Prof. Dr. Andreas Herkersdorf is a Full Professor and Chair of Integrated Systems at the Technical University of Munich (TUM) School of Computation, Information and Technology. His research focuses on application-specific multicore processors (MPSoC), FPGA-based prototyping, fault-tolerant systems, and energy-efficient architectures, with applications in IP packet processing, automotive systems, and visual computing. He has received multiple IBM innovation awards and serves on editorial boards including the DFG Review Board for computer architecture. Education: Dipl.-Ing. Electrical Engineering (TUM, 1987), Dr. techn. Electrical Engineering (ETH Zurich, 1991) Research: MPSoC architectures, autonomic computing, NoC resilience, FPGA acceleration, and self-optimizing systems. Awards: IBM Master Inventor (1998), IBM Outstanding Technical Achievement Award (2001), multiple IBM Innovation Achievement Awards (1996-2003) His recent publications emphasize hardware/software co-design, machine learning integration for runtime optimization, and network-on-chip innovations. He collaborates on projects involving 6G systems, smartNICs, and automotive communication protocols.
Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.
Professor Ai-Chun Pang is affiliated with the National Taiwan University , serving in both the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia . He held leadership roles including Associate Dean (2018-2022) and Director (2013-2016) within the College of Electrical Engineering and Computer Science. His research spans Fog/Edge Computing , Wireless Networking , Mobile Computing , and AIoT Systems , with recent advancements in federated learning security, energy-efficient network design, and 5G/6G optimization. Collaborative work includes applications in vehicular networks, industrial control systems, and non-terrestrial connectivity. Key publication themes: Edge Intelligence and Privacy (2024) Federated Learning for Heterogeneous Devices (2023-2024) 5G Backhaul Optimization (2017-2021) Wireless Energy Transfer (2022) Awarded IEEE Fellow 2021 for contributions to mobile edge networks, he has received multiple IEEE Vehicular Technology Society awards, the CES 2019 Innovation Award , and teaching accolades including National Taiwan University Distinguished Teaching Award (2010) . His lab has produced 16 PhD students now in academia and industry. As Editor-in-Chief of IEEE Wireless Communications Letters and active in conference organization, he shapes global research directions. Current projects focus on GenAI for Networking and Non-Terrestrial Networks , with recent 2024 admissions for new students.
Nicola Bezzo serves as an Associate Professor at the University of Virginia with dual appointments in the Department of Systems Engineering and the Department of Electrical and Computer Engineering. He leads research through the AMR Lab and is affiliated with the university's Link Lab, focusing on autonomous systems safety and resilience. His work bridges theoretical control frameworks with practical robotic implementations, particularly in constrained and uncertain environments. Bezzo's research centers on developing fundamentally new approaches for safe and resilient autonomous operations, with three core thrusts: (1) Control Barrier Functions integrated with Lyapunov stability theory for provably safe navigation; (2) Epistemic planning frameworks that enable robots to reason under uncertainty using active inference principles; (3) Sim-to-real transfer techniques leveraging conformal mapping for robust deployment. His work consistently addresses the critical challenge of maintaining system integrity when operating under sensor limitations, communication constraints, and unexpected environmental disturbances. Recent publications demonstrate increasing focus on heterogeneous multi-robot coordination for emergency response scenarios and human-robot teaming where predictability is paramount. Analysis of Bezzo's 15 most recent publications reveals a strong trend toward adaptive safety frameworks that dynamically adjust to environmental uncertainty. Over 70% of his 2024-2025 work incorporates machine learning components (particularly Gaussian Processes and reinforcement learning) within traditional control architectures, creating hybrid approaches for resilient navigation. The research spans both aerial (UAV) and ground (UGV) platforms with growing emphasis on cross-domain coordination. A distinctive pattern is the development of 'recovery-first' paradigms that prioritize system restoration after failures rather than solely preventing failures. Bezzo directs the Autonomous Mobile Robotics (AMR) Lab and collaborates extensively with UVA's Link Lab, a cross-disciplinary research center focused on cyber-physical systems. His lab develops experimental testbeds for evaluating navigation algorithms in physically realistic environments, including constrained indoor spaces and communication-denied scenarios. Current projects involve robotic triage systems for disaster response and resilient swarm operations for infrastructure inspection, often featuring heterogeneous robot teams combining aerial and ground vehicles.
Prof. Gabriele Kaiser is a Full Professor of Mathematics Education at the University of Hamburg’s Faculty of Education. She holds a part-time professorship at the Norwegian University of Science and Technology and an honorary professorship at the Australian Catholic University (ACU). Her research focuses on mathematical modeling, teacher professional competence, and comparative studies in education (East-West). She led the ICME-13 conference in 2016 and serves as former Editor-in-Chief of ZDM Mathematics Education. Key contributions include the TEDS research program on teacher education and the development of video-based assessment tools for teacher noticing. Awards include top Springer downloads for her edited volumes and leadership in international educational initiatives. Roles: Chair of ICME-13, Editor of ZDM, ICMI Representative, ICTMA President Affiliations: University of Hamburg, ACU Brisbane, University of Macau collaborations Research Interests: Mathematical modeling, teacher education, inclusive mathematics, flipped classrooms, and cross-cultural studies Her work emphasizes innovation in teacher training, classroom observation methodologies, and addressing diversity in mathematics education. Recent projects include studies on Chinese and German teachers’ competencies and the use of AR/VR in STEM education. Awards: Honorary professorships in Australia and Hong Kong, board member of Hamburg Scientific Foundation, and recognition for impactful publications.
Dr. Crystal Fausett is an Assistant Professor in the School of Information at San José State University. Her academic work bridges human factors, cybersecurity, and human-computer interaction, with a strong focus on how individuals and teams interact with technology in high-stakes environments such as healthcare, emergency services, and cyber operations. She teaches courses including ISDA 121 – Human Centered Cybersecurity and ISDA 130 – User Centered Interface Architecture and Prototyping, reflecting her interdisciplinary expertise. Ph.D. in Human Factors, Embry-Riddle Aeronautical University (2024) M.S. in Human Factors, Embry-Riddle Aeronautical University (2022) B.A. in Psychology, San José State University (2020) A.A. in Social and Behavioral Sciences, Santa Barbara City College (2018) Her research interests include human-centered cybersecurity, human-computer interaction, usability and UX design, teamwork and transactive memory systems, training methodologies (particularly simulation-based), and information behavior across diverse user groups such as healthcare providers, cyber defenders, and naval personnel. She applies both qualitative and quantitative methods, including expert interviews, board game simulations, and meta-analytic techniques. The recent publications highlight a strong trend in applying human factors principles to cybersecurity team performance, adaptive training, and healthcare safety. Her work increasingly explores innovative methods such as gamified simulations to study team dynamics and cognitive load in complex operational environments. There is a consistent emphasis on improving system efficiency, safety, and collaboration through human-centered design. Dr. Fausett has no listed scientific awards in the provided text. She has served as an Instructor of Record in Human Factors and Behavioral Neurobiology at Embry-Riddle Aeronautical University (2023–2024) prior to her current role. There is no mention of current grant funding or student advising in the provided materials. Her research often involves collaboration with experts in human factors and healthcare, particularly Dr. Joseph R. Keebler and Dr. E. Salas, suggesting active participation in research teams focused on team performance and safety-critical systems. She is involved in research labs or teams centered on human factors in cybersecurity and healthcare, particularly through collaborative projects involving simulation-based training and team cognition studies. Her use of board games like [d0x3d!] as experimental testbeds indicates innovative lab-based methodologies for studying real-world team interactions in controlled environments.
Laura Crosswell serves as Associate Professor of Health Communication and Director of the Center for Advanced Media Studies at the University of Nevada, Reno's Reynolds School of Journalism and Media Studies. She holds a joint appointment with the university's School of Medicine, reflecting her interdisciplinary approach to health communication research. Crosswell previously taught at the College of Charleston, Louisiana State University, and Arizona State University before joining UNR. Her research focuses on the intersection of consumerism, persuasive texts, and public health messaging, with particular attention to how commercial interests influence health communication. Crosswell frequently employs advanced eye-tracking technology to examine physiological and psychosocial responses to media content, especially in pharmaceutical advertising and public health campaigns. Her book Politics, Propaganda, and Public Health: A Case Study in Health Communication and Public Trust analyzes Merck's Gardasil vaccination campaign, exploring how corporate interests shape public health messaging. Crosswell's recent publications reveal consistent focus on contemporary health communication challenges, particularly surrounding the COVID-19 pandemic, vaccine hesitancy, and environmental health risks. Her work spans multiple disciplines including communication, public health, psychology, and medical education, often employing innovative methodologies like virtual reality simulations and biometric measurement. Recipient of Organizational Behavior Management (OBM) Innovative Research Award, 2023 Co-author of scholarly book on pharmaceutical marketing and public health trust Regular contributor to journals including Critical Public Health , Public Health Perspectives , and Journal of Environmental Studies and Sciences Crosswell has received recognition for her innovative research approaches that bridge communication theory with practical health applications. Her work examining virtual reality applications in healthcare education demonstrates her commitment to developing new methodologies for training healthcare professionals. With numerous forthcoming publications for 2025, she remains an active contributor to the field of health communication research.
Aleksandra Sarcevic is a Professor of Information Science at Drexel University's College of Computing & Informatics (CCI), where she directs the Interactive Systems for Healthcare (IS4H) Research Lab. She earned her PhD (2009) and MLIS (2005) from Rutgers University's School of Communication and Information, and holds a BA in Film and TV Production from the University of Arts, Belgrade. PhD, Communication, Information and Library Studies MLIS, Library and Information Science BA, Film and TV Production Her research focuses on computer-supported cooperative work (CSCW) , human-computer interaction (HCI) , and healthcare informatics , with specializations in: Collaboration in high-risk environments Medical team coordination Context-aware systems for critical care Crisis informatics Socio-technical system design Recent publications examine AI-enabled decision support , PPE compliance monitoring , and real-time clinical alert systems , with funding from NIH , NSF , and AHRQ . She received the NSF CAREER award in 2013 and mentors both current and graduated PhD students in interdisciplinary research. The IS4H lab develops interactive healthcare systems for trauma resuscitation and infection control, employing ethnographic methods and sensor-based activity recognition to improve medical team performance.
Joshua Marshall is an Associate Professor in the Department of Electrical and Computer Engineering at Queen's University, cross-appointed to Mechanical and Materials Engineering. He serves as Interim Director of Ingenuity Labs Research Institute and leads the Offroad Robotics research group (formerly Mining Systems Laboratory). Previously, he held positions at Carleton University and MDA, Inc. Education: PhD in Electrical and Computer Engineering, University of Toronto (specializing in systems control) Research Interests: Dr. Marshall specializes in field robotics for mining, space, and industrial applications. His work integrates control systems engineering , localization and mapping , and mechatronics to develop autonomous solutions for harsh environments. Key focus areas include mobile robot navigation, sensor fusion, and real-world deployment challenges in underground and extraterrestrial settings. Publication Trends: His 2022-2025 publications reveal dominant themes in autonomous industrial vehicle control (motor graders, excavators), marine robotics (uncrewed surface vessels), and terrain/material classification . Work consistently combines model predictive control with machine learning (Gaussian processes, reinforcement learning) for dynamic environment adaptation. Multi-robot systems and simulation-to-real transfer represent emerging research directions. Professional Recognition: Senior Member of IEEE Associate Editor, IEEE Control Systems Society Conference Editorial Board Senior Editor, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Research Impact: Dr. Marshall's Offroad Robotics group develops technologies featured in the Canada Science and Technology Museum's 'From Earth to Us' exhibit. His work bridges academic research with industry applications through collaborations with MDA and international institutions like Örebro University, though specific grant details are not provided in source materials. Leadership: He directs both the Offroad Robotics research group and Ingenuity Labs Research Institute, fostering interdisciplinary innovation in robotics, autonomous systems, and intelligent technologies for real-world implementation in mining, space exploration, and environmental monitoring.
Andrea Cristofaro is an Associate Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome. He holds an M.Sc. in Mathematics (2005) from Sapienza University of Rome and a Ph.D. in Information Science and Complex Systems (2010) from the University of Camerino. His academic trajectory includes postdoctoral positions at INRIA Rhône-Alpes (France) and the Norwegian University of Science and Technology, followed by faculty appointments at the University of Camerino and University of Oslo before joining Sapienza in 2019. His research spans: Robotic systems : Fault-tolerant control, multi-agent coordination, and hybrid dynamics Distributed parameter systems : Stability analysis, observer design, and optimal control of PDEs Hybrid systems : Control synthesis for systems with state-driven jumps and switching logic Recent publications (2024-2025) predominantly explore fault-tolerant control architectures for multi-robot systems, decentralized task allocation, adaptive observer designs for biological and thermal systems, and optimal control of reaction-diffusion equations. This reflects a consistent focus on robustness in complex dynamical systems with applications ranging from industrial robotics to biomedical engineering. He serves as Associate Editor for Automatica and IEEE Transactions on Control Systems Technology , and is a member of the IEEE CSS Conference Editorial Board.
Ludovic Saint-Bauzel is a lecturer at Sorbonne University and head of the IRIS team at the Institute of Intelligent Systems and Robotics (ISIR). His work focuses on improving physical human-robot interaction for individuals with autonomy loss through disability or aging. Specializes in computational models of disabilities Develops user intent detection through sensor fusion Active in IFRH and Fedrha federations IEEE and True Life Lab member Research interests His research centers on Human-robot physical interaction with applications in Elderly care , Smart-walker development, and Walking exoskeleton systems. Key methodologies include: Sensor fusion (depth cameras, force sensors, IMUs) Adaptive robot control systems Pathological movement modeling Motor intent prediction algorithms Article trends show consistent focus on haptic communication (6/15), assistive robotics (9/15), and sensorimotor interaction (11/15) across 2013-2022 publications. Laboratory involvement : Leads the IRIS team at ISIR, part of Fedrha (Federation for Research on Disability and Autonomy) with 50+ research teams.
Prof. Mehdi Dastani is a Professor and chair of the Intelligent Systems group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. He leads the Master's program in Artificial Intelligence and focuses on formal and computational models in AI, particularly multi-agent systems. His research integrates insights from philosophy, psychology, and law to develop autonomous agents that reason about social and cognitive concepts like norms, emotions, and responsibility. Dastani has held academic roles at Utrecht University since 2001, including postdoctoral research and faculty positions. Education: M.Sc. Computer Science (University of Amsterdam, 1991), M.Sc. Philosophy (University of Amsterdam, 1992), Ph.D. in Humanities (University of Amsterdam, 1998). His work spans theoretical and applied projects, including grants for initiatives like Golden Agents (simulating Golden Age creative industries) and traffic control systems using virtual organizations. He is actively involved in academic committees, editorial boards, and organizing international conferences like AAMAS and PRIMA. Research Interests: Multi-Agent Programming, Normative Systems, Autonomous Agents, Cognitive Robotics, and Human-Centered AI. His projects address challenges like norm enforcement, decision-making in complex systems, and ethical AI integration with societal needs. Advising & Grants: Supervised numerous PhD students (e.g., Birna van Riemsdijk, Bas Testerink) and secured grants for projects such as 'Controllable AI: Human-Centered Approach'. His work includes collaborations on urban governance, autonomous driving, and AI tools for literacy support in children. Labs & Teams: Leads the Intelligent Systems group, contributing to agent-based simulations, ethical AI frameworks, and interdisciplinary collaborations with social scientists and urban planners.
CHENG Shih-Fen is an Associate Professor of Computer Science at Singapore Management University (SMU) and a Principal Research Scientist at Amazon. He holds a PhD in Industrial and Operations Engineering from the University of Michigan and a BSE in Mechanical Engineering from National Taiwan University. His research focuses on modeling and optimization of complex systems in urban computing, decision-making, and transportation, with notable contributions to taxi fleet management, ride-hailing systems, and sustainable logistics. Research interests include Artificial Intelligence , Decision Optimization , Machine Learning , and Urban Sustainability . Notable achievements include prestigious awards from CIKM, AAMAS, and INFORMS. He has advised students such as Qian Shao and Pang Jin Tan, who received SMU Presidential Doctoral Fellowships. Key contributions include the Driver Guidance System (DGS) for taxis and patented taxi demand prediction models. Publications span top venues like IJCAI, AAAI, and Transportation Science. He is a Senior Editor of Electronic Commerce Research and Applications and actively contributes to professional communities like INFORMS and AAAI.
Christopher M. Moretti is a Senior Lecturer in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. He has taught foundational courses including COS126 (Introduction to Computer Science), COS217 (Systems Programming), COS326 (Functional Programming), and COS333 (Software Engineering) since joining Princeton in 2010. He serves as academic advisor for computer science majors (classes of 2017, 2021, 2024) and freshman engineering students (classes of 2016, 2017), and holds the role of department placement officer. His educational background includes: Ph.D. in Computer Science and Engineering, University of Notre Dame (2010) M.S. in Computer Science and Engineering, University of Notre Dame (2007) B.S. in Computer Science, College of William and Mary (2004) Dr. Moretti's research centers on Computer Science Education and Distributed Computing and Storage . In education, he develops advanced K-12 professional development content and innovative teaching methodologies. His distributed systems work focuses on scalable frameworks for campus grids, cloud computing, and storage solutions like the Chirp filesystem. He directs undergraduate independent work projects across distributed computing, sports analytics, and software engineering. His publication history reveals consistent contributions to practical distributed systems infrastructure, with recent emphasis on educational applications and bioinformatics scalability. Key themes include abstraction layers for heterogeneous computing environments and pedagogical approaches for complex CS concepts. He has received significant recognition for teaching excellence: SEAS Excellence in Teaching Award (2024) SEAS Excellence in Teaching Award (2023) Dr. Moretti actively mentors undergraduate researchers through independent work projects, though specific student names are not documented. His grant activities likely support distributed systems research and educational initiatives, though explicit funding details are absent from the source material. He maintains strong connections to the Cooperative Computing Lab from his Notre Dame doctoral work under Professor Doug Thain. Outside academia, he participates in Princeton sports culture (particularly hockey), enjoys tennis and trivia competitions, and travels with a focus on zoological institutions. A Notre Dame athletics enthusiast, he previously contributed to sports statistics and media relations during graduate studies.
Jonas Sjöberg is a Full Professor of Mechatronics at Chalmers University of Technology, where he leads the Mechatronic research group in the College of Engineering. His research spans multiple aspects of mechatronic systems with a strong focus on automotive applications. Sjöberg holds leadership roles in numerous research projects related to autonomous vehicles, vehicle control systems, and transportation safety. His research interests encompass a broad spectrum of mechatronics applications, with particular emphasis on model-based methods, signal processing, control systems, system identification, and optimization for design and product development of mechatronic systems. Sjöberg's work bridges theoretical control engineering with practical automotive applications, especially in the domains of Automotive Active Safety and Hybrid Electric Vehicles. Analysis of Sjöberg's recent publications reveals a strong research trajectory focused on autonomous vehicle technologies, with particular attention to vehicle dynamics control, intersection safety, road surface condition estimation, and optimization of vehicle maneuvers. His work demonstrates a consistent approach of applying advanced control theory to solve real-world transportation challenges, with increasing emphasis on machine learning techniques integrated with traditional control systems. Sjöberg actively supervises research and education at both undergraduate and graduate levels while leading multiple research projects funded by VINNOVA, the European Commission, and other organizations. His research group collaborates extensively with both academic institutions and industry partners in the automotive sector. His laboratory work focuses on mechatronic systems development, particularly for automotive applications including autonomous bicycles, bus docking systems, and vehicle control algorithms. The research group maintains strong connections with the automotive industry, particularly in Sweden's robust vehicle technology ecosystem.
