Jialong Li is an Assistant Professor (non-tenure-track) at the Waseda Institute for Advanced Study. His research focuses on dynamic responsibility allocation in human-software collaboration, with applications in autonomous driving, robotics, and adaptive systems. He employs techniques such as discrete controller synthesis and large language models to address runtime environmental changes. Key research themes: human-robot collaboration, self-adaptive software, autonomous systems Technical approaches: Discrete Controller Synthesis, LLM integration, reinforcement learning Recent publications emphasize multimodal human-AI collaboration, prompt engineering for software development, and ethical considerations in adaptive systems. His work bridges theoretical modeling (e.g., requirements analysis, stochastic control) with real-world applications like warehouse robotics and color-blind accessibility tools. While no formal awards are listed, his research has been presented at top conferences in software engineering and autonomous systems.
Patrick Totzke is a Professor in the Department of Computer Science at the University of Liverpool’s School of Electrical Engineering, Electronics and Computer Science. As a leading researcher at the intersection of mathematics and computer science, he specializes in the foundations of formal verification, algorithmic game theory, and infinite-state systems. Education: While specific degrees are not listed, his expertise and role as Professor strongly suggest advanced graduate training in computer science and mathematics. Research Interests: Algorithmic game theory, particularly strategy complexity and games on infinite graphs Decidability and complexity of verification problems such as bisimulation, language inclusion, model checking, and synthesis Counter automata, vector addition systems, Petri nets, and process algebras Computational logics with fixed-points, temporal or probabilistic modalities, and associated games Real-time systems including timed automata, timed Petri nets, and timed games His recent focus centers on timed automata and stochastic games on graphs. Research Grants: Below the Branches of Universal Trees – EPSRC (March 2023 – August 2024) Unambiguity in Infinite-state Systems – Royal Society (March 2021 – March 2023) COSTRA: The Cost of Winning Strategies – EPSRC (July 2021 – September 2024) Teaching: Module Co-ordinator for COMP122 Object-Oriented Programming (2024–25). Labs & Teams: While no specific lab names are provided, his grants and publications indicate active leadership of research teams in formal methods and verification.
Dr Kangfeng Ye is a Researcher at the Department of Computer Science, University of York. His work focuses on probabilistic programming, formal verification, and model-based software engineering for robotics and cyber-physical systems. PhD in Computer Science (2016), University of York Research roles in projects like CHEDDAR, SESAME, RoboTest, RoboCalc, and INTO-CPS His research bridges formal methods with practical applications in autonomous systems, security protocols, and robotics. He employs tools like PRISM, Isabelle, and RoboChart to ensure software reliability. Recent publications highlight trends in probabilistic modeling, formal verification of security protocols, and robotics safety assurance. His work intersects with 6G networks, agriculture robots, and controller synthesis from diagrams.
Amal Ahmed is a Professor and Associate Dean for Graduate Programs at the Khoury College of Computer Sciences , Northeastern University , USA. Her research focuses on correct and secure compilation , safe language interoperability , and logical relations , with a strong emphasis on bridging high-level abstractions to low-level code. Education : PhD in Computer Science, Princeton University. Her work addresses challenges in multi-language systems , gradual typing , and type-preserving compiler design , leveraging formal semantics and logical relations to ensure security and correctness. Recent projects include advancements in WebAssembly interoperability and probabilistic separation logic . Amal's research has resulted in 15 recent publications spanning areas like semantic realizability , modal logic , and parametricity in gradual typing . She mentors a dynamic research group including postdocs, PhD, and undergraduate students, and has contributed extensively to program committees and workshops in programming languages, including POPL, ICFP, and OOPSLA. Labs/Teams : Leads the SILC (Secure Interoperability, Languages, and Compilers) group and contributes to the Northeastern Programming Research Lab .
Professor Antonios Bikakis is a Professor of Artificial Intelligence in the Department of Information Studies at University College London (UCL). He serves as the Director of Research in the Department of Information Studies and co-director of the Knowledge, Information and Data Science research group. His educational background includes: PhD in Computer Science from the University of Crete (2009) M.Sc. in Computer Science from the University of Crete (2004) Bachelor of Engineering (Honours) in Electrical and Computer Engineering from Aristotle University of Thessaloniki (2002) Professional Certificate in Teaching and Learning in Higher and Professional Education from University of London Professor Bikakis's research focuses on foundational aspects of Artificial Intelligence, with particular emphasis on Knowledge Representation, Nonmonotonic Reasoning, Computational Argumentation, Semantic Technologies, and Multiagent Systems. His work extends to developing knowledge-based systems for Digital Cultural Heritage, Digital Libraries, Ambient Intelligence, and E-commerce applications. He has made significant contributions to contextual reasoning in Ambient Intelligence environments, argumentation frameworks, and knowledge graphs. His research bridges theoretical AI with practical implementations in cultural heritage and everyday problem-solving scenarios. His recent publications (2023-2025) reveal a strong trend toward integrating large language models with structured reasoning for commonsense applications, developing knowledge representation for crisis management and cooking domains, and advancing semantic technologies for cultural heritage. His work consistently combines theoretical rigor with practical applications, particularly in the cultural heritage sector where he has made significant contributions to digital museum technologies and knowledge representation. His notable scientific achievements include: Best Paper Award at the International Conference on Logic and Argumentation (CLAR 2021) Extensive editorial work including Special Issues on Semantic Web for Cultural Heritage Significant citations for his work on Crypto collectibles and museum funding (194 total citations) Professor Bikakis actively supervises PhD students and MSc dissertations in Knowledge, Information and Data Science and Digital Humanities programs. He has secured major research funding through projects including "Repurposing of Resources" (Leverhulme Trust), "CORDIAL-AI" (ESRC), and "CrossCult" (European Commission Horizon 2020). His collaborative approach is evident in his extensive network of co-authors across European institutions and his service on program committees of major AI conferences (IJCAI, AAAI, ECAI, KR). As co-director of the Knowledge, Information and Data Science research group, he leads interdisciplinary research at the intersection of AI theory and cultural heritage applications. The group develops technology that combines the latest advancements in semantic technology, mobile computing, and context-aware systems to create engaging experiences with cultural heritage materials, reflecting his commitment to making AI research impactful in real-world settings.
Mario Román is a Research Associate at the Department of Computer Science, University of Oxford , affiliated with the Compositional Systems and Methods Group at Tallinn University of Technology. His work bridges category theory , functional programming , and probabilistic programming , focusing on formal semantics and mathematical notation. His research interests include monoidal and premonoidal categories , Markov categories , and coinductive methods for dataflow programming. He explores the algebraic structures underlying quantum programming and stochastic processes , often applying graphical calculi and string diagrams. Recent work trends, as seen in his publications, emphasize distributive monoidal categories for program logics, effectful Mealy machines for bisimulation, and partial Markov categories for probabilistic reasoning. His collaborations span institutions in Europe and Asia, including Tallinn, Oxford, and Tokyo. Scientific Awards : Kleene Award, 'Monoidal Streams for Dataflow Programming', LiCS’22 Distinguished Paper, 'Effectful Mealy Machines: Bisimulation and Trace', LiCS’25 Best BSc Thesis, Spanish Royal Mathematical Society, 2018 International Mathematical Olympiad Honorary Mention, 2012 Mario has contributed to community initiatives as a member of the Compositionality Journal Executive Board and served on program committees for Applied Category Theory and Mathematical Foundations of Programming Semantics .
Sean Dae Houlihan is a Neukom Computational Science Postdoctoral Fellow and Lecturer in the Cognitive Science Program at Dartmouth. His research focuses on computational models of social cognition, particularly how humans reason about emotions through Bayesian inference and generative models. Education : PhD in Brain and Cognitive Sciences from MIT, advised by Rebecca Saxe, Josh Tenenbaum, and John Gabrieli. Collaborations : Works with Luke Chang, Jonathan Phillips, and Soroush Vosoughi at Dartmouth; affiliated with the Center for Brains, Minds and Machines (CBMM) and the Dalai Lama Center for Ethics. His work bridges cognitive science, artificial intelligence, and neuroscience by reverse-engineering social intelligence through probabilistic programs. This approach aims to formalize psychological theories and develop machines with human-like emotional intelligence. The 15 most recent publications highlight his focus on Bayesian modeling of theory-of-mind, cross-cultural emotion representation, and probabilistic programming frameworks like memo. Key themes include causal inference, social coordination, and multimodal integration in emotion understanding. Awards : Glushko Dissertation Prize (2024), Best Dissertation in Affective Science Award (2024).
Leonidas Akritidis serves as a Lecturer at the Department of Science and Technology within the School of Science and Technology at International Hellenic University. He holds a PhD and Diploma in Electrical and Computer Engineering from University of Thessaly and Aristotle University of Thessaloniki respectively. His educational background includes: PhD in Electrical and Computer Engineering, University of Thessaly (2007-2013) Diploma in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1997-2003) Akritidis specializes in Natural Language Processing, Deep Machine Learning, and Data Mining with particular expertise in short text clustering, dimensionality reduction, and rank aggregation techniques. His research addresses challenges in sparse data environments and high-dimensional feature spaces, developing novel algorithms for text representation and processing. He has contributed significantly to parallel and distributed computing approaches for big data analytics. His publication trends reveal a consistent focus on NLP and machine learning applications, with recent work expanding into generative models for tabular data, medical image analysis, and advanced fault detection systems. The research demonstrates progression from foundational text processing techniques to more complex applications in healthcare, e-commerce, and software engineering. Akritidis has taught multiple courses including Big Data and Cloud Computing, Machine Learning Principles and Concepts, Mobile Application Development, and Web Programming. His research projects include CyberPi (intelligent cyber threat detection), NANOTRIM (transistor sizing optimization), and iMuSe (virtual museum platform). He collaborates extensively with researchers like Panayiotis Bozanis, Miltiadis Alamaniotis, and Athanasios Fevgas, primarily within the Greek academic community.
Corina Pasareanu is a Principal Scientist at Carnegie Mellon University's CyLab Security and Privacy Institute and serves as a Technical Professional Leader for Data Science at NASA Ames Research Center through KBR. She holds a PhD in Computer Science from Kansas State University (2001), an MS (1995) and BS (1994) from the University Politehnica of Bucharest. Her research focuses on formal methods for trustworthy AI , including model checking, symbolic execution, compositional verification, and probabilistic software analysis. She pioneers techniques for verifying autonomous systems, neural networks, and cryptographic applications, with emphasis on safety-critical domains like autonomous vehicles and aerospace systems. Recent publications demonstrate strong focus on AI safety verification , including adversarial robustness of large language models, vision-based autonomous systems, and neural network interpretability. Her work integrates formal methods with machine learning to address security challenges in emerging AI technologies. Awards and honors: ACM Fellow (2023) 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) She leads major projects funded by DARPA, NSF, AWS, and NASA including: Trinity: Neurosymbolic Learning and Reasoning (DARPA) Proving Timing Side Channel Absence (AWS) Safety of Shared Control in Autonomous Driving (AAIP) Verifiable Federated Learning (CyLab) She advises PhD students at CMU and co-leads the CyLab Security and Privacy Institute's research initiatives.
Cameron Freer is a Research Scientist at the Massachusetts Institute of Technology's Probabilistic Computing Project. His academic career spans multiple institutions including Keio University, Harvard University, and University of Hawaii at Manoa, with roles ranging from Postdoctoral Fellow to Project Associate Professor. Current affiliation: MIT Probabilistic Computing Project Past academic roles: Keio University SFC (2021–2024), MIT Brain and Cognitive Sciences (2013–2015), MIT Mathematics (2008–2010) Research interests focus on probabilistic computing , random structures , and their intersections with logic, mathematics, and artificial intelligence. Key contributions include foundational work on Markov categories , graphons , and feedback computability . Recent publications explore probabilistic programming systems , random graph modeling , and computable aspects of measure theory . Collaborators include prominent researchers from Harvard, Oxford, and MIT. Professional engagements include committee memberships in major conferences like POPL (2024), LAFI (2024–2025), and PLDI (2024). Holds PhD in Mathematics from Harvard University (2008).
Arnaud Carayol is a Professor of Computer Science at Gustave Eiffel University, where he has been working since September 2020. He is a member of the Models and Algorithms team at the Laboratoire d'informatique Gaspard Monge (LIGM). Prior to his current position, he was a full-time researcher at CNRS. His research focuses on theoretical aspects of computer science, with particular emphasis on: Automata Theory Formal Languages Logic in Computer Science Model Checking Pushdown Systems Games on Infinite Structures Professor Carayol's recent publications demonstrate a consistent focus on automata theory, particularly on infinite trees and pushdown systems, with applications to verification and game theory. His work often explores the connections between formal languages, logic, and computational models, with a particular emphasis on decidability and complexity questions. The trend shows increasing sophistication in handling higher-order systems and probabilistic elements in computational models. He has served on program committees for numerous prestigious conferences including LICS, STACS, ICALP, and FOSSACS. Notably, he was PC Co-Chair and organizer for CIAA 2017 and PC Chair and organizer for FICS 2013. Professor Carayol has led significant research projects: Head of project AMIS (2011-2014) financed by ANR Head of project VAPF (2011-2012) financed by Digiteo Member of project LiFoundations (2018-2022) His research continues to advance our understanding of theoretical models in computer science, with implications for program verification, formal methods, and computational theory.
Dr. Cheryl Zhenyu Qian serves as Professor of Interaction Design and Industrial Design at Purdue University's Rueff School of Design, Art, and Performance, where she also holds the position of Interim School Head. Her academic journey spans architecture, interactive arts, and interdisciplinary design research. Her educational background includes: B.Arch. from Southeast University (China) M.A.Sc. and Ph.D. in Interactive Arts and Technology from Simon Fraser University (Canada) Dr. Qian's research explores the harmonious integration of physical and virtual interactions, application of interaction design theories to product development, and innovative design thinking in visual analytics. She employs interdisciplinary methodologies to enhance user experience through cognitive systems and knowledge enrichment. Analysis of her recent publications reveals a strong focus on sustainable design systems, visual analytics for crisis management, accessibility technologies, and AI-VA integration. Her work consistently bridges theoretical frameworks with practical applications across domains including environmental monitoring, health technology, and cultural studies. Her scientific recognition includes: 18 IEEE VAST Challenge Awards (2010-2024) Dean's Convocation Medal for Ph.D. Dissertation Multiple VAST Challenge Honorable Mentions Honorary Professorships from three international universities As an educator, Dr. Qian developed Purdue's Interaction Design MFA program and its core curriculum. She has secured significant funding from NSF, NIH, and NIFA for interdisciplinary projects, and her students have secured positions at leading technology companies including Amazon, Google, and NASA. Her collaborative approach extends to partnerships with industry manufacturers, security analysts, and business managers. Dr. Qian maintains an active research program with frequent collaborations across engineering, computer science, and business disciplines, focusing on translating theoretical design concepts into practical user-centered solutions.
Sang Kil Cha is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology) where he holds positions in both the Graduate School of Information Security and the School of Computing. He serves as the director of the Cyber Security Research Center (CSRC) at KAIST and leads the SoftSec Lab. Dr. Cha received his Ph.D. and M.S. degrees from Carnegie Mellon University and his B.S. degree from Korea University. Current Position: Associate Professor, KAIST Leadership: Director of Cyber Security Research Center (CSRC) Laboratory: Head of SoftSec Lab Education: Ph.D. and M.S. from CMU, B.S. from Korea University Dr. Cha's research focuses on the intersection of computer security and software engineering, with particular emphasis on building and evaluating systems that can analyze programs. His work spans software security, software engineering, software systems, and program analysis. He has made significant contributions to binary code analysis, fuzzing techniques, and reverse engineering. His research has practical applications in vulnerability detection, malware analysis, and secure software development. His publication record demonstrates consistent high-impact contributions to the field, with numerous papers in top-tier security and software engineering conferences including IEEE S&P, USENIX Security, ISSTA, and ICSE. His recent work shows a continued focus on advancing fuzzing methodologies, binary analysis techniques, and security applications for blockchain technologies. Dr. Cha's research group has produced influential tools such as B2R2 (a binary analysis framework) and ofuzz (a fuzzing framework). ACM Distinguished Paper Award USENIX Distinguished Paper Award Best Paper Award NDSS Best Paper Award As an educator, Dr. Cha has taught courses including Binary Code Analysis and Secure Software Systems, Advanced Software Security, and Introduction to Information Security. His research group has mentored numerous students who have become co-authors on his publications. His work is supported by various research grants focused on software security and analysis techniques. The SoftSec Lab maintains active collaborations with both academic and industry partners in the security research community.
Qingkai Shi is an Associate Professor in the School of Computer Science at Nanjing University, China, specializing in compiler techniques and formal program analysis for software and network security. His research bridges programming languages, cybersecurity, and software engineering with practical applications in real-world systems. Education: Ph.D. from Hong Kong University of Science and Technology Postdoctoral researcher at Purdue University, USA His research focuses on rigorous security validation through advanced static and dynamic analysis, particularly in network protocol parsers, compiler security, and autonomous systems. Key methodologies include context-sensitive program analysis, differential analysis, and formal specification techniques that address critical vulnerabilities in modern software ecosystems. Recent publications (2022-2026) demonstrate strong continuity in program analysis research with expanding applications to emerging domains like autonomous driving and Rust ecosystems. Work consistently targets high-impact security challenges in network protocols, compiler correctness, and memory safety, often yielding practical tools and industrial applications. Scientific Awards: Four ACM SIGPLAN/SIGSOFT Distinguished Paper Awards Google Research Paper Award Hong Kong Ph.D. Fellowship Dr. Shi co-founded Sourcebrella LLC (acquired by Ant Group), translating academic research into commercial security solutions. His ongoing industry collaborations demonstrate successful technology transfer from program analysis research. He leads an active research group developing open-source analysis tools including canary (alias analysis), netlifter (network protocol specification), and context-sensitive-reachability (data flow analysis), all available on GitHub with substantial community engagement.
NIANYU LI is an active researcher at ZGC Lab, China specializing in human-involved self-adaptive systems. Their work integrates rigorous modeling techniques to ensure software safety, security, and reliability in dynamic environments, with significant contributions to cyber-physical systems and formal verification methodologies. They earned a Ph.D. in Computer Software and Theory from Peking University in 2021 under Prof. Zhi Jin and Prof. Wenpin Jiao, with additional research experience at the National Institute of Informatics (NII) and Carnegie Mellon University (CMU) under Prof. Zhenjiang Hu and Prof. David Garlan respectively. Research focuses on human-in-the-loop adaptation mechanisms, with expertise spanning requirements modeling, system verification, and safety-critical cyber-physical implementations. Their methodology combines formal control paradigms with practical human factors considerations to address environmental uncertainty in adaptive systems. Publication trends reveal consistent contributions to ASE, ICSE, and specialized conferences like SASMS, with recent work exploring LLM applications in adaptation, federated learning robustness, and SBOM generation. Key themes include verification-driven design, human-system coordination, and empirical validation of adaptive mechanisms in real-world scenarios. As an active community contributor, they serve on program committees for ASE, ACSOS, and ICSE events while maintaining leadership roles in artifact evaluation. Their work bridges theoretical formal methods with practical implementation challenges in adaptive systems. NIANYU LI maintains research continuity through collaborations with Peking University and international institutions, directing focus toward explainable adaptation mechanisms and safety assurance in increasingly complex human-software ecosystems.
