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Tuan DamView profile

Assistant Professor

• Reinforcement Learning
• Sequential Decision Making
• Monte Carlo Tree Search
• +4 more

Tuan Dam is an Assistant Professor at the School of Information and Communication Technology (SoICT), Hanoi University of Science and Technology (HUST), focusing on the theory of Reinforcement Learning. Previously, he held a postdoctoral position at INRIA Lille, France, and completed his Ph.D. in Robotics at TU Darmstadt, Germany. His research emphasizes developing principled methods for robots in unstructured environments, with notable contributions to Monte Carlo Tree Search (MCTS) and POMDP applications. Education: Ph.D. in Robotics, TU Darmstadt (Germany), 2024 Master's in Electronics and Computer Engineering, Hanyang University (South Korea) Bachelor's in Computer Science, Vietnam Research Interests: Reinforcement Learning under uncertainty, Monte Carlo Tree Search (MCTS), POMDPs, robotics applications, and theoretical foundations of sequential decision-making. His recent work includes integrating POMDP frameworks into MCTS for robot planning tasks like Disentangling and Mikado Problems. Advising & Supervision: Co-supervised MS theses on topics like memory representations in partially observable RL and Laplacian representations for continuous MCTS Guided multiple integrated projects on MCTS benchmarking and policy search techniques Labs & Collaborations: Former affiliations include ESOS Lab (Korea), HMI Lab (Vietnam), DFKI Berlin (Germany), and Auburn University (USA). Current work focuses on advancing RL theory and its industrial applications.

• Robotics
• Control Systems
• Sensor Networks
• +5 more

Daniele Fontanelli is a Full Professor at the Department of Industrial Engineering , University of Trento, Italy. He holds a Ph.D. in "Automatics, Robotics, and Bioengineering" from the University of Pisa (2006) and a Laurea in Industrial Informatics (2001). He has taught courses such as Internet of Things , Distributed Measurement Systems , and Statistical Estimation at both Trento and Pisa. Research Interests: Robotics, human-robot interaction, signal processing for autonomous systems, UWB localization, distributed sensor networks, and smart grid measurement systems. His work spans mobile robotics, control theory, and embedded systems with applications in medical robotics, warehouse automation, and environmental monitoring. Scientific Contributions: Over 150 publications and leadership roles in European projects like AWARD2.0, SAFESTRIP, and ACANTO. He serves as Associate Editor in Chief for IEEE Transactions on Instrumentation and Measurement. Outstanding Associate Editor (2020) Most Productive Italian Reviewer (2020) Distinguished Service Award (2020)

Bin WeiView profile

Assistant Professor

• Robotics
• Control Theory
• Dynamical Systems
• +3 more

Bin Wei is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Texas A&M University-Kingsville, College of Engineering. He holds a PhD from Ontario Tech University and specializes in robotics, dynamical systems, and control theory. His research focuses on: Robotic mechanism design and dynamic balancing Control algorithms for human-robot interaction Synchronization in nonlinear systems Advanced manufacturing systems Recent publications explore neural-network controllers, stability analysis, and sensor applications. His work integrates theoretical control frameworks with practical robotic implementations, emphasizing adaptive and cognitive approaches.

Steve HansenView profile

Professor

• Human Movement Behavior Analysis
• Motor Control
• Sensory-Motor Systems
• +3 more

Dr. Steve Hansen is a Professor in the Department of Physical and Health Education at Nipissing University, serving as Graduate Program Coordinator and Chair within the Schulich School of Education. He holds a BKin (Honours), MSc, and PhD from McMaster University. His research focuses on visual control of upper limb movements, kinematic analyses of movement in both typically developing and special populations, and theoretical modeling of movement control and rehabilitation. Key themes include optimizing speed-accuracy trade-offs, sensory-motor integration, and movement variability quantification. Research interests span human movement behavior, sensory-motor systems, and movement control in clinical populations. His work examines how visual feedback and task constraints influence movement planning and execution, with applications to neurorehabilitation and motor learning. Recent trends in his publications emphasize hormonal effects on motor performance, robotics in pediatric therapy, and peer feedback dynamics in skill acquisition. No scientific awards are explicitly listed, but his extensive publication record reflects impactful contributions to motor control theory. As an academic leader, he oversees graduate training and curricular development in physical education. While no lab affiliations are noted, his research collaborations involve interdisciplinary teams studying biomechanics, neuroscience, and clinical applications.

Gim Hee LeeView profile

Associate Professor

• Computer Vision
• Robotic Perception
• Machine Learning
• +3 more

Gim Hee Lee is an Associate Professor at the Department of Computer Science, National University of Singapore (NUS) School of Computing. He heads the Computer Vision and Robotic Perception (CVRP) Laboratory and holds affiliations with the NUS Graduate School for Integrative Sciences and Engineering (NGS) and the NUS Institute of Data Science (IDS). Previously, he was a researcher at Mitsubishi Electric Research Laboratories (MERL) in the USA and worked at DSO National Laboratories in Singapore. Education Dr.sc. in Computer Science from ETH Zurich M.Eng. in Mechanical Engineering from National University of Singapore B.Eng. in Mechanical Engineering (1st Class Honors) from National University of Singapore Research Interests Professor Lee's research focuses on Computer Vision , Robotic Perception , and Machine Learning , with specialization in dynamic 3D scene reconstruction, event camera applications, and cross-view geo-localization. His work bridges theoretical advances with real-world applications in autonomous systems and augmented reality, particularly through neural scene representations and geometric computer vision techniques. Scientific Awards Faculty Teaching Excellence Award (AY 2018/19 and AY 2017/18) CVPR 2014 Doctoral Consortium Travel Award Finalist for IROS 2012 Best Paper Award Finalist for IROS 2012 Best Video Award Advising and Service Professor Lee serves as Associate Editor for IJCV and has held Area Chair positions for major conferences including CVPR, ICCV, ECCV, and NeurIPS. He chaired Program Committees for 3DV 2022 and served as Demo Chair for CVPR 2023. His research on the Cross-View Matching Network (CVM-Net) demonstrated real-time ground-to-aerial geo-localization in moving vehicles, establishing a benchmark for subsequent research in this field. Laboratories He leads the Computer Vision and Robotic Perception (CVRP) Laboratory at NUS, which develops cutting-edge solutions for 3D scene understanding, event-based vision, and robotic navigation. The lab's work includes acquiring high-quality datasets for dynamic scene reconstruction and advancing neural representations for real-world applications.

Lin SHAOView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Decision Making & Planning
• +5 more

Lin SHAO is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on developing algorithms and systems for robotic perception and manipulation, aiming to create general-purpose robotic systems capable of performing diverse tasks in varied environments. He holds a Ph.D. and M.S. from Stanford University, and a B.S. in Geochemistry from Nanjing University. His educational background includes: Ph.D. (Stanford University, 2021) M.S. (Stanford University, 2017) B.S. in Geochemistry (Nanjing University, 2014) Key research areas include robotic perception and manipulation, reinforcement learning, differentiable physics simulation, and large-scale robotic learning systems. His work emphasizes enabling robots to autonomously perceive and interact with their environment through advanced algorithms and simulation techniques. Notable contributions include the SAM-RL framework for model-based reinforcement learning and the SAGCI-System for sample-efficient robot learning. He has received the Best System Paper Award Finalist at RSS 2023 and serves as a co-chair of the IEEE Robotics and Automation Society's Technical Committee on Robot Learning. Courses taught include CS2040S (Data Structures and Algorithms), CS4278/CS5478 (Intelligent Robots: Algorithms and Systems), and Knowledge@Computing workshops. His research group actively explores topics such as robotic grasping, motion planning, and human-robot interaction, with applications in both industrial and service robotics domains.

• Business and Management
• Marketing
• Sociology
• +3 more

Mark Kennedy is an Associate Professor in the Department of Management and Entrepreneurship at Imperial College London’s Business School, and leads the Data Science Institute (DSI). He teaches on Ph.D., Masters, and Executive courses in the Business School and DSI. His research focuses on how innovations become socially recognized categories (social ontologies), analyzing their diffusion, acceptance, and societal impact across domains like technology, business, and law. He employs quantitative text and network analysis to study emerging social realities and collective reactions. Research interests include AI’s impact on work and society, cultural/legal precedents in innovation diffusion, task-mining from job descriptions, and social network dynamics in advice-seeking. He has affiliations with the Data Science Institute, Centre for Infrastructure Robotics Ecosystems, and Artificial Intelligence Network. Publications appear in top journals such as Academy of Management Journal , American Sociological Review , and Organization Science . Current projects investigate AI evolution, opposition dynamics in diffusion, task dependencies in job roles, and social network formation for professional advice. No scientific awards are explicitly listed, but his work contributes to foundational debates in innovation sociology and organizational theory. Advising and grants details are not provided in available texts. He is part of multidisciplinary teams at the DSI and collaborates across Imperial’s research ecosystems.

Aude BillardView profile

Professor

• Machine Learning
• Robotics
• Mechatronics
• +2 more

Aude Billard is a Full Professor at the Learning Algorithms and Systems Laboratory (LASA) within the École Polytechnique Fédérale de Lausanne (EPFL) , where she leads groundbreaking research in robotics and machine learning. She also holds teaching roles in the Microengineering and Mechanical Engineering Sections and serves as a member of the Conference of Section Directors (CDS) and the STI School Direction . PhD in Artificial Intelligence (University of Edinburgh, 1998) MSc in Knowledge-Based Systems (University of Edinburgh, 1996) BSc & MSc in Physics (EPFL, 1995) Her research focuses on machine learning techniques for robotics , particularly in dexterous manipulation , dynamical systems , and human-robot interaction . She has pioneered methods for constraint learning from demonstrations, obstacle avoidance in crowded environments, and adaptive control systems, with recent works exploring AI ethics, sustainability in robotics, and improving robotic dexterity beyond human capabilities. Awarded multiple times for her contributions, including IEEE Fellow , ERC Advanced Grant , and Swiss National Science Foundation Career Award , Billard has also received recognition for teaching and service leadership in robotics societies. Scientific Leadership : IEEE Robotics and Automation Society President Education Advocacy : Innovative Teaching Grant recipient PhD Mentorship : Supervised ~30 PhD students

• Swarm Robotics
• Autonomous Systems
• Droplet Architectures
• +3 more

Dr Klaus-Peter Zauner is an Associate Professor in the Department of Electronics and Computer Science at the University of Southampton. He leads research in the Agents, Interaction and Complexity Centre and the Centre for Robotics. His work focuses on swarm robotics, autonomous systems, droplet architectures, and microfluidics. He has contributed to projects like the EU-funded NEUNEU-FP7 and a Leverhulme Trust Fellowship. Zauner advises multiple PhD students in areas such as robotics and computer science. Research Groups: Agents, Interaction and Complexity Centre; Centre for Robotics PhD Students: Nicholas Charles Hill, Toby Thomas, Yue Wang, Toby Matthew Alfie Godfrey, Yanran Xu, Lianghong Ye His research interests include bio-inspired systems, with recent work emphasizing human-swarm interaction, visual navigation in forest environments, and sparse robot swarms. His articles span topics from swarm decision-making to functional droplet architectures, showcasing interdisciplinary innovation.

Mark HoView profile

Assistant Professor

• Cognitive Science
• Social Psychology
• Computational Modeling
• +5 more

Mark Ho is an Assistant Professor of Psychology at New York University (NYU), affiliated with the Department of Psychology in the College of Arts & Science. Previously, he served as an Assistant Professor at Stevens Institute of Technology (2023–2024) and held postdoctoral roles at Princeton University and UC Berkeley. He holds a Ph.D. and M.S. in Cognitive Science and Computer Science from Brown University, alongside a B.A. in Philosophy from Princeton University. His research focuses on cognitive, motivational, and social processes underlying human problem-solving, particularly in intentional action interpretation, theory of mind, and the emergence of social phenomena from individual intentions. Using computational modeling and behavioral experiments, his work spans higher-level cognition and social psychology, including planning, pedagogy, and coordination. He also explores applications of computational cognitive science to develop human-interpretable AI systems. Research interests include: cognitive costs in decision-making, representational alignment in teaching, hierarchical planning structures, and the interplay between reinforcement learning and cognitive constraints. His lab's work bridges cognitive science and AI, emphasizing human-AI collaboration and ethical machine learning. Professional affiliations include the NYU Center for Data Science (2022–2023) and multiple academic conferences. His articles frequently address human-AI interaction, decision-making frameworks, and the theoretical underpinnings of social cognition.

Marin KobilarovView profile

Associate Professor

• Intelligent Robotics
• Autonomous Systems
• Mechanical Control Systems
• +3 more

Marin Kobilarov is an Associate Professor in the Department of Mechanical Engineering at Johns Hopkins University’s Whiting School of Engineering. He leads the Laboratory for Autonomous Systems, Control, and Optimization, a subset of the Laboratory for Computational Sensing and Robotics. His research focuses on intelligent robotic vehicles capable of autonomous navigation and task execution in complex environments, with applications in aerial vehicles, medical robotics, and space systems. Dr. Kobilarov holds a BS in Computer Science and Applied Mathematics from Trinity College (2003) and a PhD in Computer Science from the University of Southern California (2008). Before joining Johns Hopkins in 2012, he completed postdoctoral fellowships at the California Institute of Technology and the United Technologies Research Center, specializing in control systems and dynamical systems. His research interests span robotics, autonomous systems, and medical applications, including surgical robotics for ophthalmology and microsurgery. Notable projects include developing perception-aware navigation frameworks, UAV pick-and-place systems, and autonomous retinal surgery tools using imitation learning and real-time imaging. His publications emphasize trajectory optimization, multi-agent coordination, and safety-critical control, with recent work addressing challenges in occluded environment navigation and uncertainty-aware planning for heterogeneous robot teams. Awards include prestigious postdoctoral fellowships from the Keck Institute for Space Studies and United Technologies Research Center. Dr. Kobilarov’s lab collaborates across disciplines to advance robotics in dynamic environments, with ongoing work on surgical automation, autonomous aerial systems, and robust control algorithms. His research bridges theoretical control methods with practical engineering solutions for real-world robotic applications.

Yuankai QiView profile

Lecturer

• Vision-and-Language for Robot Action
• Video Captioning
• Medical Image Processing
• +8 more

Dr. Yuankai Qi is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing and an Adjunct Lecturer at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on Vision-and-Language for Robot Action, Video Captioning, Medical Image Processing, and Movie Dubbing. He holds a PhD from Harbin Institute of Technology, specializing in visual object tracking, and has won awards such as the DAVIS 2017 Challenge and CAAI Outstanding Doctoral Dissertation. He has published over 60 papers in top-tier venues like CVPR and NeurIPS, and serves as an Area Chair for NeurIPS, ICCV, and ICLR. His current projects include AI-enhanced CT image analysis and generative AI for banking solutions. Dr. Qi collaborates with the Data Horizons, Smart Green Cities, and Frontier AI Research Centres. He teaches COMP 3420 (AI for Text and Vision) and COMP 2250 (Data Communication). His work spans robotics navigation, medical imaging, and video anomaly detection, recognized globally as a Top 2% Scientist in 2024. Education: PhD (Harbin Institute of Technology, China) Key Awards: ACM Multimedia 2024 Best Paper Award, ICPR 2024 Best Student Paper Award Grants: Projects on AI in healthcare and banking (2024–2028) Labs/Teams: Data Horizons Research Centre, Smart Green Cities Research Centre

Mary Beth Watson-ManheimView profile

Professor

• Information Systems
• Virtual Work Environments
• ICT-Intensive Workplaces
• +6 more

Mary Beth Watson-Manheim is a Professor and Department Head in the Department of Managerial Studies at the University of Illinois Chicago (UIC), part of the College of Business Administration. She holds a Ph.D. in Information Technology Management from Georgia Institute of Technology and previously served at the University of Florida’s Warrington College of Business. Her research focuses on the societal and organizational implications of ICT-intensive work environments, virtual collaboration, and digital transformation. Watson-Manheim has held editorial roles including Senior Editor of the Information Systems Journal , and leadership positions such as Division Chair (2016-17) of the Academy of Management’s OCIS Division. She has conducted research in India as a Senior Research Scholar (2009-2010) and at the University of Muenster as a Visiting Scholar (2018-2019). Her research interests include virtual work environments, IT-enabled organizational change, and the impact of social media and algorithmic technologies on work dynamics. Notable honors include the 2012 AIS Best Publication Award and the 2007 Distinguished Faculty Award from UIC. Watson-Manheim’s work bridges theoretical contributions with practical insights, addressing challenges in hybrid work models, ride-hailing platform impacts, and hidden work dynamics in technology-driven settings. Her publications span journals like Management Information Systems Quarterly and Information Systems Journal , with recent articles examining AI ethics, remote work transformations, and algorithmic effects on employee relationships. She advocates for rethinking organizational assumptions in the digital age, emphasizing human-centric approaches to technological adoption.

Hsieh, Sheng-JenView profile

Professor

• Automation and Robotics
• Infrared Imaging
• Non-destructive Testing and Evaluation
• +6 more

Professor Hsieh, Sheng-Jen holds a faculty position at Texas A&M University in the Department of Engineering Technology & Industrial Distribution, with affiliated roles in Electrical & Computer Engineering. He specializes in automation, robotics, infrared imaging, and smart system design. His research bridges engineering education and advanced manufacturing technologies, emphasizing Industry 4.0 applications and cyber-physical systems. Education includes a Ph.D. in Industrial Engineering from Texas Tech University (1995), M.S. from St. Mary's University (1989), and B.S. from National Taipei Institute of Technology (1982). He has received numerous accolades including the NSF CAREER Award, Halliburton Foundation Professorship, and multiple teaching awards. His research interests span additive manufacturing optimization, thermal stress prevention in 3D printing, and remote-access lab development for education. Key achievements include developing AI-driven systems for anomaly detection in motors and smart traffic light control algorithms. He actively contributes to educational outreach through low-cost automation kits and PLC training modules. Scientific contributions include over 100 publications, with recent work focusing on machine learning applications in manufacturing processes and educational technology innovations. He has advised multiple undergraduate research projects in mechatronics and robotic systems. Labs and collaborations involve experimental setups for thermal imaging analysis, automated material handling systems, and IoT-enabled HVAC control strategies. His work integrates cognitive task analysis to improve engineering education practices.

• Machine Learning
• Approximate Inference
• Optimal Control
• +6 more

Vicenç Gómez is an Associate Professor in the Department of Engineering at Universitat Pompeu Fabra (UPF), where he leads research in artificial intelligence and machine learning. He serves as Coordinator of the Erasmus Mundus Joint Master in Artificial Intelligence and the MSc program in Intelligent and Interactive Systems, and teaches in the BSc in Mathematical Engineering in Data Science. His research interests include machine learning, approximate inference, optimal control, and complex networks , with applications in social networks, robotics, brain-computer interfaces, and urban systems. He applies advanced AI techniques to model human behavior, network dynamics, and decision-making processes. The recent publications highlight a strong focus on graph-based learning, reinforcement learning, social network analysis, and health informatics . His work integrates theoretical advances in probabilistic modeling with real-world applications in digital platforms, environmental monitoring, and mental health. There is a consistent theme of modeling complex systems through structured AI and interpretable models. Scientific Awards and Recognition: Coordinator of the prestigious Erasmus Mundus Joint Master in Artificial Intelligence Local Chair of UAI 2024, a top-tier conference in AI Program Committee member for ICAPS 2024 Organizer of the EMAI Summer School in collaboration with UCL Advising and Grants: Vicenç Gómez actively supervises PhD and Master's students, including Nur Alvarez-Gonzalez, Roger Garriga, Emily Theophilou, and Sergio Calo. His advising spans topics in emotion detection, mental health modeling, air quality prediction, and representation learning. He has been involved in organizing major academic events and leading international educational programs, indicating significant leadership and collaborative grant activity. Labs and Research Groups: He is a key member of the Artificial Intelligence and Machine Learning group at UPF’s Department of Engineering, based at the Roc Boronat building in Barcelona. His team engages in interdisciplinary research combining AI theory with applications in social, health, and urban domains.