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Daniel J. StilwellView profile

Professor

• Autonomous Underwater Vehicles (AUVs)
• Marine Robotics
• Control Systems
• +7 more

Daniel J. Stilwell is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech), and Co-Director of the Center for Marine Autonomy and Robotics. He holds affiliations including the Seale Coastal Observatory Faculty Fellow role. His research focuses on autonomous underwater vehicles (AUVs), marine robotics, control systems, and sensor networks. He earned his Ph.D. in Electrical Engineering from Johns Hopkins University (1999), M.S. from Virginia Tech (1993), and B.S. in Computer Engineering from the University of Massachusetts (1991). His notable contributions include advancements in AUV control, underwater acoustic communication, multi-agent systems, and sensor network optimization. Key projects include the "Unconventional Marine Platforms" funded by the Office of Naval Research and collaborative subsea mapping initiatives. His work bridges theoretical control systems with practical robotic applications in marine environments. Dr. Stilwell has received prestigious awards such as the NSF CAREER Award and ONR Young Investigator Program Award. His research emphasizes robust control strategies, adaptive systems, and decentralized learning algorithms. He leads efforts in experimental validation of AUV control systems and underwater sensor networks, contributing to both academic and military applications.

Bradley HayesView profile

Associate Professor

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +5 more

Bradley Hayes is an Associate Professor in the Department of Computer Science at the University of Colorado Boulder, affiliated with the College of Engineering and Applied Science. He leads the Collaborative AI and Robotics (CAIRO) Lab, focusing on creating autonomous robots that collaborate effectively with humans through advances in explainable AI, machine learning, and human-robot interaction. His prior research includes foundational work at MIT's Interactive Robotics Group and Yale's Social Robotics Lab. Research interests span Explainable AI, Learning from Demonstration, Hierarchical Reinforcement Learning, Computer Vision, Natural Language Processing, and Cognitive Science. His work emphasizes making human-robot teams more efficient and safe through innovations like emotionally expressive robotic motion, socially aware navigation, and AR-based collaboration tools. Key contributions include techniques for robust robotic exploration, generative occupancy mapping, and systems for improving human trust through predictable robot behavior. His work has been applied to teleoperation training, surgical assistance, and space exploration scenarios. Grants and partnerships support development of assistive robotic canes and AR interfaces for collaborative tasks. Lab activities emphasize translating theoretical advancements into practical systems through close collaboration between researchers, engineers, and end-users. Education efforts include developing foundational robotics curricula addressing autonomy, perception, and control systems.

Hoda EldardiryView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), and Director of the Machine Learning Laboratory. Her research focuses on artificial intelligence, machine learning, data mining, and their applications in policy, ethics, and complex systems analysis. Education: Ph.D., Computer Science, Purdue University M.S., Computer Science, Purdue University B.E., Computer and Systems Engineering, Alexandria University, Egypt Research Interests: Eldardiry’s work spans machine learning modeling (e.g., hypergraph neural networks, time-series forecasting), AI ethics and policy education, and interdisciplinary applications in transportation, healthcare, and cybersecurity. She emphasizes socially responsible AI development through curriculum design and policy frameworks. Recent Research Themes: Her 2025 publications highlight advancements in graph-based learning, policy-aware AI education modules, and novel techniques in multi-modal data analysis. Key areas include zero-shot learning, sparse control systems optimization, and collaborative machine learning frameworks. Labs & Teams: Directs the Machine Learning Laboratory at Virginia Tech, fostering innovation in ethical AI systems and data-driven decision-making.

Hao ZhangView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +6 more

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

David CastañónView profile

Professor

• Stochastic Control
• Estimation Theory
• Game Theory
• +6 more

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

Sara Merino AceitunoView profile

Professor

• Kinetic Theory
• Partial Differential Equations
• Mathematical Biology
• +5 more

Prof. Sara Merino Aceituno is a Professor at the Faculty of Mathematics, University of Vienna, leading research in kinetic theory and its applications to biology, medicine, and social sciences. She holds roles as Vice-Dean of the Faculty and Head of the Institute of Mathematics. Her work bridges mathematical models with experimental data, focusing on emergent phenomena in collective dynamics, opinion formation, and cell behavior. She teaches advanced courses on kinetic theory, biomathematics, and mathematical strategies for learning. Her contributions include modeling cell delamination, nematic alignment, and swarm dynamics through PDEs and probabilistic methods. Collaborations with experimentalists drive her interdisciplinary research. She actively engages in education, advising, and public outreach, including a video series explaining mathematical patterns in nature. Her research emphasizes understanding macroscopic patterns arising from microscopic interactions in complex systems. Education: Holds a PhD in Mathematics, with expertise in kinetic theory and applied partial differential equations. Teaching and leadership roles reflect her commitment to academic excellence and student support. Her work integrates experimental and computational models to study clonal dynamics in tissues and mechanical constraints in epithelial layers. She has authored over 20 papers on topics ranging from active matter to opinion formation networks, contributing to both theoretical advancements and practical applications in biology and social sciences. Research focuses on deriving hydrodynamic and continuum models from particle systems, analyzing stability and bifurcations in collective behavior. Grants and collaborations include the Vienna Biocenter PhD Program and experimental groups in cell biology. Her lab explores how environmental factors influence particle swarms and how mechanical forces shape cell cycles in pseudostratified epithelia.

Raymond H. CuijpersView profile

Associate Professor

• Cognitive Robotics
• Human-Robot Interaction
• Socially Assistive Robotics
• +5 more

Raymond H. Cuijpers is an Associate Professor at Eindhoven University of Technology in the Human Technology Interaction group. His research focuses on Cognitive Robotics , Human-Robot Interaction , and Artificial Intelligence for cognitive agents, with applications in healthcare robotics and aging population support. PhD in Physics of Man from Utrecht University (2000) Postdoctoral research at Erasmus MC Rotterdam and Radboud University Nijmegen Key research areas include: Developing socially intelligent robots with proper social cue interpretation Hybrid AI approaches for real-world complexity handling Visual-haptic perception integration in human motor control Service robots for COPD patient assistance (KSERA project) Rescue robotics and tele-operation applications Recent research output (2025) includes studies on: Personalization in human-robot communication Optimal lighting for elderly visual perception Human-robot bonding mechanisms Interactive sensorized platforms for homecare (GUARDIAN) Audiovisual temporal integration in virtual environments He coordinates large-scale European projects like GUARDIAN and previously KSERA, contributes to sustainable development goals through healthcare robotics, and serves on editorial boards of leading journals including International Journal of Social Robotics . His work spans both technical robotics development and human-centric interaction studies.

Marynel VázquezView profile

Assistant Professor

• Human-Robot Interaction (HRI)
• Artificial Intelligence
• Robotics
• +3 more

Marynel Vázquez is an Assistant Professor in Yale University's Computer Science Department, leading the Yale Interactive Machines Group (IMG). Her research focuses on Human-Robot Interaction (HRI), particularly in multi-party settings, advancing perception and decision-making algorithms for socially aware robots. She holds a PhD from Carnegie Mellon University and previously worked at Stanford and Disney Research. Her research combines computer science, behavioral science, and design, emphasizing interdisciplinary approaches. Key projects include the social robots Chester and Shutter, and frameworks like SEAN-VR for evaluating robot navigation in virtual reality. Vázquez has received prestigious awards including the NSF CAREER Award (2022) and AFOSR YIP Award (2024). Teaching includes courses on interactive machines, robotics, and human-computer interaction. She actively mentors students and emphasizes ethical and socially responsible robotics development.

Damian Nale DailisanView profile

Lecturer

• Computational Social Science
• Machine Learning
• Multi-Agent Systems
• +3 more

Dr. Damian Nale Dailisan is a Lecturer in the Department of Humanities, Social and Political Sciences at ETH Zürich, affiliated with the Computational Social Science group. He holds a Ph.D. in Physics from the University of the Philippines, specializing in traffic modeling and machine learning applications. His research focuses on multi-agent systems, particularly in transportation and urban systems. He has held postdoctoral roles and contributed to projects like the ACCeSs@AIM lab. His work bridges computational methods with real-world challenges, including traffic control optimization, AI-driven decision-making, and smart city infrastructure. Notable projects include FAIRLANE for priority lane management and studies on democratizing traffic control systems. Dailisan’s publications span journals like Transportation Research Part C and IEEE Access, addressing topics such as reinforcement learning in traffic signals and ethical AI frameworks. He has presented at workshops like 'Back to the Future' at ETH Zurich and collaborates with interdisciplinary teams to enhance urban mobility solutions. His technical expertise includes Python, network analysis, and agent-based modeling, with contributions to open-source tools for earthquake networks and social systems analysis.

Ke ChenView profile

Senior Lecturer

• Machine Learning
• Artificial Intelligence
• Computational Cognitive Science
• +9 more

Dr. Ke Chen is a Senior Lecturer in the School of Computer Science at The University of Manchester, leading the Machine Learning and Perception (MLP@UoM) Lab. His research focuses on machine learning, deep learning, reinforcement learning, and their applications in intelligent systems, computer vision, and audio/speech processing. He has supervised over 50 PhD students and holds editorial roles in journals like Neural Networks and IEEE Transactions on Neural Networks . Dr. Chen has received awards such as the NSFC Distinguished Principal Young Investigator Award (2001) and JSPS Research Award (1993). Education: PhD in Computer Science (1990), with academic positions at institutions including Peking University, The Ohio State University, and Kyushu Institute of Technology. His professional activities include roles in IEEE Computational Intelligence Society committees and external examiner roles at universities like Essex. Research interests span computational cognitive systems, biometric authentication, and video game AI. Key contributions include advancements in deep architectures, reinforcement learning, and speaker-specific feature extraction. His lab, MLP@UoM, explores topics like explainable AI and transfer learning. Recent publications (2024) include work on goal-conditioned reinforcement learning and bias-resilient algorithms. He is actively involved in international conferences, serving as a keynote speaker and program committee member.

Stephen Lee-UrbanView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Game AI
• +3 more

Stephen Lee-Urban is a Teaching Associate Professor in the Department of Computer Science & Engineering at Lehigh University, affiliated with the Rossin College of Engineering. He holds a Ph.D., M.S., and B.S. in Computer Science and Engineering from Lehigh University, all completed with summa cum laude distinction. His research focuses on fundamental and applied artificial intelligence, machine learning, game AI, cognitive systems, and automated planning. He has contributed to innovative projects such as HuManIC (human-machine interpretive control), CORA (cognitive systems framework), and crowdsourced narrative generation systems. His academic career includes significant work in cybersecurity through intelligent agent modeling of malware, as well as contributions to game AI for strategy games and military training simulations. Notable awards include summa cum laude honors for all three of his university degrees. Lee-Urban's scholarly output spans over 20 publications since 2000, with recent emphasis on AI applications in collaborative storytelling, adaptive planning systems, and human-computer interaction. His research integrates machine learning techniques with sociocultural analysis, crowd-powered content creation, and hierarchical task networks. Current work explores autonomous systems capable of leveraging crowd intelligence for generating interactive narratives and optimizing military training scenarios. While no specific grants or advising roles are listed, his interdisciplinary approach bridges computer science with game design, cybersecurity, and cognitive modeling.

Elena PanteleyView profile

Researcher

• Nonlinear Control Systems
• Networked Systems
• Multi-Agent Systems
• +5 more

Elena Panteley is a Research Director at the CNRS and a member of the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. She holds a PhD in Applied Mathematics from Saint Petersburg State University (1997) and has extensive experience in research, including roles at the Institute of Problems in Mechanical Engineering, Russian Academy of Sciences (1986–1998). She co-chairs the International Graduate School of Control at the European Embedded Control Institute (EECI-IGSC) and serves as a Book-reviews Editor for Automatica and Associate Editor for IEEE Control Systems Letters . Education: PhD in Applied Mathematics, Saint Petersburg State University, 1997 MSc/BSc in relevant fields (implied by career progression) Research Interests: Focuses on stability and control of nonlinear dynamical systems, networked systems, multi-agent systems, and their applications. Her work emphasizes robust control, consensus algorithms, synchronization, and distributed control strategies for autonomous systems. She explores theoretical frameworks like Lyapunov methods and adaptive control, with practical applications in robotics and communication networks. Publications: Over 100 peer-reviewed articles in top journals like IEEE Transactions on Automatic Control and Automatica . Recent trends include advancements in consensus algorithms for multi-agent systems, synchronization of nonlinear oscillators, and control under communication delays. Awards: No specific awards mentioned, but contributions to control theory and networked systems are widely recognized. Grants/Advising: Leads research on adaptive control, network synchronization, and robotics. Collaborates internationally and advises on projects involving autonomous vehicles and distributed systems. Labs/Teams: Active in L2S and affiliated with CNRS, contributing to teams like MODESTY, COMEDY, and SYCOMORE. Engaged in transverse research on energy, industry automation, and healthy systems.

Wenqi ShiView profile

Assistant Professor

• Artificial Intelligence in Healthcare
• Precision Medicine
• Translational Medicine
• +3 more

Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.

• Cybersecurity
• Machine Learning
• Data Analytics
• +5 more

Dr Zhiyuan (Thomas) Tan is an Associate Professor at Edinburgh Napier University’s School of Computing, Engineering and the Built Environment . He is internationally recognised for his cybersecurity research and has been listed among Stanford University’s Top 2% Scientists for 2021–2023. Education BEng (2005) with high distinction – North-eastern University, China MEng (2008) – Beijing University of Technology, China PhD in Computer Systems (2014) – University of Technology Sydney, Australia Research Interests Dr Tan’s research integrates cybersecurity with machine learning and data analytics. His core areas include: Intrusion detection and defence of critical service systems Adversarial machine learning for malware and anomaly detection Virtualisation security through non-parametric behaviour modelling IoT and vehicular network security—cloud/edge/cloudlet frameworks Privacy-preserving AI and federated machine unlearning Smart-city digital forensics and cyber-physical system resilience Research Output Trends His recent articles (2020–2025) reveal a strong focus on federated learning, edge & mobile computing, UAV coordination, and AI-driven security. Topics span advanced steganography, metamorphic malware, graph injection attacks, and trustworthiness in vehicular platoons, all anchored in real-world IoT and transportation applications. Awards & Distinctions Stanford University Top 2% Scientists List (2021, 2022, 2023) National Research Award 2017 – Research Council of the Sultanate of Oman Best Paper Awards (three instances) Kaspersky Lab Student Cyber Security Conference – Finalist Award SICSA Supervisor of the Year 2019 – Honourable Mention Grants & Leadership Dr Tan has attracted over £200k in external funding including: Carnegie Trust (£73,564) – Federated Machine Unlearning ENU Development Trust (£29,998) – Machine Unlearning Royal Society (£12,000) – VANET Security & Privacy SICSA & other awards for MemoryCrypt, AI Secrets, behaviour biometrics, and visiting-fellow schemes. Supervision & Mentoring Since 2013 he has supervised or co-supervised 16 doctoral candidates and several master’s students; six PhDs have successfully graduated. Roles range from Director of Studies to additional supervisor across diverse topics from malware evolution to VR olfactory interfaces. Research Groups & Collaboration He is affiliated with the Centre for Artificial Intelligence and Robotics , the Centre for Distributed Computing, Networking and Security , and the Centre for Cybersecurity, IoT and Cyber-physical Systems at ENU, fostering interdisciplinary collaboration with national and international partners.

Jacob WhitehillView profile

Associate Professor

• Applied Machine Learning
• Speech and Speaker Recognition
• Educational Data Mining
• +3 more

Jacob Whitehill is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), affiliated with the Learning Science & Technologies (LST) program. His research focuses on applying machine learning to education and human-computer interaction, including speech recognition, emotion analysis, and automated classroom observation. He leads the NSF-funded project Developing New Scientific Instruments for Classroom Observation and collaborates on the AI Institute for Student-AI Teaming (iSAT) . His research interests span Applied Machine Learning (e.g., multi-modal systems, speaker diarization), AI for Education (e.g., automated instructional evaluation, child speech recognition), and Emotion Recognition (e.g., affective computing in classrooms). Recent work emphasizes classroom observation tools and improving student-teacher interaction analysis through video and audio data. Notable projects include: NSF-funded classroom observation tools AI Institute for Student-AI Teaming (iSAT) Schmidt Futures-funded Hybrid Human-Agent Tutoring for math education His team includes PhD students Xinlu He (Data Science), Jiani Wang (Computer Science), and Yiwen Guan (Computer Science), along with visiting scholar Cecilia Tivir. He advises students on topics like speaker recognition, educational data mining, and computer vision in classroom settings. Grants and collaborations include NSF, Schmidt Futures, and industry partnerships. His lab develops tools for automated feedback on teaching practices, leveraging LLMs and multimodal data. For more details, contact jrwhitehill@wpi.edu .