Andrew Kun is a Professor in the Department of Electrical and Computer Engineering at the University of New Hampshire, where he has served since 2000. His research bridges Human-Computer Interaction (HCI), autonomous vehicles, and pervasive computing, with a focus on driver interfaces and future work environments. Ph.D., Engineering, University of New Hampshire (1997) M.S., Electrical & Electronic Engineering Technology, University of New Hampshire (1994) B.S., Electrical & Electronic Engineering Technology, University of New Hampshire (1992) Kun's work explores how automation transforms mobility and remote work, emphasizing safety, inclusivity, and cognitive load management. He investigates interfaces for autonomous vehicles, hybrid conferencing, and AI-assisted collaboration, often leveraging wearable technology and multimodal interaction. His recent publications highlight trends in work-from-home practices post-pandemic, human-AI interaction in mobile environments, and adaptive interfaces for automated vehicles. Themes include balancing productivity with wellbeing, designing inclusive virtual spaces, and understanding cognitive shifts in hybrid settings.
Konstantinos Blekas is a Professor at the Department of Computer Engineering and Informatics, University of Ioannina, Greece. He is affiliated with the Polytechnic School and teaches advanced courses such as 'Machine Learning' (MYE002) and 'Probability and Statistics' (MYY304). His research focuses on Machine Learning, Intelligent Agents, Computer Vision, and Bioinformatics, with particular expertise in Reinforcement Learning, Deep Learning, and their applications in autonomous systems, traffic management, and aerospace engineering. Education: Ph.D. in Electrical and Computer Engineering, National Technical University of Athens (1997) Diploma in Electrical Engineering, National Technical University of Athens (1993) Research Interests: Dr. Blekas explores cutting-edge topics in machine learning, including generative adversarial networks (GANs), multi-agent systems, and reinforcement learning for autonomous navigation. His work spans domains such as unmanned surface vehicles, air traffic management, and medical informatics. Notable contributions include advanced frameworks for flight trajectory modeling, urban traffic optimization, and brain functional network analysis. Awards and Recognition: While specific awards are not explicitly listed, his extensive publications and contributions to AI and robotics reflect significant academic impact. Advising and Grants: He supervises research in machine learning applications, though specific student names or grant details are not provided in the texts. His courses emphasize practical implementation, with resources available on e-learning platforms like e-course.uoi.gr. Labs and Teams: Engaged in collaborative projects involving robotics and AI, though no specific lab names are mentioned. His work integrates interdisciplinary approaches across computer science, engineering, and biomedical fields.
Kevin Vincent is a Researcher at Coventry University, serving as Director of the Centre for Connected and Autonomous Automotive Research (CCAR) and the National Transport Design Centre (NTDC). His work bridges academia, industry, and government to advance future transport technologies. External positions include Associate Director, Connected Places Catapult (2023–2024), and advisory roles in the UK Automotive Council and National Digital Twin Hub Focus areas: digitalisation for safe connected/automated transport, scenario-based testing, lightweight low-carbon mobility, and human factors in remote monitoring Contributed to British Standards Institute guidelines (1887 Flex) for self-driving vehicle teleoperation Active in EU/nationally funded R&D projects, with over £20M secured during his career Collaborates across automotive, aerospace, and creative industries Supports UN Sustainable Development Goals through net-zero transportation research
Esen Yel is an Assistant Professor in the Electrical, Computer, and Systems Engineering (ECSE) department at Rensselaer Polytechnic Institute (RPI) since January 2024. She leads the Reliable Intelligent Systems Lab (RISL), focusing on enhancing safety in autonomous systems through planning, uncertainty-aware decision-making, and runtime monitoring. Her work integrates reachability analysis, machine learning, and adaptive control to ensure safe operations in unpredictable environments. Educational background: Ph.D. in Systems Engineering, University of Virginia (2021) M.S. and B.S. in Electrical and Electronics Engineering, Bogazici University, Turkey (2016 and 2014) Postdoctoral Scholar in Aeronautics and Astronautics at Stanford University (2021–2023), contributing to the Stanford Intelligent Systems Lab (SISL) and Stanford Center for AI Safety Research interests emphasize safety-critical autonomous systems , including: Uncertainty-aware planning and decision-making Runtime monitoring and recovery mechanisms Machine learning for adaptive control Formal verification of neural networks Robotics and UAV operations under degraded conditions Her publications (2017–2024) explore themes like spatiotemporal prediction, reachability analysis, meta-learning for UAVs, and safety validation in perception systems. She directs the RISL lab, advancing interdisciplinary research in reliable AI and robotics.
Chris Herdman is a Professor in the Department of Psychology at Carleton University, Canada, and Director of the Advanced Cognitive Engineering (ACE) Lab within the Visualization and Simulation Centre (VSIM). His research focuses on human perception, cognition, and their application to advanced human-machine systems, particularly in aviation and automotive contexts. He holds a Ph.D. from the University of Alberta. Research interests include cognitive health assessment for pilots, driver safety, and the integration of neurophysiological measures (e.g., EEG) to evaluate human performance in high-stakes environments. His lab collaborates across disciplines, involving students from Psychology, Cognitive Science, Biology, and Engineering. Recent work emphasizes developing tools like CANFLY for pilot risk assessment and studying driver engagement using machine learning. His studies often combine experimental methods with real-world applications, addressing challenges like age-related cognitive decline and automation in transportation. Key projects include investigating the effects of workload, sleep deprivation, and sensory integration on task performance. The ACE Lab also explores autonomous vehicle disengagement protocols and the impact of pandemic-related changes on driving behavior. Labs/Teams: Advanced Cognitive Engineering (ACE) Lab, Visualization and Simulation Centre (VSIM). Collaborations span academic, governmental, and industry partners.
Prof. Kui Wu is a Professor in the Department of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. His research focuses on computer networks, wireless and mobile networking, mobile computing, and network security. He is part of the Parallel, Networking and Distributed Computing (PANDA) research group. Key areas of expertise include distributed learning frameworks, autonomous systems, IoT anomaly detection, and edge computing architectures. His work integrates machine learning techniques with network optimization, addressing challenges in real-time systems, security, and resource allocation. Notable contributions include advancements in federated learning, privacy-preserving distributed systems, and UAV-based monitoring solutions. Prof. Wu's research also explores edge computing innovations, such as smart contract-aided IoT resource sharing and energy-efficient edge data centers. He has contributed to over 50 peer-reviewed publications, with recent work emphasizing AI-driven network design, anomaly detection in IoT, and reinforcement learning applications in autonomous driving safety. His research has practical implications for improving the reliability and efficiency of next-generation communication and computing infrastructures.
Fei Miao is a Pratt & Whitney Associate Professor at the School of Computing, University of Connecticut, and a courtesy faculty member of the Department of Electrical & Computer Engineering. She serves as Director of the Miao Embodied AI Lab and is affiliated with the Institute for Advanced Systems Engineering. Previously, she was a postdoc researcher at the GRASP Lab and PRECISE Lab with Professors George J. Pappas and Daniel D. Lee at the University of Pennsylvania. Dr. Miao received her PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2016, where she also earned a dual Master's degree in Statistics from the Wharton School. She completed her undergraduate studies at Shanghai Jiao Tong University, earning a Bachelor's degree in Automation with a minor in Finance in 2010. Her research focuses on developing the foundations for the science of Embodied AI, with emphasis on assuring safety, efficiency, robustness, and security of cyber-physical systems through the integration of learning, optimization, and control. Her technical expertise spans multi-agent reinforcement learning, robust optimization, uncertainty quantification, control theory, and game theory. These methods are applied to connected and autonomous vehicles, intelligent transportation systems, transportation decarbonization, smart cities, and power networks. Her work involves both theoretical development and practical implementation, including system modeling, theoretical analysis, algorithmic design, and experimental validation using real urban transportation data, simulators, and small-scale autonomous vehicles. Dr. Miao's publication record reveals a strong focus on robustness in AI systems for transportation applications, with recent work emphasizing uncertainty quantification, safety guarantees, and multi-agent coordination. Her research demonstrates a clear trajectory from foundational theoretical work to practical implementations in real-world transportation systems. Her notable awards include the prestigious NSF CAREER Award (2021) for "Distributionally Robust Learning, Control, and Benefits Analysis of Information Sharing for Connected and Autonomous Vehicles," a Best Paper Award at ICCPS'21 for "DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services," and the "Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation" during her PhD studies. Dr. Miao has secured significant research funding, including a $509,573 NSF CAREER Award (2021-2026) and a $2.3 million NSF collaborative grant as PI of UConn (2020-2023). She has also received multiple NSF grants for projects related to electric vehicle fleets, vehicular sensing, and control for smart city systems. She actively collaborates with researchers across institutions and has given talks at leading universities and industry research labs including CMU, Microsoft Research, Northeastern, Caltech, UCLA, USC, UCSD, Facebook FAIR, Lawrence Berkeley National Lab, UC Berkeley, Nvidia, Stanford, Princeton University, Columbia University, Waymo, and New York University.
Marcus Völp is an Associate Professor and head of the CritiX research group at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT). His research focuses on ultra-reliable operating systems, fault and intrusion tolerant systems for cyber-physical environments, and hierarchical hybridization techniques to construct resilient computing platforms. His work bridges theoretical foundations with practical implementations, particularly in developing dependable systems-on-chip that can withstand faults and intrusions. Research spans formal verification methods, radiation-hardened hardware, blockchain security, and autonomous vehicle safety systems. Recent publications demonstrate advancement in parameterized system verification, radiation effects on routers, and N-version machine learning for autonomous systems. His group maintains strong focus on practical resilience mechanisms for real-time and critical systems across computing layers.
Dr. Ying Liu is an Associate Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. She serves as the Secretary/Treasurer of the APSIPA US Chapter and holds multiple leadership roles including APSIPA Distinguished Lecturer (2025-26) and Vice Chair of the APSIPA US Local Chapter. Her research focuses on deep learning applications for visual data processing and compression. Education: Ph.D., Electrical Engineering, SUNY at Buffalo, 2012 M.S., Electrical Engineering, SUNY at Buffalo, 2008 B.S., Telecommunications Engineering, Beijing University of Posts and Telecommunications, 2006 Dr. Liu's research spans deep learning-based image/video processing , coding for machines , and generative AI . Her work integrates convolutional neural networks, transformers, and generative models to advance video compression and machine vision systems. Current projects include vision-language models and point cloud coding, with emphasis on computational efficiency for real-world deployment. Analysis of her 15 most recent publications reveals a strong focus on neural video compression (40%), image coding for machines (30%), and generative AI applications (20%), with increasing emphasis on transformer architectures and multi-task frameworks since 2022. Her research bridges theoretical innovation with practical industrial applications in manufacturing and autonomous systems. Scientific Awards: Researcher of the Year Award, School of Engineering, Santa Clara University (2024) APSIPA Distinguished Lecturer Appointment (2025-26) Dr. Liu actively mentors PhD students in the Video and Image Processing (VIP) Laboratory, with four current advisees including Pengli Du (first PhD graduate in 2024). Her research is supported by significant grants including an NSF ERI award ($500K, 2022-2025), NVIDIA Academic Hardware Grant, and multiple industry-funded projects with Kwai Inc. totaling over $300K. She also secures internal university funding through Kuehler Undergraduate Research Grants and School of Engineering Research Grants. The VIP Laboratory develops deep learning solutions for the projected 13 billion global cameras by 2030, focusing on applications for mobile video sharing, surveillance, and autonomous vehicles. Current projects utilize CNNs, GANs, RNNs, and transformers for visual coding efficiency.
Sam Thellman is an Assistant Professor in Cognitive Science at Linköping University, affiliated with the Department of Computer and Information Science (IDA) and the Human-Centered Systems (HCS) division. His research focuses on folk-psychological interpretations of AI systems, particularly how humans attribute mental states and intentions to robots and autonomous vehicles. He investigates human-AI interaction to design more predictable and explainable systems, with interdisciplinary work on human-vehicle interaction in self-driving car contexts. Education: Ph.D. in Cognitive Science (2021). Teaching responsibilities include the Bachelor’s program in Cognitive Science, courses on Distributed and Situated Cognition, and Applied Cognitive Science. His research spans human-robot interaction, mental state attribution, and autonomous vehicle behavior perception. Publications highlight topics such as robot emotional capabilities, driving simulation studies, and societal impacts of AI. His work emphasizes ethical and human-centric approaches to technology development. No scientific awards are explicitly listed, but his contributions to explainable AI and human-centered computing are significant.
Prof. Lionel Briand is a distinguished academic and researcher in software engineering, holding the Tier 1 Canada Research Chair in Intelligent Software Dependability and Compliance at the University of Ottawa's School of Electrical Engineering and Computer Science (EECS Department). He is also the director of Lero, Ireland's Research Centre for Software, and affiliated with the Nanda Laboratory. His roles span technical leadership, research, and academia across institutions in seven countries. Educated with a PhD, Briand's research focuses on trustworthy AI, software verification/validation, requirements engineering, and regulatory compliance. He pioneered model-driven development and search-based techniques in software engineering. His work bridges academia and industry, collaborating with sectors like aerospace, automotive, and finance. Key research trends in his articles include AI-driven testing methodologies, regulatory compliance tools (e.g., GDPR), safety-critical systems, and metamorphic testing for autonomous systems. His recent work emphasizes large language models (LLMs) for consensus-based test generation and safety monitoring of AI agents. Awards: IEEE/ACM Fellowships, Harlan Mills Award, ACM SIGSOFT Research Award Grants: ERC Advanced Grant, Canada Research Chairs Tools: CompAI (GDPR compliance), TEASMA (DNN testing), Smarla (safety monitoring) Labs/Teams: Leads Lero and collaborates with interdisciplinary teams at the University of Luxembourg's SnT center and Fraunhofer Institute. His work emphasizes synergies between Canadian and Irish research ecosystems.
Annika Stensson Trigell is a Professor and Vice President for Research at KTH Royal Institute of Technology, affiliated with the Division of Vehicle Dynamics under the Digital Futures Faculty. Her research focuses on vehicle dynamics, energy efficiency, and control systems, with particular emphasis on electric vehicles, tyre-vehicle interaction, and fault-tolerant systems. She teaches and examines courses in vehicle engineering, including ground vehicle dynamics and system technology. Her academic work spans over two decades, with publications covering topics such as energy-efficient yaw moment control, rolling resistance analysis, and autonomous vehicle coordination. She has contributed to advancements in vehicle safety, aerodynamics, and over-actuated systems. Her research integrates experimental and simulation-based methods, addressing challenges in both theoretical and applied mechanics. Key areas of investigation include optimizing vehicle performance under dynamic conditions, improving energy efficiency through advanced control strategies, and enhancing road safety through driver behavior analysis. Her work often bridges mechanical engineering principles with computational modeling, emphasizing interdisciplinary collaboration. Annika has led projects on autonomous parking systems, crosswind effects on vehicles, and tyre dynamics modeling. Her contributions have been published in journals like Vehicle System Dynamics , Energies , and International Journal of Vehicle Design , reflecting her deep engagement with both academic and industrial applications.
Swaminathan Gopalswamy is Research Professor in Texas A&M's Mechanical Engineering Department, specializing in autonomous systems and cyber-physical security. His research develops control systems for autonomous vehicles and infrastructure-enabled autonomy frameworks. Current projects include sensor fusion for multi-vehicle tracking, health monitoring for cyber-physical systems, and resilient algorithms for autonomous navigation. His work bridges control theory, embedded systems, and intelligent transportation applications.
Associate Professor at Nanyang Technological University's College of Computing and Data Science, serving as Deputy Director of the Cyber Security Research Centre @ NTU (CYSREN) and Associate Director of the NTU Centre Computational Technologies for Finance (CCTF). His research focuses on building trustworthy, efficient, and intelligent systems with emphasis on security and privacy across AI, robotics, and cloud infrastructures. Educational background: Bachelor of Physics, Peking University, 2011 Ph.D. in Electrical Engineering, Princeton University, 2017 Research spans five core domains: Generative AI Safety (vulnerability identification, safety testing, misuse detection), Deep learning security (adversarial examples, backdoor attacks, privacy protection), Robotics security (perception system attacks, safety testing), Machine learning optimization (workload scheduling, acceleration), and Computer architecture security (side-channel defenses, cloud security). His work bridges theoretical security with practical system implementations across diverse applications. Recent publications (2024-2026) reveal intense focus on securing generative AI systems, particularly text-to-image models and large language models, with significant contributions to red teaming methodologies, backdoor attack mitigation, and multimodal security. Emerging trends show expanding research into autonomous vehicle security and privacy-preserving machine learning with cryptographic techniques. Key awards include: Distinguished Artifact Award (CCS 2024) Stamatis Vassiliadis Best Paper Award Nominee (FPL 2024) Outstanding Paper Award (ACL 2024) Distinguished Artifact Award (Usenix Security 2024) Actively supervises PhD students and research staff while leading multiple high-impact grants: Ongoing: NRF CREATE Quantum Security (2025-2029), Continental NTU Corp Lab Automotive HPC (2025-2028), CRPO EV Charging Security (2025-2027) Completed: MoE AcRF Tier2 IP Protection (2022-2025), NTU S-Lab Efficient GPU Scheduler (2020-2025) Leads research within CYSREN and TAICeN (Trustworthy AI Centre NTU), directing interdisciplinary teams that investigate security threats across AI deployment stacks while developing practical defenses for real-world systems.
Professor Anne Marie Frøseth holds a position at the Faculty of Law, University of Bergen. She specializes in interdisciplinary legal research focusing on artificial intelligence liability, civil liability frameworks, insurance systems, and discriminatory speech regulation. Her work emphasizes comparative studies of European legal systems and the role of EU regulations in shaping national tort law. Her research groups include the Research Group for Legal Theory, Tort Law and Insurance Law, and Property Law. She teaches and supervises master's theses in tort law (JUS213). Key publications address AI liability in autonomous vehicles, user liability models, and legal responses to hate speech in social media. Frøseth's recent work explores the intersection of AI innovation and legal accountability, including strict liability principles in French and Norwegian law. She has contributed to international projects like the Institute of European Tort Law's studies on personal injury claims and hospital liability across Europe. Her writing spans over two decades, with notable contributions to topics like hybrid sanctions for harassment, punishment for hate speech, and the evolution of Norwegian tort law. No formal awards are cited, though her extensive publication record reflects academic recognition.
