Find Academic Supervisors

Chuang GanView profile

Assistant Professor

• Computer Vision
• Graph Deep Learning
• Multimodal Learning
• +5 more

Chuang Gan is a distinguished researcher holding dual positions as a Principal Research Staff Member at the MIT-IBM Watson AI Lab and an Assistant Professor at the University of Massachusetts Amherst. His work bridges academic research and industrial applications in artificial intelligence, with particular focus on advancing the frontiers of computer vision and multimodal learning systems. Dr. Gan's research interests span multiple interconnected domains within artificial intelligence. He specializes in video understanding, with deep expertise in representation learning, neural-symbolic visual reasoning, audio-visual scene analysis, and embodied intelligence. His work frequently integrates graph deep learning techniques with neuro-symbolic approaches to create more interpretable and robust AI systems. The recurring themes across his research portfolio include developing models that can understand physical dynamics from visual inputs, creating systems capable of embodied reasoning, and building bridges between symbolic and neural approaches to artificial intelligence. His publications reveal a strong trend toward increasingly sophisticated multimodal systems that integrate visual, auditory, and linguistic information. Over time, his work has evolved from basic video understanding tasks to complex embodied reasoning systems capable of physical simulation, 3D scene understanding, and multi-agent collaboration. A notable pattern is the progression from analyzing static scenes to understanding dynamic physical interactions and embodied agent behaviors in increasingly complex environments. Microsoft Fellowship Baidu Fellowship Dr. Gan's research has received significant recognition from major technology companies through prestigious fellowships and has been widely covered by leading media outlets including CNN, BBC, The New York Times, WIRED, Forbes, and MIT Tech Review. His work at the MIT-IBM Watson AI Lab provides him with access to substantial resources for cutting-edge AI research, while his academic position enables him to train the next generation of AI researchers. His collaborations with prominent researchers like Antonio Torralba demonstrate his integration within the top echelons of the computer vision and AI research community. At the MIT-IBM Watson AI Lab, Dr. Gan leads research initiatives focused on advancing video understanding and embodied intelligence. His work contributes to the lab's mission of developing AI systems that can perceive, reason about, and interact with the physical world in more human-like ways. His research group likely focuses on developing novel architectures for multimodal learning, creating benchmarks for physical reasoning, and building systems that can transfer knowledge between simulation and real-world environments.

• Communication Studies
• Political Rhetoric
• Algorithmic Governance
• +3 more

Marcus Paroske is an Associate Professor in the Department of Language and Communication at the College of Arts, Sciences and Education, The University of Michigan-Flint. He teaches courses such as Intro to Human Communication and Lifelong Learning Capstone . Ph.D. in Communications (2006) His scholarly work bridges political communication, algorithmic governance, and science policy. Research focuses include redistricting debates, argumentation theory, and the rhetoric of public policy. Recent publications analyze algorithmic redistricting (2025) and deliberative spaces (2024). Key themes across his work involve: Algorithmic decision-making in policy Deliberative democracy and citizen participation Communication in science regulation Rhetoric of uncertainty in global health Assessment and pedagogy in communication education

Klaus SteinView profile

Part-Time Lecturer

• Digital Heritage Technologies
• Heritage Conservation
• Geographic Information Systems (GIS)
• +6 more

Klaus Stein is a part-time lecturer at the Chair of Heritage Conservation , Otto-Friedrich University of Bamberg. His work bridges digital technologies , spatial modeling , and cultural heritage through interdisciplinary research projects. Co-leader of the UrbanMetaMapping project (BMBF-funded) analyzing WWII war damage maps Developed KTF (Wiki-based terminology framework) for SMEs Contributed to COM (Communication-Oriented Modeling) DFG research network Active in EMN-MOVES project for age-friendly mobility solutions His research focuses on digital heritage , social network analysis , and collaborative GIS , with over 20 years of publications in spatial cognition and information systems . While his primary role involves teaching in the Master's program in Digital Heritage Technologies , he also contributes to open-source GIS and web mining methodologies. Key projects include: Find Diversity (BioDiv2Go): Promoting biodiversity awareness via location-based games Virtual Spaces : Digital reconstruction of historical church color schemes

Jun LiuView profile

Professor

• Artificial Intelligence
• Trustworthy AI
• Explainable AI
• +4 more

Professor Jun Liu is a Professor of Artificial Intelligence and Director of the Artificial Intelligence Research Centre (AIRC) at the School of Computing, Ulster University. With over 270 publications and more than £18 million in research funding, he is a leading figure in artificial intelligence, particularly in trust and explainable AI systems and logic-based reasoning methods. Dr. Liu received his BSc and MSc degrees in Applied Mathematics, and PhD degree in Information Engineering from Southwest Jiaotong University, Chengdu, China, in 1993, 1996, and 1999, respectively. Prior to joining Ulster University, he held postdoctoral positions at The University of Manchester, UK (Feb. 2002 - Dec. 2004) and the Belgian Nuclear Research Centre (SCK*CEN) (Mar. 2000 - Feb. 2002). Professor Liu's research focuses on trust and explainable data-knowledge integrated AI decision models with applications in safety and risk analysis, policy decision making, security/disaster management, and healthcare; and logic and automated reasoning methods for intelligent systems, including resolution-based automated reasoning and lattice-valued logics for handling incomparability, inconsistency, and imprecision. His work spans theoretical foundations to practical applications in smart homes, healthcare, and industrial settings. His recent publications demonstrate a strong trend toward developing more trustworthy and explainable AI systems, with particular emphasis on belief rule-based approaches for handling uncertainty in decision-making. The research spans multiple domains including smart home activity recognition, medical imaging, food quality analysis, and environmental monitoring, showing the versatility and applicability of his methodologies. Ulster University best computer science paper award for 2016 IEEE Senior Member including IEEESMC and IEEECI Fellow of the UK Higher Education Academy Associate Editor of IEEE Transaction on Fuzzy Systems Current Chair of IEEE CIS Emergent Technologies Technical Committee As Director of the Artificial Intelligence Research Centre, Professor Liu has secured significant research funding as principal investigator and co-investigator. His current projects include "The use of Agentic AI in judicial decision-making" funded by EPSRC and "Adaptive Modeling Method for Deep Belief Rule Base" for smart home applications. He serves on editorial boards of multiple high-impact journals and organizes international conferences including the 23rd UK Workshop on Computational Intelligence. The Artificial Intelligence Research Centre under Professor Liu's leadership focuses on developing cutting-edge AI methodologies with practical applications. The center collaborates extensively with industry partners including BT through the BTIIC Phase 2 initiative and PwC through their Advanced Engineering and Research Centre, ensuring research has real-world impact across multiple sectors.

• Medical Informatics
• Artificial Intelligence
• Deep Learning
• +3 more

David Bani-Harouni is a researcher at the Chair of Computer Aided Medical Procedures at Technische Universität München (TUM). His work focuses on Medical Informatics , Artificial Intelligence , and Deep Learning , with an emphasis on Clinical Decision Support and Medical Image Analysis . Research Interests : Large Language Models (LLMs), Vision Language Models (VLMs), interpretability in deep learning, multimodal clinical decision support, and medical image analysis. Teaching : He contributes to lectures and practical courses such as Computer Aided Medical Procedures I , Medical Augmented Reality , and Deep Learning for Medical Applications . Publications : His research spans reinforcement learning for clinical decision-making, multimodal operating room datasets, toxin prediction systems (e.g., ToxNet), and graph convolutional networks for intoxication prediction. Contact : david.bani-harouni@tum.de

• Medical applications of vision-language models
• Multimodal deep learning for medical applications
• Holistic clinical decision support systems
• +2 more

Matthias Keicher is a Postdoc and Research Manager at the Chair for Computer Aided Medical Procedures at the Technical University of Munich , affiliated with the IFL Lab at Klinikum Rechts der Isar. His work focuses on deploying AI for clinical applications, particularly vision-language models and large language models for structured report generation and decision support systems. Education: Dipl.-Ing. in Mechanical Engineering and Management from TUM (2006-2013) Industry Experience: Former CTO and Managing Director at SurgicEye GmbH (2016-2018) Research Interests: Medical Vision-Language Models (VQA, structured reporting) Multimodal Deep Learning for diagnostics Interpretable AI with generative models Decision support systems integrating patient data Article Trends: Over 2024-2014, his publications span surgical phase recognition (TeCNO), vertebral fracture grading (iMIMIC best paper), chest X-ray classification (FlexR), radiology report generation (RaDialog), and toxin prediction (ToxNet). Keywords include Medical Imaging, Graph Networks, Language Models , with subfields like 3D Computer Vision, Federated Learning, Clinical Reasoning . Scientific Awards: MICCAI iMIMIC 2023 Best Paper Teaching: He organizes two lectures ( Computer Science for Medical Students , Innovation Generation in Healthcare ) and tutors courses such as Deep Learning for Medical Applications and Machine Learning in Medical Imaging . Labs & Teams: Works at the IFL Lab (Intelligent Future Lab) in Munich, leading a research team funded by the DIVA project focused on vision-language models in clinical settings.

Hussain JamilView profile

Assistant Professor

• User Experience
• User Research
• Social Media Analysis
• +6 more

Hussain Jamil is an Assistant Professor at Sejong University 's Department of Data Science , specializing in User Experience (UX) evaluation , Machine Learning , and Medical Informatics . His work bridges Artificial Intelligence with Healthcare Applications , particularly in DXA image analysis and social media-based mental health detection . Education: Ph.D. in Biomedical Engineering (Kyung Hee University, 2019) MS in Biomedical Engineering (Kyung Hee University, 2014) BCs in Computer Science (Kohat University, 2010) Research interests focus on Machine Learning for Medical Imaging , User Experience , and Text Mining . Key projects include AI-driven osteoporosis diagnosis , depression detection via SNS analysis , and multimodal UX platforms . Recent publications span 2024 research on respiratory disease prediction , Arabic text analysis for pandemic monitoring , and multimodal emotion recognition , alongside foundational work in 2023-2018 on UX evaluation frameworks and knowledge-based systems .

• Building Information Modeling (BIM)
• Model-Based Systems Engineering (MBSE)
• Graph-Based Design
• +3 more

Benedict Harder is a Researcher at the Chair of Computing in Civil and Building Engineering, Technical University of Munich, specializing in Building Information Modeling and Model-Based Systems Engineering. He contributes to the DFG SPP 2187 project on modular concrete bridges and maintains active roles in research and teaching. His educational foundation includes a 2024 Master's thesis on algorithmic design of modular precast structures and a 2021 Bachelor's thesis exploring train station design via parametric modeling. These works established his trajectory in computational civil engineering. Harder's research integrates semantic web technologies with infrastructure design, focusing on SysML-OWL interoperability, graph-based modular construction, and formal methods for BIM. His work bridges computer science formalisms with practical civil engineering challenges, particularly in bridge systems and precast structures. Publication trends reveal consistent advancement in formalizing design processes: early work centered on parametric regulation compliance (2021), evolving to SysML-based bridge representation (2024) and semantic reasoning frameworks (2025). Key themes include graph rewriting for modular design, incremental BIM updates, and MBSE applications in structural monitoring. He supervises graduate research including a 2025 Master's thesis on MBSE for bridge monitoring and teaches courses like Computer-Aided Modeling of Products and Processes. His academic activities align with TUM's BIM-Lab and research groups in Digital Twinning and Knowledge Representation.

• Digital Twinning
• Building Information Modeling (BIM)
• Computer Vision
• +3 more

Mansour Mehranfar is a Researcher at the Chair of Computing in Civil and Building Engineering, Technical University of Munich (TUM). He contributes to advancing AI applications in digital twinning of the built environment through the AI4Twinning research initiative, focusing on automated generation of semantic building models from point cloud data and imagery. His research centers on Digital Twinning, Building Information Modeling (BIM), and Computer Vision, with specific expertise in point cloud processing, semantic segmentation, and 3D reconstruction. He develops AI-driven frameworks that integrate deep learning with geometric modeling to convert raw sensor data into semantically enriched digital representations of buildings and infrastructure components. Recent publications reveal a strong emphasis on staircase modeling, indoor space documentation, and domain adaptation techniques. Key innovations include hybrid top-down/bottom-up approaches for Manhattan-world structures, parametric prototype model fitting, and multi-task learning frameworks that simultaneously handle scene parsing and 3D reconstruction from single images. Dr. Mehranfar actively supervises Master's theses, guiding students in topics such as load-bearing wall detection, staircase modeling automation, and BIM change management. He teaches Engineering Databases at TUM and collaborates within the university's BIM-Lab ecosystem, leveraging facilities for robotic fabrication and mobile machinery research.

• Strategy in uncertainty
• Foresight methodology
• Computer-aided reasoning
• +6 more

Dr. Steven Popper is a Distinguished University Professor in Decision Sciences at Tecnológico de Monterrey, jointly appointed to the School of Social Sciences and Government and the School of Government and Public Transformation. He brings extensive experience from his previous roles as Professor of Science and Technology Policy at the Pardee RAND Graduate School and senior economist at RAND Corporation. His career spans academia, policy research, and entrepreneurship, with significant contributions to decision-making frameworks under conditions of deep uncertainty. Dr. Popper holds a PhD in Economics from the University of California, Berkeley and a Bachelor's degree in Biochemistry from the University of Minnesota, reflecting his interdisciplinary approach to complex problems. His educational background bridges the natural sciences and social sciences, informing his unique perspective on technology policy and decision-making. His research focuses on strategy in contexts of uncertainty, foresight methodologies, computer-aided reasoning, and robust decision making (RDM). He has made significant contributions to science, technology and innovation policy, economic development, emerging technologies, and defense and security. Dr. Popper's work addresses the challenge of making effective decisions when facing multiple plausible futures rather than relying on single-point predictions, which is increasingly relevant in our rapidly changing world. Analysis of Dr. Popper's recent publications (2019-2022) reveals a consistent focus on decision-making under deep uncertainty, with applications spanning technology assessment, national security policy, and innovation capacity evaluation. His work demonstrates an interdisciplinary approach that bridges technical analysis and policy implementation, with increasing emphasis on practical applications of robust decision frameworks across multiple sectors. Co-founder and COO of Evolving Logic Inc., developer of the Computer-Assisted Reasoning (CAR) system Founding president of the education and training division of the Society for Decision Making Under Deep Uncertainty (DMDU) Associate director of the Science and Technology Policy Institute (1996-2001) Chair of the Science and Technology Industry Section of AAAS Dr. Popper has advised numerous organizations including the White House, World Bank, and OECD. His consulting work focuses on applying robust decision frameworks to complex policy challenges, particularly in technology assessment and strategic planning. He has developed methodologies used by government agencies and multinational corporations to navigate uncertainty in critical decision-making processes. While no specific research lab is mentioned, Dr. Popper's work through Evolving Logic Inc. and his academic positions has created significant impact in the field of decision-making under uncertainty. His Robust Adaptive Planning (RAP™) methodology represents a practical application of his theoretical work, providing organizations with tools to develop strategies that remain effective across multiple plausible futures.

Shachar ItzhakyView profile

Associate Professor

• Program Synthesis
• Automated Reasoning
• Functional Programming
• +5 more

Shachar Itzhaky is an Associate Professor in the Department of Computer Science at Technion - Israel Institute of Technology, Haifa. His research spans multiple areas of programming languages, formal methods, and software engineering, with a focus on making program development and verification more accessible and efficient. He has served on program committees for numerous prestigious conferences including PLDI, POPL, SPLASH, and ICFP. Dr. Itzhaky's research interests center around program synthesis, automated reasoning, and formal verification. His work in program synthesis explores techniques for automatically generating programs from high-level specifications, with applications in end-user programming and software development. In automated reasoning, he has made significant contributions to e-graph based reasoning, invariant inference, and property-directed verification. His research in formal methods focuses on practical applications for program verification, particularly for data structures and security properties. An analysis of his recent publications reveals a strong focus on leveraging advanced formal techniques for practical program understanding and generation. His work consistently bridges theoretical foundations with practical applications, particularly in program synthesis, verification, and end-user programming tools. The trend shows increasing integration of machine learning techniques with traditional formal methods, as well as expanding applications to security and privacy domains. ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award Dr. Itzhaky has been actively involved in the programming languages research community, serving on numerous program committees and contributing to the advancement of formal methods and program synthesis. His work has practical implications for software development tools, security analysis, and end-user programming environments. While specific grant information isn't detailed in the provided text, his extensive publication record in top-tier venues suggests successful funding for his research endeavors. His work on projects like Object Spreadsheets and Lifty demonstrates a commitment to creating practical tools that address real-world programming challenges. Dr. Itzhaky's research is conducted within the vibrant programming languages and formal methods group at Technion's Computer Science department. His work intersects with multiple research threads including program synthesis, verification, and security, suggesting collaboration across these areas within the department. His tools like EPR-based Verification, PDR∀, and VeriCon represent significant technical contributions that likely form the basis of ongoing research projects with students and collaborators.

Qunfen QiView profile

Senior Lecturer

• Knowledge modelling for manufacturing
• Smart information systems
• Category theory
• +7 more

Dr. Qunfen Qi is a Senior Lecturer at the University of Bristol's School of Electrical, Electronic and Mechanical Engineering, where she leads groundbreaking research at the intersection of mathematics, informatics, and manufacturing. With over 13 years of experience, she specializes in developing smart decision-making tools for product design and inspection using category theory as a foundational mathematical language. Her work bridges multiple disciplines to create more intelligent and transparent manufacturing systems that can 'think' like human experts. Dr. Qi's research focuses on knowledge modeling for manufacturing, with particular emphasis on smart information systems, abstract mathematical theory (category theory), Geometrical Product Specifications and Verification (GPS), Additive Manufacturing, surface design, and metrology. Her unique approach applies category theory to simplify complex relationships in manufacturing data, enabling better decision-making in digital manufacturing environments. This work has become increasingly relevant as manufacturing embraces advanced digital technologies like cyber-physical systems, IoT, and AI, where managing multiple levels of abstraction is critical. Her publication record shows a clear trajectory toward more sophisticated applications of mathematical frameworks in manufacturing, with growing integration of category theory with AI approaches, particularly in explainable AI for manufacturing contexts. Recent work demonstrates increasing practical applications in additive manufacturing, quality control, and digital twin development, with strong emphasis on how these systems can explain their decisions at multiple levels of detail. Scientific Awards and Recognition UKRI EPSRC Innovation Fellow (2018-2022) EPSRC Peer Review College Member (ranked top 4% for participation in 2020/21) Project lead for ISO/TR 24331-2 Geometrical Product Specification development ISO Technical Committee Expert (TC 213, TC 184 WG26, JWG 21) BSI Technical Committee Member (AMT_4, TPR1/1, TPR1/8, TPR1/9) Co-Chair of CIRP CAT 2024 (18th International Conference on Computer-Aided Tolerancing) Dr. Qi actively recruits PhD students interested in explainable machine learning, mathematical AI foundations, and smart manufacturing applications. Her research portfolio includes four major projects totaling significant funding, including an EPSRC Innovation Fellowship for 'A semantic infrastructure for advanced manufacturing' and a Royal Society International Exchanges project on mathematical foundations for digital manufacturing systems. She maintains strong international collaborations, particularly with Italian institutions like Politecnico di Milano and CNR-STIIMA, reflecting her global impact in advancing the mathematical foundations needed for next-generation intelligent manufacturing systems.

• Biology Education
• Science Education
• Educational Technology
• +5 more

Dr. Marit Kastaun serves as Lecturer for special tasks in the Didactics of Biology at the University of Kassel since November 2023, with continuous affiliation to the department since 2013. She is a core researcher at the FLOX Teaching and Learning Laboratory and contributes to the outdoor education site Freilandlabor Dönche. Her educational background includes: First State Examination for Gymnasium Teaching (Biology/History), University of Kassel (2017) Cumulative PhD in Biology Didactics, University of Kassel (2024) Dr. Kastaun's research pioneers the integration of AI tools in biology teacher education, focusing on adaptive feedback systems for scientific hypothesis formation and diagnostic competence development. Her work bridges digital learning environments with hands-on experimentation, particularly through video-based resources and eye-tracking studies of cognitive processes during scientific inquiry. Current projects emphasize context-aware AI applications in both classroom and outdoor laboratory settings. Her funded research includes: Shift project (2023-present): AI-driven hypothesis feedback systems ProfiLL (2020-2023): Intelligent systems for teacher professionalization Digital Media Handling (2020-2022): Baseline digital competencies for biology students At the FLOX Teaching Laboratory, she develops and tests digital learning scenarios that merge theoretical knowledge with practical experimentation, with recent focus on Mimosa pudica movement studies and photosynthesis measurement techniques. Her approach emphasizes inclusive design principles for diverse learner needs in science education.

Moshe Y. VardiView profile

Academic

• Automated Reasoning
• Logic in Computer Science
• Artificial Intelligence
• +7 more

Moshe Y. Vardi is University Professor and the Karen Ostrum George Distinguished Service Professor in Computational Engineering at Rice University, where he has been a faculty member since 1993. He holds joint appointments as Professor of Computer Science, member of the Ken Kennedy Institute for Information Technology, and Fellow for Science and Technology Policy at the Baker Institute for Public Policy. Vardi leads Rice's Initiative on Technology, Culture, and Society, reflecting his dual expertise in computational logic and societal implications of technology. With over 50,000 citations, Vardi ranks among the most influential computer scientists globally. His research centers on automated reasoning, a branch of artificial intelligence with applications spanning machine learning, database theory, computational complexity, knowledge in multi-agent systems, and computer-aided verification. Vardi has made seminal contributions to the application of logic in computer science, earning him the moniker 'the calculus of computer science' for his work. Vardi's scholarly output includes over 750 papers and two foundational books: 'Reasoning about Knowledge' and 'Finite Model Theory and Its Applications.' His work shows a clear trajectory from theoretical foundations to societal impact, with recent publications increasingly addressing the ethical dimensions and societal consequences of technological advancement, particularly in artificial intelligence and automation. IEEE TCCH Outstanding Leadership Award, 2025 CAV Award, 2025 ICDT 2025 Test-of-Time Award Knuth Prize Foreign Member of the Royal Society ACM SIGMOD Codd Award IEEE Computer Society Goode Award ACM Kanellakis Award Vardi served as Editor-in-Chief of Communications of the ACM for a decade until 2017 and continues as Senior Editor. His thought leadership extends beyond academia through public commentary on technology's societal impact, particularly regarding AI's effect on employment and ethical considerations in technological development. He maintains an active engagement with students through courses on logic in computer science and research ethics seminars.

Alexandra SilvaView profile

Professor

• Programming Languages
• Formal Verification
• Kleene Algebra with Tests
• +6 more

Alexandra Silva is a Professor of Computer Science in the Department of Computer Science at Cornell University's College of Engineering. She joined Cornell as faculty in 2021 after previously serving as a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation at University College London. Her research spans programming languages, formal verification, and theoretical computer science, with particular focus on Kleene Algebra with Tests (KAT), probabilistic programming, and automata theory. She has held numerous leadership roles in major programming languages conferences including POPL, PLDI, and ICFP. Dr. Silva completed her PhD at Centrum Wiskunde & Informatica (CWI) in Amsterdam under the supervision of Jan Rutten and Marcello Bonsangue, with her thesis entitled "Kleene coalgebra" defended in December 2010. Prior to her PhD, she was an undergraduate student at University of Minho in Portugal, where she completed a 5-year Mathematics and Computer Science degree in May 2006. Her research focuses on the modular development of specification languages and algorithms for models of computations, often from the unifying perspective offered by coalgebra. She has made significant contributions to Kleene Algebra with Tests, probabilistic programming semantics, network verification, and automata learning. Her work bridges theoretical foundations with practical verification tools, particularly in the domain of Software-Defined Networking where her NetKAT framework has gained significant attention. Analysis of her recent publications reveals a strong trend toward unifying frameworks for program verification, particularly through her development of Outcome Logic which provides foundations for both correctness and incorrectness reasoning. Her work increasingly integrates probabilistic and concurrent aspects of programming languages, with applications to network verification and security. The NetKAT ecosystem remains a central theme, with extensions to infinite state verification, symbolic execution, and learning-based approaches. Distinguished paper award for Guarded Kleene algebra with tests: verification of uninterpreted programs in nearly linear time (POPL 2020) Dr. Silva advises a large research group with numerous PhD students, postdocs, and undergraduate researchers. Her group has produced significant work in programming languages theory, verification, and applications to networking. She has secured substantial research funding through various grants that support her work on formal methods for network verification and probabilistic programming. Her mentoring approach emphasizes both theoretical depth and practical impact, with many of her students moving to prestigious academic and industry positions. Her research group, spanning both Cornell University and University College London, focuses on developing theoretical foundations for programming languages with practical applications in network verification, probabilistic systems, and program analysis. The group maintains active collaborations with researchers at CWI, University of Oxford, and other leading institutions in programming languages and formal methods.