Professor Ingrid Bouwer Utne is a faculty member at the Department of Marine Engineering, Norwegian University of Science and Technology (NTNU). Her primary research focuses on risk analyses of ships, marine systems, and autonomy, with emphasis on operational safety, maintenance management, and risk control in autonomous maritime technologies. Key Projects: Leader of the Risk Group at NTNU, involved in the SFI Autoship initiative and ERC AdG BREACH project addressing risk-based rationality in autonomous systems. Research Interests: Autonomous systems design, probabilistic risk assessment, safety engineering, and risk-informed decision-making for marine operations. Grants & Funding: Secured funding from the Research Council of Norway, MAROFF, and industry partners for projects like ORCAS (Online Risk Management for Autonomous Ships) and UNLOCK (Supervisory Risk Control). She supervises numerous PhD students and postdocs, focusing on topics such as autonomous vessel navigation, risk modeling for underwater robotics, and decarbonization of maritime systems. Her work bridges theoretical risk analysis with practical applications in marine autonomy and safety systems.
Tim Baarslag is a Senior Researcher and group leader of the Intelligent and Autonomous Systems group at CWI (The Dutch research institute for Mathematics and Computer Science). He holds the title of Professor of Mathematics of Cooperative AI at Eindhoven University of Technology and serves as a Visiting Associate Professor at Nagoya University of Technology, Visiting Fellow at the University of Southampton, and Visiting Scholar at MIT. His research focuses on automated negotiation systems for collaborative decision-making in smart energy trading, IoT, autonomous vehicles, and digital privacy. Education : MSc (cum laude) and BSc (cum laude) from Utrecht University; PhD (cum laude) from Delft University of Technology Tim pioneered the COMBINE project (NWO Vidi grant) for coordinating multi-deal negotiations and developed the widely-used Genius negotiation environment. His work appears in prestigious venues like Science Magazine , Artificial Intelligence , and MIT Technology Review . He also leads the International Automated Negotiating Agent Competition and contributes to policy through memberships in The Young Academy and Netherlands Academy of Engineering . Recent research trends emphasize multi-deal negotiation protocols (2024), preference uncertainty modeling in privacy negotiations (2022), and scalable algorithms for handling outcome spaces as large as 10²⁵⁰ possibilities. His 2023 work on search algorithms for large negotiation domains has applications in energy trading and supply chain management. Scientific Awards : Cor Baayen Young Researcher Award (2017), Springer Theses Award (2016), multiple Best Paper Awards (AAMAS 2022, WI-IAT 2015, IJCAI 2014), and recognitions as Science Talent (2018), Academic Pioneer (2020), and Young Talent (2019) As a grant recipient , Tim leads NWO Vidi project COMBINE and previously held a Veni grant for preference uncertainty research. He mentors through organizing competitions, serving on conference PCs (AAAI, IJCAI), and reviewing in top journals like Artificial Intelligence . His work bridges theory and practice through the Genius framework and real-world implementations in smart grid and vehicular platooning.
Dr Andrew Coles is a Reader in Artificial Intelligence at the Department of Informatics, King's College London, within the Faculty of Natural, Mathematical & Engineering Sciences. His research focuses on temporal and numeric planning, explainable planning, and human-robot collaboration. He leads and co-investigates multiple research projects funded by EPSRC, the Royal Academy of Engineering, and the European Commission. His research interests include Artificial Intelligence, Temporal and Numeric Planning, Planning with Rich Domain Models, Explainable Planning, Human-Robot Interaction, Autonomous Systems, Heuristic Search, and Decision-Making. He has published extensively in top-tier AI and robotics conferences such as ICAPS, IROS, HRI, AAAI, and IJCAI. His recent publications demonstrate a strong trend toward explainable AI in human-robot collaboration, with a focus on multimodal sensing (e.g., eye tracking), user needs for explanation, and adaptive planning. His work integrates planning algorithms with human-centered evaluation and real-world applications in robotics. Scientific Awards: International award for PhD thesis on assistive robots (2020) Advising and Grants: Dr Coles has supervised multiple students, including Lara Wachowiak, Guillem Canal, and Petra Tisnikar. He has led or co-investigated several major research projects, including: COHERENT (EPSRC): Collaborative Hierarchical Robotic Explanations Plan and Goal Reasoning for Explainable Autonomous Robots (Royal Academy of Engineering) ADE (European Commission): Autonomous Decision Making in Very Long Traverses ERGO (European Commission): European Robotic Goal-Oriented autonomous controller Labs and Teams: He is affiliated with the Reasoning and Planning research group and the Trusted Autonomous Systems Hub at King's College London, focusing on developing trustable autonomous systems through robust planning and human-centered AI.
Eshed Ohn-Bar is an Assistant Professor in the Department of Electrical & Computer Engineering at Boston University. He leads the Human-to-Everything (H2X) Lab, focused on developing intelligent systems for assistive and autonomous technologies. His research bridges machine perception, learning, and human-computer interaction, with applications in autonomous driving and accessibility for visually impaired individuals. Educated at UCLA (BS in Mathematics, 2010; MEd, 2011) and UCSD (PhD in Electrical Engineering, 2017), he holds a Humboldt Fellowship and has received the IEEE ITS Society Best PhD Dissertation Award (2017) and the 2025 BU Early Career Excellence in Research Award. His work emphasizes robust autonomy, real-time assistance, and inclusive design, collaborating with industry partners like Motional and receiving NSF grants (e.g., IIS-2152077). Research interests include autonomous systems, computer vision, and assistive technologies. Recent trends in publications highlight advancements in decision-making frameworks, neural volumetric models, and scalable learning for navigation. His lab’s projects address challenges in accessibility, such as blind motion generation and inclusive autonomous vehicle design. Awards: Humboldt Fellowship, IEEE ITS Best Dissertation, BU Early Career Award Grants: NSF IIS-2152077 Labs/Teams: H2X Lab, collaborating on projects with industry and academic partners
Christoforos Mavrogiannis is an Assistant Professor of Robotics at the University of Michigan, leading the Fluent Robotics Lab within the Department of Robotics. He holds a Ph.D. and M.S. from Cornell University and a Diploma in Mechanical Engineering from the National Technical University of Athens. His research focuses on enabling robots to seamlessly integrate into dynamic, unstructured environments through advancements in human-robot interaction, shared autonomy, multiagent systems, and navigation algorithms. Education: Ph.D. and M.S., Cornell University Diploma in Mechanical Engineering, National Technical University of Athens Research Interests: His work spans human-robot interaction , shared autonomy , and multiagent systems . He develops algorithms for navigating dynamic environments , decentralized control , and behavior prediction , with applications in robotic manipulation and collaborative transport . Recent projects include the HOUND off-road robot and pixel-art generation with mobile robots. Awards: Best paper award at the RSS Social Navigation Workshop (2024) Grants & Advising: He advises the Fluent Robotics Lab and contributes to organizing conferences like ICRA and HRI. His lab focuses on human-centered robotics and socially competent navigation . Labs & Teams: Fluent Robotics Lab at the University of Michigan, collaborating on projects like the HOUND platform and electrostatic brake systems for manipulation.
Mehrdad Nojoumian is an Associate Professor at Florida Atlantic University's College of Engineering and Computer Science, specializing in Security, Privacy, Trust, and Human-Autonomy Interaction. His research bridges computer science with FinTech and autonomous systems, focusing on cryptographic protocols, blockchain, and IoT security. PhD, University of Waterloo (Computer Science, 2012) MSc, University of Ottawa (Computer Science, 2007) BSc, Islamic Azad University (Computer Engineering, 2002) His research explores privacy-preserving mechanisms in autonomous systems, trust modeling in blockchain, and cross-disciplinary applications of cryptography. Recent work includes patents on adaptive driving modes and ongoing studies on IoT vulnerabilities and autonomous coordination. Notable awards include the Excellence and Innovation in Undergraduate Teaching Award (2023), NAI Induction (2022), and multiple Best Paper Awards . His publications span journals like Information and Computation and conferences such as GameSec and IEEE Blockchain . He mentors graduate students in cybersecurity bootcamps, high school outreach programs, and collaborative research projects funded by NSF , ARO-AFOSR , and AFRL . His lab emphasizes diversity and inclusion, particularly supporting underrepresented groups in STEM.
Xiaofan Yu is an Assistant Professor in the Department of Electrical Engineering at the University of California, Merced. He holds a Ph.D. (2025), M.S. (2020), and B.S. (2018) from the University of California, San Diego and Peking University, respectively. His research focuses on embedded systems , edge AI , and neuromorphic computing , with applications in IoT, federated learning, and hyperdimensional computing. ML&Systems Rising Star (2024) CPS Rising Star (2023) EECS Rising Star (2022) His work addresses on-device AI for real-world IoT deployments, reliability-driven sensor networks , and next-generation edge intelligence . Recent publications highlight advancements in federated learning (TIOT 2025), multimodal sensor interaction (IMWUT 2025), and noise-resilient sensor systems (Sensors 2024). Key subfields include hyperdimensional computing , asynchronous distributed training , and resource-efficient edge models . Dr. Yu actively mentors students across institutions and programs, including the Early Research Scholarship Program (UCSD) and ENLACE Summer Research Program. He has advised projects on smart elderly monitoring , LLM-based sensor reasoning , and hyperdimensional algorithm optimization . Collaborations span UCSD, TUM, and Stanford, with industry partnerships in IoT design automation (RelIoT simulator) and biomedical applications (bladder fullness restoration system).
Anqi Liu is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She maintains significant affiliations with the Johns Hopkins Mathematical Institute for Data Science (MINDS) and the Johns Hopkins Institute for Assured Autonomy (IAA), while also collaborating extensively with the Center for Language and Speech Processing (CLSP) and the Laboratory for Computational Sensing and Robotics (LCSR). Her research focuses on developing principled machine learning algorithms for building reliable, trustworthy, and human-compatible AI systems in real-world applications. Key research areas include: Distributionally robust learning under covariate shift Uncertainty quantification for AI safety and fairness Safe exploration in control systems Fair machine learning under distribution shift Active learning under label shift Dr. Liu's work addresses critical challenges in high-stakes AI applications where reliability, safety, and societal impact are paramount. Her methods ensure AI systems remain robust to changing data environments, provide accurate uncertainty estimates, and incorporate human preferences in interactions. Analysis of her recent publications reveals a strong trajectory in trustworthy AI research with significant contributions to distribution shift handling, uncertainty quantification techniques, and safe decision-making frameworks. Her work bridges theoretical foundations with practical applications across healthcare, robotics, and social media analysis. Amazon Research Award Dr. Liu actively mentors eight PhD students and teaches specialized courses on Machine Learning for Trustworthy AI and standard Machine Learning at Johns Hopkins University, preparing the next generation of researchers to address critical challenges in AI safety and reliability.
Professor Brian C. Williams is a leading academic at the Massachusetts Institute of Technology (MIT) , holding the position of Professor of Aeronautics and Astronautics and directing the Autonomous Systems Laboratory (ASL) . He is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Space Systems Laboratory (SSL) . His work focuses on advancing model-based autonomy, enabling robots to operate independently in extreme environments such as space, underwater, and urban traffic. His contact details include email williams@csail.mit.edu and phone 253-2739 . Education : S.B., S.M., and Ph.D. in Computer Science and Electrical Engineering from MIT (1989). Williams' research spans risk-bounded decision making , collaborative robotics , and neural-symbolic learning . He has pioneered systems like Remote Agent, which demonstrated autonomous self-repair in NASA's Deep Space One mission, and Geordi, a risk-aware driver assistant. His work integrates symbolic reasoning, probabilistic methods, and machine learning to create resilient robotic systems for space exploration, manufacturing, and transportation. The most recent publications highlight trends in risk-aware planning , multi-agent robotics , and stochastic control . Key innovations include tube-based trajectory optimization, conflict-directed task allocation, and theory-of-mind guided interventions, reflecting his focus on robustness in uncertain environments. Scientific Awards : NASA Space Act Award (1999), AAAI Fellow, and multiple best paper awards from AAAI, IJCAI, ICAPS, CDC, HRI, and ECAI. Williams leads the Model-Based Embedded and Robotics Systems Group at CSAIL, collaborating with institutions like NASA's Jet Propulsion Laboratory. His projects include ASIST (Artificial Social Intelligence for Teams), RADMAX (Risk and Deadline Aware Planning), and Uhura (risk-aware personal assistants), addressing challenges in health care, manufacturing, and defense applications.
Mahadev Satyanarayanan is the Jaime Carbonell University Professor of Computer Science at Carnegie Mellon University. His multi-decade research focuses on performance, scalability, availability, and trust in distributed systems spanning cloud to mobile edge computing. He pioneered foundational concepts in mobile computing and Edge Computing through his seminal work on VM-based cloudlets. His current research explores cloudlet-based Edge Computing for latency-sensitive applications, wearable cognitive assistance systems integrating augmented reality, and edge-based machine learning frameworks for efficient training data discovery. He collaborates with Dan Siewiorek, Martial Hebert, and Bobby Klatzky on transformative applications. Dr. Satyanarayanan received his PhD from Carnegie Mellon University after completing Bachelor's and Master's degrees at the Indian Institute of Technology, Madras. His honors include ACM and IEEE Fellowships recognizing his contributions to distributed systems and mobile computing. ACM Fellow IEEE Fellow
Anna Maria Rapetti is an Associate Professor of Medieval History at Ca' Foscari University of Venice's Department of Humanities. She holds a PhD from the Catholic University of the Sacred Heart (Milan, 1996) and has been affiliated with Ca' Foscari since 2002. Her research focuses on medieval monasticism, ecclesiastical institutions, and rural history in Northern Italy. She is co-director of the Studi di storia book series and a member of several research centers including the Inter-University Centre for Early Medieval History and Archaeology (SAAME). Educations: PhD in Medieval History (1996), Postdoctoral studies at Universities of Genoa and Milan Research Interests: Monastic dynamics, church-state relations, agricultural history, and Venetian territorial expansion Teaching: Medieval History courses across multiple degree programs, including 'History of the Medieval Church' and 'Environmental History' Grants: Principal Investigator for EU-funded projects (e.g., 'Tarvisium GIS'), PRIN 2020 national research initiatives Labs/Teams: Director of AGeS Research Centre, member of the Centre for Historical Studies on Christianity Her work bridges institutional analysis with environmental and social history, emphasizing monastic communities' roles in shaping medieval landscapes and power structures.
Steven Ceron is an Assistant Professor in Robotics at the University of Michigan's College of Engineering. His research focuses on swarm robotics, multi-agent systems, and programmable self-organization of micro- and macro-scale robot swarms. He leads the Synergetic Adaptive Machinas (SAM) Lab, which develops reconfigurable robot swarms for biomedical applications and smart materials integration. Key research areas include microrobot fabrication, heterogeneous swarm coordination, and self-reconfigurable modular systems. His work envisions seamless integration of robot swarms into daily life through innovations in design, control, and scalability. Recent publications emphasize swarmalator dynamics, strain-based coordination in soft robots, and scalable fabrication methods. His lab explores both theoretical frameworks and practical implementations, bridging micro-scale and macro-scale robotics applications. Though no awards were explicitly listed, his contributions to novel fabrication techniques and modular robotics suggest ongoing recognition in the field. Advising and grant details are not provided here, but his lab's focus on biomedical and aerospace applications indicates active collaborative projects.
Christian List is Professor of Philosophy and Decision Theory at Ludwig Maximilian University of Munich, where he serves as Co-Director of the Munich Center for Mathematical Philosophy (MCMP). Previously, he was Professor of Philosophy and Political Science at the London School of Economics until 2020. His work bridges philosophy, economics, and political science with a particular focus on individual and collective decision-making and the nature of intentional agency. Professor List's research spans multiple interconnected domains: theories of individual and collective choice (particularly social choice theory and judgment aggregation), free will and consciousness, the philosophy of mind and action, and the foundations of the social sciences. His work on group agency, developed in his influential book Group Agency with Philip Pettit, has reshaped debates about corporate entities and collective intentionality. His more recent work on free will, culminating in his book Why Free Will is Real , presents a scientifically grounded defense of free will against reductionist skepticism. His recent publications reveal a sophisticated integration of formal methods with deep philosophical questions, particularly regarding consciousness, probability aggregation, and the relationship between different levels of explanation. List's work consistently demonstrates how mathematical precision can illuminate fundamental philosophical problems while maintaining relevance to broader social and scientific contexts. Scientific Awards and Recognition: Elected Fellow of the British Academy (2014) Member of Academia Europaea (2023) Member of the Bavarian Academy of Sciences and Humanities (2022) Joseph B. Gittler Award from the American Philosophical Association (2020) Philip Leverhulme Prize in Philosophy (2007) 5th Social Choice and Welfare Prize (2010) List has supervised numerous PhD students and early-career researchers, many of whom have gone on to prominent positions in philosophy and related fields. His collaborative work with Franz Dietrich on judgment aggregation has been particularly influential. As Co-Director of the Munich Center for Mathematical Philosophy, he has secured substantial research funding and established MCMP as a leading international hub for formal and mathematical approaches to philosophical problems. Through the Munich Center for Mathematical Philosophy, List leads a vibrant research community that brings together philosophers, economists, political scientists, and mathematicians to tackle foundational questions using rigorous formal methods. The center hosts regular workshops, seminars, and visiting scholars, creating a dynamic intellectual environment that bridges disciplinary boundaries.
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au
