Sanjit A. Seshia is the Cadence Founders Chair Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . He is affiliated with the Group in Logic and the Methodology of Science and participates in centers like the Industrial Cyber-Physical Systems Center , Berkeley AI Research , and the Simons Institute for the Theory of Computing . Research interests include formal methods for automated verification and synthesis of dependable systems, with applications to cyber-physical systems , AI-based autonomy , and computer security . His work spans SMT solving, model counting, syntax-guided synthesis, and algorithmic improvisation, with tools like UCLID5 , VerifAI , and Scenic for verifying autonomous systems and educational platforms like CPSGrader . Students and collaborators include notable researchers such as Dorsa Sadigh (Stanford), Daniel Fremont (UC Santa Cruz), and Hazem Torfah (Chalmers). He has co-founded startups like Decyphir and 20ⁿ Labs based on his research.
Wei Sun is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, where he also serves as the Director of the Siemens Digital Grid Lab. His research focuses on power system restoration, self-healing smart grids, cyber-physical security, and renewable energy integration. Dr. Sun received his Ph.D. from Iowa State University in 2011, and his M.S. and B.S. from Tianjin University in 2007 and 2004, respectively. Prior to joining UCF, he was an Assistant Professor at South Dakota State University (2013-2015), a power system engineer at Alstom Grid (2011-2012), a visiting scholar at the University of Hong Kong (2011), and an intern at California Independent System Operator (2010). His research interests include: Power System Restoration and Self-healing Smart Grid Resilient and Secure Critical Infrastructure Cyber-Physical Systems Renewable Energy and Microgrid Distributed Energy Resources Integration Dr. Sun's recent publications demonstrate strong focus on cyber-physical security in power systems, distributed energy resource integration, and resilient grid operations. His work shows increasing emphasis on AI and machine learning applications for grid security and resilience, particularly in the context of high renewable penetration. His notable scientific awards include: Microsoft Software Engineering Innovation Foundation Award (2014) Best Paper Award, 2019 IEEE PES ISGT Asia Mentor of the Year, UCF Graduate Student Association (2019) Dr. Sun has successfully secured multiple research grants totaling millions of dollars from agencies including the US Department of Energy, National Science Foundation, Florida Center for Cybersecurity, and Microsoft. He currently serves as PI or Co-PI on several major projects including "Secure and Resilient Operations Using Open-Source Distributed Systems Platform (OpenDSP)" funded by the Department of Energy. He leads the Siemens Digital Grid Laboratory at UCF, which is equipped with utility-grade software and hardware including Spectrum Power Microgrid Management System, Power System Simulator for Engineering, and Siemens Distribution Feeder Automation. The lab provides capabilities for both software modeling and hardware-in-the-loop testing of power systems.
Christof Paar is a Professor of Embedded Security at Ruhr University Bochum's Faculty of Computer Science. His work focuses on hardware security, cryptography, and embedded systems security. He leads the Embedded Security Group within the CASE Cluster of Excellence (Cyber Security in the Age of Large-Scale Attacks), addressing cutting-edge challenges in hardware Trojans, side-channel attacks, and cryptographic engineering. His research spans FPGA and IoT security, with contributions to physical-layer security, wireless jamming defenses, and tamper-resistant systems. Research Interests: Paar's expertise lies in hardware-software co-design for security, with a focus on embedded systems. His work includes analyzing vulnerabilities in FPGAs, developing countermeasures against side-channel attacks, and exploring physical-layer security mechanisms. He also investigates the human factors in hardware reverse engineering and the implications of adversarial machine learning on security systems. Notable Work: Recent publications highlight breakthroughs in detecting hardware Trojans across CMOS generations, breaking industry-standard IP protection mechanisms (IEEE 1735), and proposing novel defenses like IRShield against adversarial wireless sensing. His group actively contributes to standards for secure embedded systems and IoT devices, emphasizing practical implementations.
Prof. Dr. Barbara Kraus is the Chair of Quantum Algorithms and Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences. She previously held academic positions at the University of Innsbruck, where she founded her research group in 2010. Education : Physics and Mathematics at the University of Innsbruck; Post-doctoral work at MPI for Quantum Optics and University of Geneva. Her research focuses on foundational problems in quantum information theory, particularly entanglement in multipartite systems, quantum simulation, and verification of quantum processors. She develops theoretical tools for quantum many-body systems and explores applications in quantum computing, emphasizing error characterization and experimental validation. Recent publications highlight advancements in Hamiltonian learning, symmetry-resolved entanglement detection, and multipartite state transformations. Her work bridges theoretical quantum physics with practical implementations, including Rydberg platforms and quantum metrology. Key Awards : START Prize (2010), Ignaz L. Lieben Award (2013), Boltzmann Prize (2011), Südtiroler Sparkasse Research Prize (2019). She supervises doctoral students and postdocs in quantum information theory, with a focus on stabilizer states, quantum networks, and entanglement measures. Her courses at TUM include Quantum Information , Quantum Algorithms , and workshops on entanglement manipulation.
Daniel Genkin is an Associate Professor at the School of Cybersecurity and Privacy and School of Computer Science at Georgia Institute of Technology. His research spans system security, cryptography, side-channel attacks, hardware security, cryptanalysis, secure multiparty computation (MPC), verifiable computation, and SNARKs. He holds a PhD in Computer Science from the Technion - Israel's Institute of Technology and was awarded the 2024 Sloan Research Fellowship. His work focuses on microarchitectural vulnerabilities, including Rowhammer , Cache Timing , and Speculative Execution attacks. Notable contributions include co-discovering Spectre and Meltdown vulnerabilities. He has received multiple awards, including Distinguished Paper Awards , IEEE Micro Top Picks , and Black Hat Pwnie Awards . Publications highlight trends in Side-Channel Analysis , Hardware Exploitation , and Cryptographic Implementations . His email is genkin@gatech.edu , and he actively seeks students for research in security and cryptography.
Debjit Pal is a Post-Doctoral Associate at the School of Electrical and Computer Engineering, Cornell University, and a member of the Computer Systems Laboratory. His research focuses on machine learning techniques for hardware verification, SoC validation, and FPGA optimization. Education: Ph.D. in Computer Engineering (University of Illinois at Urbana-Champaign, 2019) M.S. in Computer Science (IIT Kharagpur, 2012) B.E. in Electronics Engineering (Jadavpur University, 2008) Research Interests: Machine Learning for Electronic Design Automation (EDA) System-on-Chip (SoC) Verification Edge Intelligence as a Service Compiler Optimizations for Reconfigurable and High-Performance Computing Scientific Awards: IEEE CEDA Student Research Award (2016) Best Paper Nomination (ICCAD 2015, DAC 2018, ASP-DAC 2019) E. J. McCluskey Best Doctoral Thesis Competition Semi-Finalist (2020) Travel Grants for ICCAD/DAC/ASPDAC (2018-2019) Professional Roles: Technical Program Committee Member (DAC, VLSID), Reviewer (IEEE TVLSI, DATE, ICCAD). Collaborates with researchers like Zhiru Zhang and Shobha Vasudevan.
Sanghyun Hong is an Assistant Professor at Oregon State University's School of Electrical Engineering and Computer Science , focusing on Trustworthy AI and Cybersecurity . He holds a Ph.D. in Computer Science from the University of Maryland, College Park (2021) and a B.S. in Electrical Engineering and Computer Science from Seoul National University (2015). His research bridges machine learning , security , and privacy-preserving systems . Current research themes: Robustness of AI systems to adversarial attacks Privacy-preserving machine learning Security of pre-trained and large language models Hardware fault vulnerabilities in neural networks Cybersecurity workforce development Publication Trends (15 most recent): Focus on adversarial machine learning (jailbreaking LLMs, membership inference) Advances in physics-informed neural networks and time series forecasting Key contributions to AI security and malware detection Interdisciplinary work in visualization design and tsunami warning systems Scientific Accolades : Google Faculty Research Award (2023) Samsung Global Research Award (2022, 2023, 2024) DARPA Riser (2022) NSF SFS Award (co-PI, 2023) USENIX Enigma Speaker (2021) Academic Leadership : Mentors 5 Ph.D. students and has graduated 8 M.S. and B.S. students. Currently developing next-generation auditing frameworks for AI systems while on medical leave until Winter 2026.
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
Supratik Chakraborty serves as the Bajaj Group Chair Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He maintains dual affiliations with the Centre for Formal Design and Verification of Software and the Centre for Liberal Education at IIT Bombay, demonstrating his cross-disciplinary engagement. Professor Chakraborty's research spans formal methods with focus on formal verification, rigorous analysis of system models, and automated synthesis of systems from specifications. His work bridges theoretical foundations with practical applications, particularly in developing mathematically provable guarantees for increasingly complex hardware, software, and intelligent systems. Current research interests include constrained counting and sampling, scalable formal verification of software and hardware systems, automated synthesis of programs and circuits, and applications of automata, logic and finite model theory to practical verification challenges. His publication trajectory shows a significant evolution from traditional hardware and software verification toward addressing verification challenges in machine learning and AI systems. Recent work increasingly focuses on interpretability of black-box models, verification of neural networks, and synthesis techniques applicable to intelligent systems. The research demonstrates strong interdisciplinary connections between formal methods, programming languages, and artificial intelligence. IIT Bombay Excellence in Thesis (CSE) Award 2011 (for Bhargav Gulavani's thesis) IIT Bombay Excellence in Thesis (CSE) Award 2017 (for Abhisekh Sankaran's thesis) Best Paper in Algorithms and Architecture track at IEEE International Conference on Computer Design: VLSI in Computers and Processors, 1998 Professor Chakraborty has successfully supervised 11 doctoral students, with research spanning formal verification techniques, Boolean functional synthesis, constrained counting, and applications to hardware and software systems. His students have gone on to positions at major institutions including Microsoft Research, TCS Research, Georgia Tech, and BARC, reflecting the strong industry and academic impact of his mentorship. Current research directions show increasing emphasis on verification challenges posed by machine learning systems and AI. His research group at IIT Bombay, while not explicitly named in the materials, appears to focus on formal methods with strong connections to the Centre for Formal Design and Verification of Software. The group maintains active collaborations with international researchers including Moshe Y. Vardi at Rice University, and has made significant contributions to verification tools like VeriAbs that bridge theoretical advances with practical applications.
Prof. Dr. Sebastian Steinhorst is an Associate Professor (W3-level with tenure) at the Technical University of Munich (TUM) within the Embedded Systems and Internet of Things group at the TUM School of Computation, Information and Technology . His research focuses on advancing the security, predictability, reliability, and interoperability of smart connected and autonomous systems, particularly for applications in Internet of Things (IoT) , Industry 4.0 , and automotive systems . PhD in Computer Science (2011) from Goethe University Frankfurt Postdoctoral roles at TUMCREATE Singapore (2011-2016) and Aarhus University (2016) Joined TUM in 2016 as Rudolf Moessbauer Tenure Track Professor His research areas include decentralized embedded systems, hardware/software co-design, modeling and verification of cyber-physical systems, security protocols for automotive networks, and time-sensitive networking (TSN) for industrial applications. Recent work explores blockchain-based data sovereignty, zero-knowledge proofs for vehicle authentication, and resilient architectures for autonomous systems. Key scientific contributions include the 2019 ACM TODAES Best Paper Award and pioneering work on CyberSecDome , LeapChain , and Simutack frameworks. He serves on editorial boards and conference committees, including co-organizing the Autonomous Systems Design initiative at DATE. His teaching portfolio spans lectures on System Design for IoT , Software Architecture for Distributed Systems , and IoT Security across multiple semesters. He also leads advanced seminars on embedded systems and IoT.
Thomas Gray is an Assistant Professor in the Mechanical Engineering Department at Texas A&M University, affiliated with the Mike J. Walker ’66 Department. His research focuses on Human Strength Amplification, Wearable Robotics, and Control Systems, with a particular emphasis on exoskeleton design and biomechanical interaction. He leads the HERC Lab, aiming to advance direct control paradigms for physically interactive robots. Educational Background : Ph.D., Mechanical Engineering, University of Texas at Austin (2019) B.S., Engineering: Robotics, Olin College of Engineering (2012) Research Interests : Gray’s work centers on enhancing human performance through advanced robotic systems. Key areas include: Development of wearable devices for strength amplification and fatigue mitigation Design of series-elastic actuators and force/torque feedback mechanisms System identification for robust control in dynamic environments Optimization of mechanical impedance rendering for natural human-robot interaction Awards & Recognition : IEEE ICRA Best Manipulation Paper Award (2017) IJHR Best Paper Award (2016) NASA Space Technology Research Fellowship (2015) DARPA Virtual Robotics Challenge Winner (Team IHMC, 2013) Grants & Advising : Gray has secured significant funding for his research, including grants from NASA and DARPA. He advises students in robotics and control systems, though specific student names are not listed. Labs & Teams : He directs the Human-Empowering Robotics and Control (HERC) Lab, which explores next-generation robotics for human augmentation and direct control methodologies.
Professor Tulika Mitra is the Dean of the School of Computing and Vice Provost (Special Projects) at the National University of Singapore (NUS). She holds the Provost’s Chair Professor in the Department of Computer Science and has been instrumental in shaping academic policies and strategic initiatives at NUS since joining in 2001. PhD in Computer Science, Stony Brook University (2000) M.E. in Computer Science, Indian Institute of Science (1997) B.E. in Computer Science, Jadavpur University (1995) Her research focuses on hardware-software co-design for energy-efficient computing systems, particularly in real-time embedded systems, heterogeneous architectures, and AI accelerators. She leads major research programs such as the NRF Competitive Research Programme on Low-Power Edge Accelerators and the MOE Tier-3 Programme on Green AI , collaborating with industry leaders like ARM, AMD, and Meta. Her recent publications highlight innovations in CGRA optimization , sparse attention mechanisms , photonic-digital hybrid architectures , and low-power ML inference . These works often integrate compiler techniques, architectural design, and real-time constraints for edge computing applications. Scientific Awards : ESWEEK Test-of-Time Award (2022), ACM SIGDA Distinguished Service Award, IEEE CEDA Outstanding Service Recognition Award, Teaching Excellence Award (2006), and multiple best paper recognitions. Education Leadership : Spearheaded the Computer Engineering (CEG) Programme at NUS, a joint initiative between Engineering and Computing. As a mentor , she has supervised over 25 PhD students , many now in prominent academic or industrial roles. Her research group eCO Lab focuses on embedded computing challenges, while her grant collaborations include projects on 5G base stations, reconfigurable architectures, and IoT-optimized SoCs.
Ardalan Amiri Sani is an Associate Professor in the Computer Science Department at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. He leads the Trustworthy Systems Lab (TrussLab), focusing on secure and reliable mobile systems, operating systems, and virtualization. His research bridges mobile computing, security, and OS design, addressing challenges in I/O devices, kernel hardening, and verifiable provenance. Education: Ph.D. and M.Sc. from Rice University (ECE), B.Sc. from Sharif University of Technology. Awards include the NSF CAREER Award, Google ASPIRE Award, and UCI Dean's Mid-Career Research Award. He has advised numerous graduate and undergraduate students, many of whom now work in academia and industry. Research highlights include Tabellion (secure legal contracts on mobile devices), ProvCam (verifiable video provenance), and work on minimizing smartphone TCBs via hardware isolation. His grants include NSF funding for OS kernel security and NSA support for provenance systems. Education: Ph.D., Electrical and Computer Engineering, Rice University, 2014 B.Sc., Electrical Engineering, Sharif University of Technology Grants: $500K NSF Award (with UCR) for OS kernel security (2020) NSF SaTC Award for GPU security in browsers Google ASPIRE Award for Android system call filtering Labs/Teams: Trustworthy Systems Lab (TrussLab), collaborating with industry partners like Intel and Broadcom.
Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.
Daniel Balasubramanian is an Adjunct Associate Professor of Computer Science and Research Scientist at Vanderbilt University's School of Engineering. His research focuses on cybersecurity, software verification, and cyber-physical systems, with expertise in symbolic execution, code analysis, and formal methods. He contributes to advancing secure systems through frameworks like RAMPART for adversarial defense and Syntheto for formal verification. His work intersects edge computing, hardware security, and autonomous systems resilience. Research Interests: Cybersecurity (including ethical hacking, network defense), formal methods (verification, theorem proving), edge computing (tinyML, cloud integration), and cyber-physical systems (emulation, testbeds). His recent work emphasizes assurance provenance in software documentation and adversarially robust autonomous systems. Publications since 2019 highlight contributions to cybersecurity testbeds, reinforcement learning for penetration resistance, and hardware security against rowhammer attacks. He has explored domain-specific languages (Syntheto), incremental modeling techniques (differential-formula), and cloud-edge service resilience against adversarial perturbations. Labs/Teams: Affiliated with the Institute for Software-Integrated Systems (ISIS), focusing on integrating software with physical systems through model-driven approaches and cybersecurity innovations.
