences and journals, reflecting his expertise in formal verification and software engineering.

• Formal Methods
• Automated Reasoning
• Inductive Logic
• +3 more

Marton Hajdu is a PostDoc Researcher at the Vienna University of Technology , affiliated with the Systems Engineering department under the Formal Methods in Systems Engineering group (E192-04). His research focuses on formal methods , automated reasoning , and inductive logic . He actively contributes to projects such as ARTIST (2021–2026) , ForSmart (2023–2027) , and SFB SPyCoDe (2023–2026) , which explore recursive programming, saturation-based reasoning, and inductive benchmarks. His work intersects superposition calculus , term rewriting , and formal verification . His recent publications highlight advancements in inductive reasoning , recursive program synthesis , and constraint solving using saturation techniques. These contributions align with broader trends in automated deduction and logic programming for computer-aided verification. Marton Hajdu holds a Diploma Thesis from TU Wien (2020) titled Automating inductive reasoning with recursive functions , establishing his expertise in formal methods and recursive logic. He collaborates with researchers like Laura Kovács and Andrei Voronkov , and his work is supported by projects spanning formal reasoning, smart systems, and theoretical computer science.

Ming MaView profile

Assistant Professor

• Artificial Intelligence
• Medical Imaging
• Large Language Model
• +3 more

Ming Ma is an Assistant Professor in the Department of Graduate Computer Science and Engineering at the Katz School of Science and Health, Yeshiva University. He leads the Medical Imaging and Artificial Intelligence lab. He holds a Ph.D. in Computer Science from Stony Brook University and completed a postdoctoral fellowship at Stanford University. His research focuses on integrating artificial intelligence with medical imaging, particularly in areas like medical image segmentation, deep learning applications in healthcare, and geometric modeling for biomedical systems. Education: Ph.D. in Computer Science, Stony Brook University, USA Postdoctoral Scholar, Stanford University, USA Research Interests: His work spans Artificial Intelligence , Medical Imaging , Large Language Models , Geometric Modeling , and Computer Vision . He develops novel algorithms for medical image analysis, including techniques for dose prediction in radiation therapy, pulmonary nodule detection, and segmentation of vascular structures. Recent projects emphasize the application of graph convolutional networks and optimal transport methods in low-dose CT imaging. Conference Contributions: He serves on program committees for conferences like AAAI 2022, ICONIP 2024, and CHASE 2025, reflecting his leadership in advancing AI and biomedical engineering research. Labs/Teams: His lab at Yeshiva University focuses on interdisciplinary research at the intersection of AI, medical imaging, and computational anatomy, with a commitment to translating algorithmic innovations into clinical applications.

Abdelhamid TayebiView profile

Professor

• Control Systems Theory
• Robotics
• Autonomous Vehicles
• +7 more

Abdelhamid Tayebi is a Distinguished Professor and Research Chair in the Department of Electrical and Computer Engineering at Lakehead University. He also holds a courtesy professorship at Western University. His research focuses on control systems, autonomous navigation, and robotics, with applications to UAVs and multi-agent systems. Key roles include Graduate Coordinator and founder/director of the Robotics & Automatic Control Laboratory. Education: B.Sc. in Electrical Engineering, Ecole Nationale Polytechnique (1992) M.Sc. in Robotics, Université Pierre & Marie Curie (1993) Ph.D. in Robotics and Automatic Control, Université de Picardie Jules Verne (1997) Research Interests: Control systems theory, iterative learning control, autonomous vehicles, UAV navigation, hybrid feedback control, and nonlinear state estimation. His work emphasizes provable stability guarantees and real-world applications. Recent Articles Trends: Focus on hybrid control for obstacle avoidance, distributed estimation in multi-agent systems, and nonlinear observers for inertial navigation. Publications span IEEE Transactions on Automatic Control , Automatica , and robotics conferences like CDC and ACC. Awards: 2024 Distinguished Instructor Award (Lakehead University's top teaching honor) 2023 IEEE Fellow and EIC Fellow NSERC Discovery Accelerator Supplements (2013) Top 2% Highly Cited Scientist (Stanford, 2019–) Students & Labs: Advised over 20 PhD/MSc students and postdocs. Current research group includes postdocs Mayur Sawant (autonomous robotics) and Ishak Cheniouni (navigation algorithms). Notable former students include Miaomiao Wang (geometric observers) and Mouaad Boughellaba (multi-agent systems). Grants & Editorial Work: Holder of NSERC grants and Lakehead Research Chairs (2012–2027). Associate Editor for Automatica , IEEE Transactions on Control Systems Technology , and others. Active in reviewing for top journals/conferences.

Armando Solar-LezamaView profile

Professor

• Program Synthesis
• Neurosymbolic Programming
• Machine Learning
• +5 more

Armando Solar-Lezama is a Professor at the MIT Schwarzman College of Computing , Associate Director and COO of MIT CSAIL, and leads the Computer-Aided Programming Group . He earned his BS in Computer Science and Mathematics from Texas A&M University and PhD (2008) from UC Berkeley under Rastislav Bodik. His research focuses on program synthesis at the intersection of Programming Systems and Artificial Intelligence, with recent work on neurosymbolic programming. Education: BS (Texas A&M), PhD (UC Berkeley) Labs: CSAIL, Center for Deployable Machine Learning His research explores automated reasoning and learning to reduce programming effort, including the development of the Sketch programming language. Current neurosymbolic work combines deep learning with logical reasoning for applications in multi-agent systems, RNA splicing, and interpretable policy generation. Recent projects include VLMaterial for procedural asset generation and CRUXEval for code evaluation benchmarks. Scientific awards include: Robin Milner Young Researcher Award (2024) Best Paper (PLDI 2005, PPoPP 2016) Outstanding Paper (EMNLP 2023) He advises graduate students through MIT's EECS PhD program and has developed courses like 6.820 Foundations of Program Analysis and Program Synthesis . His work impacts software synthesis automation, programming language design, and deployable machine learning.