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.
Dr.-Ing. Yunshuang Yuan is a Researcher at the Institute of Cartography and Geoinformatics, Leibniz University Hannover. She holds a B.Sc. in Building Electrical Engineering and Intelligentization from Beijing University of Civil Engineering and Architecture (2010-2014), followed by an M.Sc. in Navigation and Environmental Robotics from Leibniz University Hannover (2017-2020). Since 2020, she has been a Research Associate, focusing on collective perception for autonomous driving, point cloud processing, and deep learning applications in geoinformatics. Her research interests span collaborative perception systems, LiDAR data fusion, and semantic segmentation for historical maps. Key projects include the DFG-funded Collective Perception - Data Fusion and Visualization (2020) and the development of datasets like COMAP and LuCoop. She actively supervises Master's theses on topics such as reinforcement learning for data selection and semantic segmentation of historical maps. Her work addresses challenges in autonomous vehicle perception, including efficient data transmission, real-time environmental mapping, and accessibility for visually impaired users through metadata generation. Collaborations with BMW Munich and contributions to IEEE and ISPRS journals highlight her interdisciplinary impact.
Professor Tolga Bektas is a faculty member at the University of Liverpool Management School, holding the rank of Professor. Previously, he served as Professor of Logistics Management and Head of the Decision Analytics & Risk Department at Southampton Business School for 11 years. He holds a PhD in Industrial Engineering from Bilkent University (2005) and completed a postdoctoral fellowship at CIRRELT, University of Montreal. His research focuses on applying mathematical modeling and optimization to freight transportation, distribution planning, and supply chain networks, emphasizing environmental sustainability. Key projects include railway timetable optimization, maintenance planning for sea vessels, and last-mile urban logistics. He has secured funding from InnovateUK, EPSRC, and others for initiatives like SCALE, Freight Traffic Control 2050, and FTC2050. Professor Bektas actively contributes to academic leadership, serving as Editor for Computers & Operations Research , Transportation Science , and Networks . He has organized conferences such as ICCL 2017 and presented keynote addresses on topics like green vehicle routing and urban last-mile logistics. His professional roles include Associate Dean International at Liverpool and past Treasurer of the Southern OR Group. His teaching responsibilities include coordinating modules on Business Analytics (EBUS205/EBUS305) and teaching Operations Modelling and Simulation (EBUS504). His work bridges academia and industry, collaborating with SMEs to enhance operational efficiency through analytical approaches.
Dr. Bo Tang is an Associate Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI), with collaborative appointments in Computer Science and Data Science. His research focuses on bio-inspired AI, AI security, edge AI, and applications in Cyber-Physical Systems such as wireless networks, autonomous vehicles, and power systems. He holds a Ph.D. from the University of Rhode Island (2016) and previously served as an Assistant Professor at Mississippi State University (MSU). Education: Ph.D., University of Rhode Island, 2012–2016 M.S., Chinese Academy of Sciences, 2007–2010 B.E., Central South University, 2003–2007 Research interests include bio-inspired neural networks, AI security frameworks, edge computing for IoT, and autonomous systems. He leads the Computational Intelligence and Learning Systems (CILS) Lab, developing AI solutions for real-world challenges like secure federated learning, underwater robotics, and smart grid resilience. Notable awards include the NSF CAREER Award (2021), NIJ Early Career Award (2019), and MSU's Emerging Research Scholar Award (2022). He is a Senior IEEE Member and Associate Editor for IEEE Transactions on Neural Networks and Learning Systems . Grants and Projects: NSF CISE CCRI Grant ($1.8M, Co-PI, 2021) NTIA Grant ($1.9M, 2023) ONR Grant ($249K, Co-PI, 2019) Lab & Teams: The CILS Lab collaborates on projects like Open AI Cellular (OAIC), secure O-RAN networks, and autonomous vehicle navigation. Recent student projects include federated learning algorithms and underwater robot localization.
Qingzhao Zhang is an incoming Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, starting in Fall 2025. Previously, he completed his Ph.D. at the University of Michigan under the supervision of Professor Z. Morley Mao. His academic journey bridges computer science, electrical engineering, and cybersecurity disciplines with a focus on practical security applications. Dr. Zhang's research spans computer security at the intersection of system security, artificial intelligence, and cyber-physical systems. His work addresses critical security challenges in safety-critical applications, particularly connected and autonomous vehicles. His research methodology combines formal verification, system building, and security analysis to develop practical solutions for real-world security problems. His expertise extends to blockchain security, industrial control systems, and AI security, demonstrating a broad yet cohesive research vision. Analysis of Zhang's recent publications reveals a consistent focus on security challenges in emerging technologies, with a particular emphasis on autonomous systems. His work demonstrates strong technical depth across multiple domains, from low-level network protocols to high-level AI systems. The trajectory of his research shows increasing sophistication in addressing complex security problems in cyber-physical systems, with recent work expanding into large language model security. Reviewer for ICLR 2025, ICRA 2025, ACM Multimedia 2024 Artifact Evaluation Reviewer for Usenix Security 2022 Pre-Review Task Force for NSDI 2025 Teaching experience in Multidisciplinary Design Program at University of Michigan (2022-2023) Dr. Zhang is actively recruiting PhD students for Fall 2026 and research interns. His research group focuses on developing security solutions for next-generation AI and cyber-physical systems, with particular emphasis on autonomous vehicles and critical infrastructure security. He maintains strong industry connections through his applied research in automotive security and blockchain technologies.
Johnathon P. Ehsani is an Associate Professor at the Johns Hopkins Bloomberg School of Public Health, with primary affiliation in the Department of Health Policy and Management and joint appointments in Health, Behavior and Society and the Whiting School of Engineering. He is actively engaged in research and teaching related to transportation safety and public health policy. PhD, University of Michigan, 2012 MPH, University of Sydney, 2003 BS, University of Technology, Sydney, 2000 Dr. Ehsani's research centers on injury prevention and transportation safety, utilizing policy analysis and behavioral research to reduce motor vehicle crashes. His work spans teen driving behavior , distracted driving policies , licensing systems , and the public health implications of emerging mobility technologies such as e-scooters, bike-share, and autonomous vehicles. He employs experimental, observational, and population-based methods, including naturalistic driving studies, to evaluate real-world driver behavior and policy effectiveness. His recent research emphasizes equity in transportation and the integration of public health principles into automated vehicle development. The 15 most recent publications reflect a strong focus on adolescent driving safety, mental health and driving risk, transportation equity, and the safety impacts of new mobility services. His work combines epidemiological rigor with policy relevance, often addressing vulnerable populations such as teenagers and children in rideshare vehicles. The research spans disciplines including public health, behavioral science, urban planning, and engineering. Scientific Awards and Honors: Leon S. Robertson Faculty Development Chair in Injury Prevention (2016–2019) NIH Intramural Research Training Award (2012–2016) Eno Transportation Fellow (2014) Outstanding New Researcher, University of Michigan School of Public Health (2009) Sir John Monash Scholar (2008–2011) Dr. Ehsani leads active research projects, including the Logbook Research Study on teen driver safety. He serves on the Academic Advisory Council of PAVE (Partners for Automated Vehicle Education), contributing expertise on public trust and safety in autonomous vehicles. While student advisement is not explicitly detailed, his leadership in funded research and collaborative projects indicates a supervisory role in training junior researchers. His work is supported by grants and institutional funding, though specific grant titles are not listed in the provided text. He is affiliated with interdisciplinary teams bridging public health, engineering, and policy, reflecting the integrative nature of his research on transportation systems and health outcomes.
Bochen Jia is an Associate Professor at the University of Michigan-Dearborn's College of Engineering and Computer Science , specializing in Industrial and Manufacturing Systems Engineering . With a background in Industrial & Systems Engineering (PhD and MS) and Engineering Mechanics (MS and BS), he leads research on human factors, ergonomics, and human-exoskeleton interactions. Research Highlights: Investigates worker capacity changes under modern occupational risks Led ASTM International task group to establish exoskeleton digital modeling standards Explores ergonomic challenges in connected/autonomous vehicle design Develops wearable systems for posture and fatigue monitoring Advances pedestrian safety through AR/VR interface design Publications Trends show expertise in biomechanics, AI-driven workload assessment, autonomous vehicle-pedestrian communication, and STEAM education integration over 2018-2025. Scientific Awards include 11 grants from organizations like ASTM International, Michigan Mobility Transformation Center, and Ford Motor Company. Key projects involve exoskeleton standards, automated driving education, and pedestrian safety technologies. Teaching Innovation includes integrating ergonomic competitions, practical engineering tools, and lab development for automotive education. Established a multipurpose lab with external funding to prepare students for future mobility challenges.
Dr. Yi Lu Murphey is a Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. She directs the Intelligent Systems Lab and holds editorial responsibilities at the Journal of Pattern Recognition. Ph.D. in Computer/Information/Control Engineering (1989) from University of Michigan M.S. in Computer Science (1983) from Wayne State University Her research focuses on machine learning applications for: Automotive diagnostics and prognostics Vehicle power management optimization Robotic vision systems Driver behavior analysis Medical data processing Recent publications demonstrate expertise in: Deep learning architectures Autonomous vehicle systems Biomedical signal processing Power electronics optimization Multimodal data fusion Intelligent transportation Scientific honors include: IEEE Fellow Major funding sources: National Science Foundation National Institutes of Health Toyota Research Institute Ford Motor Company Nissan USA Her lab develops technologies for: Driver workload estimation Pedestrian detection Personalized route prediction Vehicle context detection Electric vehicle energy management
Ya Sha (Alex) Yi is a Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn, affiliated with the College of Engineering and Computer Science, the Energy Institute, and the Lurie Nano Fabrication Facility. He holds a Ph.D. in Electronics and Optoelectronics from the Massachusetts Institute of Technology. Education: Ph.D., Electronics and Optoelectronics, MIT His research focuses on chip-scale intelligent optoelectronics with applications in Silicon Photonics , Artificial Intelligence Chips , Autonomous Driving , Renewable Energy , and Quantum Photonics . The lab develops 3D Integrated Photonic Circuits and Smart Sensor Technologies through collaborations with MIT, 3M, and national laboratories. Key scientific contributions include foundational work on Thin-Film Solar Cells and On-Chip Biomedical Sensors , with over 150 top-tier publications and 34 issued patents. The lab's innovations have generated over $300 million in industry revenue and were recognized as a breakthrough by MIT Technology Review . Fellow, Optical Society of America (2020) Presidential Fellowship, MIT Rosenblith Fellowship, MIT Guang Hua Prize (Peking University's highest scholarship) Marquis Who’s Who in Science and Technology The Intelligent Optoelectronics Chip Laboratory, led by Prof. Yi, licenses patents globally and collaborates with Los Alamos National Laboratory, 3M, and MIT. The lab actively recruits Postdoctoral Associates, Research Associates, and PhD students. He also serves as Associate Editor for the IEEE Photonics Society Newsletter.
Faizan Mir is a Researcher II-Computational Science at the National Renewable Energy Laboratory (NREL) , affiliated with the Center for Integrated Mobility Sciences and the Transportation and Mobility Research Topic . His work focuses on computational science applications in transportation systems, particularly intelligent transportation and autonomous vehicles. Research Interests : Intelligent Transportation Systems (ITS) Sensor Calibration and Fusion GNSS Integration for Mobility Spatiotemporal Data Analysis Digital Twin Technology Transportation Safety Engineering Article Trends : His publications emphasize sensor fusion techniques (Lidar, Radar, Camera) for infrastructure-based perception in autonomous vehicles, GNSS-assisted calibration, and adaptive traffic management systems. The work spans safety-critical LED signaling, multisensor data integration, and cooperative perception frameworks.
Sangyoung Park is a Junior Professor at the Technical University of Berlin, holding the FGL Junior Professorship for Development of Digitalized Transport Technologies (Smart Mobility Systems). Based in the Main Building (Room H 4133) at Straße des 17. Juni 135, 10623 Berlin, Dr. Park leads research at the intersection of transportation engineering, electrical systems, and digital technologies to advance sustainable mobility solutions. Dr. Park's research focuses on several critical areas in modern transportation systems: Smart Mobility Systems and Digitalized Transport Technologies Electric Road Systems and Vehicle Electrification Battery Management Systems for Electric Vehicles Connected and Autonomous Vehicle Technologies Teleoperated Driving Systems with Network Delay Compensation Integration of Renewable Energy in Transportation Urban Infrastructure Interconnectivity and Resilience Analysis of Dr. Park's recent publications reveals a strong emphasis on solving practical challenges in sustainable transportation. The research demonstrates a systematic approach to integrating digital technologies with physical transport infrastructure, particularly focusing on electric vehicle systems, battery management, and vehicle-to-infrastructure communication. A notable trend is the development of digital twin technologies for teleoperated driving systems that can compensate for network delays, addressing a critical challenge in remote vehicle operation. The work also shows increasing attention to the integration of renewable energy sources with electric mobility solutions, particularly through photovoltaic integration on light electric vehicles. Dr. Park's research has practical applications across multiple domains of transportation engineering, from heavy-duty vehicle electrification to urban mobility solutions for light electric vehicles. The interdisciplinary nature of the work bridges electrical engineering, computer science, and transportation planning to develop holistic solutions for sustainable mobility.
Dr. Dirk W. Donker serves as Full Professor in Cardiovascular and Respiratory Physiology at the University of Twente, where he is deeply integrated with the interdisciplinary TechMed Centre. His academic leadership spans critical care physiology with specialized focus on extracorporeal life support systems and respiratory monitoring in intensive care settings. Research interests prominently feature Extracorporeal Membrane Oxygenation (ECMO) applications, cardiogenic shock management, and neuro-ventilatory coupling in mechanically ventilated patients. His work bridges physiological modeling with clinical implementation, particularly in aging-related cardiovascular variability and advanced signal processing for non-invasive monitoring. Current investigations explore machine learning frameworks for blood pressure estimation and fluid responsiveness prediction in critical care. Recent publication trends reveal intensive collaboration in multinational clinical trials like REMAP ECMO, with strong emphasis on adaptive trial design for left ventricular unloading. His 2025 output demonstrates methodological innovation through physiological-model-based neural networks and systematic reviews of ICU monitoring techniques, consistently targeting translational applications in extracorporeal life support. Prof. Donker operates within the TechMed Centre ecosystem, which fosters engineering-medicine integration through cross-faculty initiatives. His collaborative network spans European critical care research groups, evidenced by co-authorship in major trials and systematic reviews addressing ECMO complications and hemodynamic monitoring challenges.
Associate Professor Rifai Chai is a faculty member in the Department of Biomedical Engineering at the School of Engineering, Swinburne University of Technology. He serves as the Academic Director (Partnerships), reflecting his leadership in academic-industry collaboration. His research focuses on the intersection of biomedical engineering and artificial intelligence, with applications in brain-computer interfaces, medical technologies, robotics, and embedded systems. University: Swinburne University of Technology School: School of Engineering Department: Biomedical Engineering Position: Associate Professor Email: rchai@swin.edu.au His educational and professional background includes over a decade of experience in product development in hardware, firmware, and software design in Indonesia and Australia from 2000 to 2011. His research interests are extensive and include: Artificial Intelligence and Machine Learning Brain-Computer Interfaces Medical Device Design Assistive Technology (e.g., smart wheelchairs, exoskeletons) Cognitive Fatigue and Workload Monitoring Back Pain Assessment and Rehabilitation Embedded Systems EEG and Physiological Signal Processing Rifai Chai's recent publications demonstrate a strong focus on AI-driven healthcare solutions. His work spans advanced computational intelligence in medical imaging (e.g., lung cancer and thyroid cancer detection), EEG-based systems for driver fatigue and emotion classification, rehabilitation technologies using exoskeletons, and cybersecurity in industrial IoT. He employs deep learning, hybrid models (e.g., MLP-BiLSTM), and advanced signal processing techniques to solve real-world biomedical problems. His research consistently targets practical, non-invasive, and real-time applications in clinical and industrial settings. His scientific contributions include numerous high-impact publications in journals such as IEEE Access, Sensors, and Medical & Biological Engineering & Computing, as well as book chapters with Elsevier and Springer. He has secured multiple research grants from industry partners and the Australian Research Council, supporting projects in breast electromagnetic scanning, solar-powered charging poles, and haptically-enabled motion simulation. Rifai Chai actively supervises PhD and Master’s students, with current HDR projects covering areas such as brain-computer interfaces for prosthetics, cognitive workload monitoring, distracted driving detection, and AI in medical imaging. He leads a multidisciplinary research team working at the forefront of biomedical innovation. His lab integrates AI, robotics, and physiological sensing to develop technologies that improve health outcomes and human performance.
