Wenchao Li is an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University, directing the Dependable Computing Laboratory. He holds a B.S., M.S., and Ph.D. in Electrical Engineering and Computer Sciences, along with a B.A. in Economics from UC Berkeley. His research focuses on dependable computing, applying formal verification, machine learning, and control theory to cyber-physical systems, electronic design automation, and AI safety. Key research interests include neural network verification, safe reinforcement learning, autonomous systems security, and resilient control strategies for connected vehicles. His work emphasizes provable safety guarantees and defense against adversarial attacks in critical infrastructure systems. Notable awards include the ACM Outstanding Ph.D. Dissertation Award and the Leon O. Chua Award. His lab investigates topics such as neural network repair, secure multi-robot coordination, and formal methods for autonomous systems. He advises students like Jiameng Fan and collaborates on projects funded by grants in AI safety and cyber-physical systems. Labs/Teams: Dependable Computing Laboratory Grants: Focus on formal verification, AI safety, and autonomous systems resilience
Overview Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University , where he directs the Ethos Security & Privacy Lab and is a member of the National Security Institute. His work focuses on securing emerging technologies like IoT, AR, and ML systems. Education PhD in Computer Science & Engineering, University of Michigan (2017) Research Focus His research addresses security threats in emerging systems, including: Cyber-Physical System vulnerabilities Adversarial ML attacks Hardware security Privacy-preserving frameworks Notable contributions include work on cryptocurrency scam detection, neural network robustness, and AR system security. Grants & Awards Supported by the Air Force Office of Scientific Research (AFOSR), Office of Naval Research (ONR), Meta, NVIDIA, and IBM. His research has been featured in MIT Technology Review , Washington Post , and Bloomberg . Advising Seeking students with expertise in hardware, software, ML, UX, or network protocols passionate about security/privacy. Apply via the graduate program and fill out his interest form. Labs & Collaborations Leads the Ethos Lab focusing on securing IoT, AR, and ML systems. Collaborates on projects like Erebus (AR access control) and Valve (serverless computing security).
Vivy Suhendra serves as Associate Professor of Practice and Programme Director for Master Programmes at the National University of Singapore's School of Computing, while also holding the position of Assistant Dean for Graduate Studies. Previously, she led the Singapore Cybersecurity Consortium (SGCSC) as Executive Director from 2016 to 2022, driving collaborative cybersecurity research between academia, industry, and government agencies. Her career at NUS spans over two decades, beginning as a Research Assistant before advancing to her current leadership roles. Her academic credentials include: Ph.D. in Computer Science, National University of Singapore (2009). Thesis: "Memory Optimizations for Time-predictable Embedded Software". Advisors: Abhik Roychoudhury and Tulika Mitra. B.Comp. (Honors) in Computer Science, National University of Singapore (2004). Dr. Suhendra's research integrates Software Assurance, Cybersecurity, Security and Privacy, and Embedded Systems domains. Her work bridges theoretical foundations with practical applications, particularly in national cybersecurity ecosystem development, smart grid security protocols, denial-of-service mitigation techniques, and real-time embedded system optimization. This interdisciplinary approach enables innovative solutions for critical infrastructure protection and time-predictable software execution in multi-core environments. Her 14 selected publications (2004-2020) demonstrate an evolving research trajectory from foundational embedded systems timing analysis to applied cybersecurity solutions. Early work focused on memory optimization for predictable execution in multi-core embedded systems, which naturally transitioned into cybersecurity applications for smart grids, cloud environments, and national infrastructure. This progression highlights her ability to translate low-level system expertise into high-impact security frameworks for complex real-world systems. Scientific recognition includes: Microsoft Research Asia Fellowship (2006) Valedictorian at NUS School of Computing Ph.D. Commencement (2010) While specific graduate student advising details aren't provided, her leadership as SGCSC Executive Director involved extensive mentorship across academic-industry partnerships. She has also contributed significantly to the research community through roles including Conference Chair for ESEC/FSE 2022 and Workshops Committee Member for ICSE 2024. Dr. Suhendra established the Singapore Cybersecurity Consortium as a national platform for collaborative R&D during her directorship (2016-2022). Though no personal laboratory is specified, her research leadership manifests through cross-institutional teams focused on cybersecurity innovation, particularly in critical infrastructure protection and embedded systems security where she maintains active publication records.
Murat Arcak is a Professor of Electrical Engineering and Computer Sciences and Mechanical Engineering at the University of California, Berkeley, holding the Robert M. Saunders Endowed Chair in the College of Engineering. His research spans control theory, autonomous systems, and multi-agent systems with applications in transportation, energy, and biology. Dr. Arcak received his Ph.D. in Electrical Engineering from the University of California, Santa Barbara in 2000, following an M.S. from the same institution in 1997 and a B.S. from Bogazici University in Istanbul, Turkey in 1996. His research interests focus on developing scalable control design and verification methods for complex systems with many interconnected components, nonlinear dynamics, and learning capabilities. He has made significant contributions to control theory, particularly in areas like reachability analysis, dissipative systems, and compositional verification methods. His work bridges theoretical advances with practical applications in transportation systems, energy networks, and biological systems. A leading researcher in control systems, Dr. Arcak's recent publications demonstrate a strong focus on data-driven approaches for system verification, synthetic biology applications, and formal methods for traffic control. His research combines mathematical rigor with practical implementation, often developing novel theoretical frameworks that address real-world engineering challenges. CAREER Award from the National Science Foundation (2003) Donald P. Eckman Award from the American Automatic Control Council (2006) Control and Systems Theory Prize from SIAM (2007) Antonio Ruberti Young Researcher Prize from IEEE Control Systems Society (2014) Brockett-Willems Outstanding Paper Award (2021) IFAC Fellow (2020) IFAC Automatica Paper Prize (2020) CSS Transactions on Control of Network Systems Outstanding Paper Award (2017) Electrical Engineering Award for Outstanding Teaching (2014) CSS Antonio Ruberti Young Researcher Prize (2014) IEEE Fellow (2012) SIAM Activity Group Control and Systems Theory Prize (2007) Dr. Arcak has advised numerous graduate students and postdoctoral researchers, though specific names are not listed in the available information. His research has been supported by various grants from the National Science Foundation and other funding agencies, enabling his work on control theory and applications across multiple domains. He is affiliated with several research centers at UC Berkeley including the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Deep Drive (BDD), the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB), the Institute of Transportation Studies (ITS), and Partners for Advanced Transit and Highways (PATH).
Michael Everett is an Assistant Professor at Northeastern University with a joint appointment in the Department of Electrical & Computer Engineering and the Khoury College of Computer Sciences. He directs the Autonomy & Intelligence Laboratory, focusing on certifiable learning machines at the intersection of robotics, deep learning, and control theory. His research emphasizes safety, reliability, and efficiency in robotics applications like off-road navigation and social environments. Education: PhD in Mechanical Engineering, Massachusetts Institute of Technology (2020) SM in Mechanical Engineering, MIT (2017) SB in Mechanical Engineering, MIT (2015) Research Interests: Robotics and motion planning Control theory and neural network verification Reinforcement learning applications Certifiable safety guarantees for autonomous systems Navigation in dynamic/human environments Awards: Runner-Up: Best Paper Award (ICML 2022) Winner: Best Student Paper (IROS 2017/2023) Editors’ Top 5 Published Articles (IEEE Access 2021) Lab & Contributions: The Autonomy & Intelligence Lab develops algorithms for high-speed off-road autonomy, socially aware navigation, and neural feedback verification. His work includes the RAMP planning pipeline and Evora traversability learning framework. He collaborates with Google’s PAIR team on trustworthy AI.
Dr. Yangjun Chen is a Full Professor in the Department of Applied Computer Science at the University of Winnipeg, Canada. He holds a Ph.D. from the University of Kaiserslautern, Germany (1995). His research focuses on database systems, graph algorithms, computational complexity, and theoretical computer science. Key areas include Federated Databases, Deductive Databases, DNA Databases, and the P vs NP problem. Education: Ph.D. in Computer Science, University of Kaiserslautern, Germany (1995). Research interests span graph query processing, algorithm design, big data optimization, and NP-completeness. Recent work includes polynomial-time solutions for 2-MAXSAT and advancements in string matching algorithms for DNA databases. Publications highlight contributions to graph indexing, efficient reachability queries, and algorithmic efficiency in databases. His work often bridges theoretical foundations with practical database applications. Awards: Excellent Merit Award (2022-2023, 2021-2022) - University of Winnipeg Best Article, ACTA Scientific Computer Sciences (2022) Multiple Best Paper Awards at conferences like DBKDA 2016 and CyberC Summit Teaching includes Advanced Databases, Distributed Database Systems, and Algorithms courses at both undergraduate and graduate levels. Active in supervising research in database systems and theoretical computer science. Laboratory and team focus on database innovation, including projects on graph databases and efficient query processing techniques.
Khanh Nguyen is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. His research focuses on improving scalability and efficiency in Big Data systems through compiler and runtime innovations, particularly in memory management and distributed computing. Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) M.S. in Computer Science, University of California, Irvine (2015) B.S. in Computer Science, University of California, Irvine (2012) His research interests include programming languages, compiler design, memory management, and Big Data systems. He has developed techniques such as Gerenuk for thin computation over big data, Skyway for distributed heap connectivity, and Yak , a high-performance garbage collector. His work emphasizes resource efficiency and workload scalability, particularly for machine learning and data-parallel applications. Recent publications (2021–2024) highlight advancements in adaptive memory management for warehouse-scale computers, semantics-aware swapping in disaggregated systems, and query-driven distributed tracing. Awards: Google Ph.D. Fellowship (2017) Facebook Ph.D. Fellowship Finalist (2017) His research bridges compiler/runtime systems with large-scale data processing, addressing challenges in distributed systems and far-memory utilization. He collaborates with industry and academic partners to advance practical, scalable solutions for modern data-intensive workloads.
Ana Sokolova is a Professor in the Department of Computer Science at the University of Salzburg. She is affiliated with the Faculty of Digital and Analytical Sciences and actively contributes to research in theoretical computer science. University: University of Salzburg Faculty: Faculty of Digital and Analytical Sciences Department: Computer Science Email: ana.sokolova@plus.ac.at Her research focuses on probabilistic systems , concurrency theory , convex algebras , and formal verification . This work bridges theoretical foundations with practical applications in distributed computing and programming semantics. Recent publications highlight advancements in trace semantics , determinization , probabilistic anonymity , and coalgebraic modeling . Key trends include the integration of Markov chains , nondeterministic systems , and algebraic structures for formal verification.
Dr. Shufang Zhu is a Lecturer (Assistant Professor equivalent) at the Department of Computer Science, University of Liverpool , and an Associate Member at the University of Oxford’s Department of Computer Science . Previously, she held roles including Senior Research Associate at Oxford (2023–2024) and Postdoctoral Researcher at Sapienza Università di Roma (2020–2022). She earned her Ph.D. in Software Engineering from East China Normal University (ECNU, 2020) under Prof. Geguang Pu, with a visiting Ph.D. at Rice University (2016–2018) under Prof. Moshe Y. Vardi. Education: B.Sc./Ph.D. in Software Engineering from ECNU (2010–2020). Scholarships include the Chinese Scholarship Council (CSC) and UT Austin EECS Rising Star (2022). Her research focuses on interdisciplinary areas of Formal Methods and Artificial Intelligence , emphasizing automated reasoning, planning, and synthesis. Key topics include temporal logics (LTL/LTLf), symbolic synthesis frameworks, and applications in reactive systems. Notable work addresses finite-trace specifications, best-effort strategies, and coordination in multi-agent systems. Teaching: Lecturer for Game-Theoretic Approach to Planning and Synthesis (European Summer School) and Foundations of Self-Programming Agents (Oxford). She also supervises funded Ph.D. positions, including a 2025 deadline for CSC-Liverpool scholarships. Awards: Future Digileader (Digital Futures, 2023), UT Austin EECS Rising Star (2022). Erdős number ≤3 via Moshe Y. Vardi. Collaborations: Co-chair of AAAI 2023 symposium on temporal logics in AI. Active in open-source tools like LydiaSyft for LTLf synthesis. Engages with academic networks through Google Scholar, DBLP, and GitHub.
Joseph Devietti is an Associate Professor in the Department of Computer & Information Science at the University of Pennsylvania. His research focuses on improving programmability and performance of multiprocessor systems through architectural and programming model innovations. He actively advises PhD students and has supervised numerous graduates now employed at leading tech companies and academic institutions. Education: PhD (2012), MS (2009) in Computer Science and Engineering from University of Washington; BSE (2006) in Computer Science and BA (2006) in English from University of Pennsylvania. Employment: Associate Professor (2020–present), Assistant Professor (2013–2020) at University of Pennsylvania; Principal Scientist & Co-founder at Cloudseal, Inc. (2018–2020). Devietti’s research spans computer architecture, parallel programming, and deterministic execution. Key areas include cache/memory optimization (prefetching, false sharing repair), GPU programming models (race detection, block-size independence), and hardware-software co-design for concurrency safety. His recent work addresses dynamic runtime prefetch tuning (RPG 2 ), online code layout optimization (OCOLOS), and intelligent BTB prefetching (Twig) for data center applications. His publications from 2024–2017 reveal trends in instruction/cache optimization (2024–2020), GPU determinism (2018–2017), and race detection (2018–2016). Awards include the 2024 Penn Engineering Ford Motor Company Award, Radhia Cousot Best Paper (2018), and IEEE Micro Top Picks recognition (2023, 2009, 2008). Scientific Awards : 2024 Penn Engineering Ford Motor Company Award Radhia Cousot Young Researcher Best Paper Award (SAS 2018) IEEE Micro Top Picks (2023, 2009, 2008) Intel Early Career Faculty Honor Program (2013) Intel Ph.D. Fellowship (2011) Advising : Supervised 15+ PhD/Master’s students with placements at Google, Microsoft, Amazon, NYU, and the United States Naval Academy. Collaborations : Works with industry leaders (NVIDIA, Facebook) and academic institutions (University of Washington, Penn).
Jana Tumova is an Associate Professor at the Division of Robotics, Perception and Learning, KTH Royal Institute of Technology. Her research focuses on designing algorithms for safe, purposeful autonomous systems using formal methods to ensure rigorous specifications and guarantees. Applications span autonomous driving, UAV exploration, and network control. She teaches courses like Artificial Intelligence (DD2380) and leads the Robotics, Reading Group (FDD3316) . Her work emphasizes formal methods integration with AI, safety-critical control, and human-robot interaction. Notable research directions include risk-aware planning, belief space control, and contingency planning under uncertainty. She has published extensively on motion planning, robust control synthesis, and multi-agent systems. Key technical contributions include techniques like Belief Control Barrier Functions , Backward Underapproximate Reachability (BURNS) , and Transitional Grid Maps . She actively participates in interdisciplinary initiatives like the Control for Societal-Scale Challenges: Roadmap 2030 .
Marcel Böhme is a faculty member at the Max Planck Institute for Security and Privacy (MPI-SP) , leading the Software Security research group. His work focuses on foundational advancements in fuzzing , statistical program analysis, and scalable vulnerability discovery. Education: PhD from National University of Singapore (NUS) Research interests span: Statistical and causal frameworks for software testing Efficiency/Scalability of automated testing Fundamental limits of vulnerability detection Practical fuzzing technology (e.g., Entropic in LibFuzzer) Recent publications highlight trends in: Machine learning for security analysis Privacy-preserving statistical methods Future-proof security frameworks Protocol fuzzing with large language models Scientific accolades include: ERC Consolidator Grant (2024) NUS Outstanding Young Alumni Award (2022) ARC DECRA (2019) Multiple ACM Distinguished Paper Awards He serves as: Spokesperson for Research Group Leaders at Max Planck Society Guest Editor-in-Chief for ACM TOSEM PC Chair for ASE'25 and ISSTA'26
Mahnaz Nazneen is an Associate Professor (Teaching Focussed) in the Department of Economics at the University of Warwick. Her research focuses on behavioral economics, particularly exploring social preferences, gender dynamics, cooperation mechanisms, and experimental economics methodologies. She holds a mailing address at the Social Studies Building and is reachable at m.nazneen.1@warwick.ac.uk (Room S0.82). In teaching, she leads EC106: Introduction to Economics and EC984: Experimental Economics, while also serving as a tutor for EC202: Microeconomics II and lecturer for EC345: Behavioural Economics: Theory and Applications. Her advising hours include Thursday 12:00-14:00 (MS Teams) and a PG Pastoral Support Drop-in Session at 15:00-16:00 (Term 3). Her research highlights include examining how happiness influences workplace productivity and cooperation dynamics, with notable contributions to understanding mood effects on strategic interactions (2017-2023). Experimental studies form the core of her work, often involving controlled laboratory and field experiments. Her academic output spans articles in prestigious journals like Journal of Economic Behavior & Organization and the Oxford Research Encyclopedia of Economics and Finance. While no formal awards are listed, her publications reflect consistent engagement with cutting-edge behavioral economic questions.
Xiao Wang is a research assistant and PhD student in the Cyber-Physical Systems Group at the Technical University of Munich since 2019. She holds a Master of Science in Mechanical Engineering from the same university (2018) and a Bachelor of Engineering in Vehicle Engineering from Tongji University, China. Her research focuses on Motion Planning for Autonomous Vehicles , Formal Methods , and Safe Reinforcement Learning . She has supervised multiple theses exploring topics like constrained RL, online verification, imitation learning, and safety falsification for autonomous systems. Her teaching roles include exercises and practical courses on Artificial Intelligence and Motion Planning for Autonomous Vehicles since 2018. Her publications (2020–2023) span journals like Transactions on Machine Learning Research and conferences such as ITSC and FISITA , addressing challenges in safe RL, control barrier functions, and naturalistic traffic rule violations. She has also contributed to integrating the Apollo framework with the CommonRoad motion planning environment. Key research areas: Safe Reinforcement Learning, Motion Planning, Formal Verification, Autonomous Driving, Control Barrier Functions, Trajectory Prediction
Dr. Hafizul Asad serves as a Lecturer in Dependability at City St George's, University of London, leveraging his PhD in Electrical Engineering (City University of London, 2016) and MS in Aerospace Engineering (University of Belgrade, 2008) to advance cybersecurity and formal verification research. His expertise bridges critical infrastructure protection and cyber-physical systems security, with significant contributions to IoT/IIoT security frameworks. His educational journey includes: PhD in Electrical Engineering, City, University of London (2012-2016) MS in Aerospace Engineering, University of Belgrade, Serbia (2007-2008) BSc in Electrical and Electronics Engineering, University of Engineering and Technology Peshawar, Pakistan (1999-2003) Asad's research centers on formal verification of hybrid systems and verifiable intrusion detection mechanisms for interconnected environments. He pioneers provably robust security architectures for IoT/IIoT systems, emphasizing mathematical verification to ensure system resilience against cyber threats. His work integrates diversity principles to create defense-in-depth strategies for critical infrastructure, with recent focus on wind turbine cyber-safety and industrial control system protection. Analysis of his 15 most recent publications (2014-2025) reveals an evolution from aerospace applications and analog circuit verification toward cutting-edge cybersecurity for cyber-physical systems. His 2023-2025 work demonstrates increasing specialization in IoT security and formal methods, while maintaining foundational contributions to diversity-based security architectures established in his 2015-2018 research. No scientific awards or prizes are documented in the provided materials, though he maintains professional standing as a British Computer Society member and Higher Education Academy Associate Fellow. Details regarding doctoral student supervision or specific research grants are not disclosed in the source text. His professional trajectory indicates significant project involvement, including the D3S security project at City University of London (2015-2018) and Rolls-Royce-funded Future Systems Simulator development at Cranfield University (2018-2019), though current laboratory affiliations remain unspecified.
