جست‌وجوی استاد و پژوهشگر

Brandon Haworthمشاهده پروفایل

دانشیار

• Computer Graphics
• Artificial Intelligence
• Human-Computer Interaction
• +۷ مورد دیگر

Dr. Brandon Haworth is a Tenured Research Stream Associate Professor in the Department of Computer Science at the University of Victoria. He leads the Graphics, Artificial Intelligence, Design, & Games (GAIDG) Lab and is a Research Fellow at the Institute on Aging and Lifelong Health. His research focuses on computer graphics, crowd simulation, multi-agent reinforcement learning, and assistive technologies. He holds a Ph.D. from York University and has been recognized with the Cowie Faculty Innovation Award (2022). Education: B.Sc. (Honours) in Computer Science (York University, 2013), M.Sc. (2016), Ph.D. (2019), and Post-Doctoral Fellow with the Ontario Research Fund/ISSUM project (2019–2020). Research Interests: Graphics, agent-based modeling, crowd simulation, human-centred AI, game design, virtual reality, and assistive healthcare technologies. His work emphasizes interdisciplinary applications in architecture, urban design, and rehabilitation. Notable Contributions: Developed simulation tools like SimService for architectural design analysis, and contributed to gamified motor speech rehabilitation for neurodegenerative diseases. He chairs Graphics at the Graphics Interface Conference (GI ’24) and co-chairs AI in Simulation tracks at ANNSIM 2024. Awards: Cowie Faculty Innovation Award (2022), NSERC Discovery Grant (2021), and CFI infrastructure funding for the GAIDG Lab (2023). Teaching & Service: Teaches 3D Graphics and Rendering Essentials, and serves as Editor-in-Chief of the Canadian Human-Computer Communications Society. Supervised numerous graduate students and postdocs, though specific names are not listed here.

Martin Jagersandمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Medical Imaging
• +۲ مورد دیگر

Martin Jagersand is a Professor in Computing Science at the University of Alberta, specializing in robotics, computer vision, and medical imaging. His research develops real-time vision systems for robot control in unstructured environments, including assistive technologies and space telerobotics. Key projects include Back2Future-SIM for predictive haptic feedback in teleoperation, quantum models for visual neuroscience with Concetta Morrone, and wheelchair-mounted robotic manipulators for independent living. His work emphasizes applications in healthcare, space exploration, and autonomous systems. He teaches courses like CMPUT 428 (3D Computer Vision) and mentors graduate students in robotics and machine learning. His recent publications focus on data-driven teleoperation and activity analysis for assistive robotics.

Edward Hubbardمشاهده پروفایل

دانشیار

• Educational Neuroscience
• Cognitive Development
• Neuroimaging
• +۴ مورد دیگر

Edward Hubbard is an Associate Professor at the University of Wisconsin-Madison, affiliated with the School of Education’s Department of Educational Psychology, the Neuroscience Training Program, and the Waisman Center. His research focuses on educational neuroscience, exploring how educational experiences shape neural circuits underlying numerical and mathematical abilities in children and adults. He holds a PhD in Psychology and Cognitive Science from the University of California, San Diego (2004), along with an MA (2001) and a BA in Cognitive Science from UC Berkeley (1998). His research interests span developmental cognitive neuroscience, synesthesia, and the neural foundations of mathematical learning. Notable projects include studying spatial-numerical associations, the cognitive mechanisms of fractions understanding, and the impact of synesthesia on perception and memory. He leads the Educational Neuroscience Lab, advancing interdisciplinary approaches to education through neuroimaging and behavioral studies. Hubbard’s recent work emphasizes educational interventions involving robotics (e.g., RoboMath) to enhance children’s numerical skills and parental engagement. His articles explore topics like fraction processing, math anxiety effects, and embodied cognition in mathematical learning. Awards: UW-Madison Teaching Academy Inductee (2018) Award for Mentoring Undergraduates (2017) Highly Commended Book Award for The Oxford Handbook of Synesthesia (2014) Hubbard collaborates on grants exploring educational neuroscience and has advised numerous undergraduate researchers. His lab integrates fMRI, EEG, and behavioral experiments to bridge educational practice and cognitive theory.

Wencen Wuمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Control Theory
• +۲ مورد دیگر

Wencen Wu is an Associate Professor in the Computer Engineering Department at San José State University, specializing in robotics, machine learning, and cyber-physical systems. She holds a Ph.D. and M.S. from Georgia Institute of Technology, and dual M.S./B.S. degrees from Shanghai Jiao Tong University. Current research focuses on autonomous systems integration with NSF-funded projects including RINGS (co-PI), EES (co-PI), and CPS (lead PI). Research Focus: Integration of machine learning with control theory for multi-robot systems, distributed sensing, and autonomous decision-making in dynamic environments. Publication Trends: Recent work demonstrates strong focus on multi-agent coordination for environmental monitoring, federated learning applications in transportation, and blockchain solutions for supply chain management. Methodological innovations include distributed Kalman filtering and reinforcement learning frameworks.

Md Shirajum Munirمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Trustworthy Artificial Intelligence
• +۷ مورد دیگر

Md Shirajum Munir is an Assistant Professor of Computer Science at the School of Computing, Analytics, and Modeling, Dr. Perry College of Mathematics, Computing, and Sciences, University of West Georgia. Previously, he held roles as a Research Assistant Professor at Old Dominion University (2023–2024), Visiting Faculty at Air Force Research Lab (2024), and Postdoctoral Researcher at Virginia Modeling, Analysis, and Simulation Center (2022–2023) and Kyung Hee University (2021–2022). His research focuses on trustworthy AI, stochastic models for cyber-physical systems, edge computing, and resilient smart grids. Education: B.S., Computer Science and Engineering, Khulna University, 2010 Ph.D., Computer Engineering, Kyung Hee University, 2021 Research Interests: Machine Learning & Data Science Trustworthy AI and Explainable AI Edge/Sustainable Computing and IoT Cyber-Physical Systems Security Smart Grid and Energy Management Reinforcement Learning and Stochastic Optimization His recent articles explore topics like multi-agent reinforcement learning for urban air mobility, AI-driven cybersecurity frameworks, and energy-efficient communication systems. He serves as an Associate Editor for the IEEE Internet of Things Journal and is a member of IEEE and its societies.

Byron Reevesمشاهده پروفایل

استاد

• Communication Technology
• Human-Computer Interaction
• Digital Media Effects
• +۵ مورد دیگر

Byron Reeves is the Paul C. Edwards Professor of Communication at Stanford University and Professor (by courtesy) in the Graduate School of Education. His research focuses on the psychological processing of media, affective computing, and digital behavior dynamics, with applications in social robots, autonomous systems, and educational technology. PhD in Communication from Michigan State University Co-founder of the Human Screenome Project (2020) Former Director of the Center for the Study of Language and Information Co-Director of H-STAR Institute Founding Director of mediaX at Stanford Reeves' work examines media effects on attention, memory, and emotional responses. His recent research includes computational analysis of digital affective dynamics, media production patterns on smartphones, and symbolic vs. natural interactions with social robots. He applies AI and deep learning to analyze super-intensive longitudinal data. His 2020-2024 publications reveal trends in digital behavior analysis , affective computing , human-robot interaction , and media psychology . Notable methodologies include time-series analysis of smartphone use and computational affective modeling. International Communication Association Fellow (1997) ICA Fellows Book Award for The Media Equation (with Clifford Nass) Novim Foundation Epiphany Science and Society Award Paul C. Edwards Professorship (1992) As a doctoral advisor at Stanford, Reeves guides research in communication theory, media psychology, and digital behavior. He leads the Human Screenome Project, a collaborative initiative involving the Wu Tsai Human Performance Alliance and Precourt Institute for Energy, focusing on real-time digital life analysis through screenshot tracking and AI-driven behavioral modeling.

Lucy Chekeمشاهده پروفایل

استاد

• Learning and Memory
• Cognitive Neuroscience
• Artificial Intelligence Evaluation
• +۳ مورد دیگر

Dr. Lucy Cheke is a Professor of Experimental Psychology at the University of Cambridge and Director of Studies for Psychological and Behavioural Sciences at Sidney Sussex College. Her research focuses on learning and memory processes across health and disease, with emphasis on sub-clinical deficits associated with obesity, long COVID, and AI cognition evaluation. She leads the Cambridge Lab (http://www.camblab.psychol.cam.ac.uk/), developing translational cognitive assessments for human, animal, and computational models. Key projects include the COVCOG studies on post-COVID cognitive impacts and the Animal-AI Environment for benchmarking artificial intelligence against animal cognition. Affiliations: Department of Psychology, Sidney Sussex College Roles: Academic Staff, Director of Studies, Principal Investigator Research Interests: Episodic memory development, comparative cognition, AI evaluation frameworks, and cognitive deficits in neurological conditions. Her work bridges psychology, neuroscience, and artificial intelligence, emphasizing translational tools like the Animal-AI Testbed. Publications highlight longitudinal studies on long-COVID’s cognitive effects, AI benchmarking methods, and object permanence research in embodied agents. She actively contributes to debates on general intelligence metrics and common-sense reasoning in language models. Grants & Advising: Supervises PhD students and collaborates on grants exploring cognitive aging, obesity-related memory deficits, and AI ethical evaluation. Her team’s work has been featured in interdisciplinary journals and conferences. Labs/Teams: Cambridge Cognition Lab (CamBlab), part of the Department of Psychology’s research network.

Michiel van de Panneمشاهده پروفایل

استاد

• Computer Graphics
• Robotics
• Machine Learning
• +۴ مورد دیگر

Michiel van de Panne is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on computer graphics, robotics, and artificial intelligence, with emphasis on physics-based animation, reinforcement learning for motion control, and character animation. He teaches advanced courses such as Computer Animation (CPSC 426) and Topics in Computer Graphics (CPSC 533V), emphasizing topics like Learning to Move and Motion Optimization. Dr. van de Panne has been recognized with prestigious awards including the SIGGRAPH Academy Membership, SIGGRAPH Computer Graphics Achievement Award, and multiple Best Paper Awards across conferences like ACM SIGGRAPH and IEEE Vis. His work bridges theoretical advancements with practical applications in interactive animation and autonomous systems. His research group explores cutting-edge methods for simulating and controlling dynamic motions, including locomotion in complex environments, motion planning with diffusion models, and imitation learning for humanoid and quadrupedal robots. He collaborates on projects ranging from physically-based balance control to curriculum-driven learning for stepping stone skills. Research Themes: Physics-based animation, reinforcement learning, motion synthesis, character control, and sim-to-real robotics. Key Contributions: SIMBICON biped control, DeepMimic example-guided RL, and diffusion-based motion planning techniques. His educational contributions include developing novel curricula and receiving the UBC 2018 Faculty Teaching Award, reflecting his commitment to advancing both research and education in computer graphics and AI.

Mykel Kochenderferمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Aerospace Engineering
• Decision-Making Systems
• +۶ مورد دیگر

Mykel Kochenderfer is an Associate Professor at Stanford University's Department of Aeronautics and Astronautics and holds a courtesy appointment in Computer Science. He is a Senior Fellow at the Stanford Institute for Human-Centered AI (HAI) and directs the Stanford Intelligent Systems Laboratory (SISL), focusing on advanced algorithms for robust decision-making systems in aerospace and autonomous systems. His research emphasizes safety and efficiency in uncertain environments, with applications in air traffic control, unmanned aircraft, and automated driving. Education: PhD from the University of Edinburgh (2006), M.S. and B.S. in Computer Science from Stanford (2003). Prior to Stanford, he worked at MIT Lincoln Laboratory on airspace modeling and collision avoidance, leading to the ACAS X program. He co-directs the Center for AI Safety, and is affiliated with SAIL, HAI, the Symbolic Systems Program, Bio-X, and the Wu Tsai Neurosciences Institute. Research interests span decision-making under uncertainty, optimization algorithms, AI safety, and robotics. He authored influential textbooks including Decision Making under Uncertainty (2015), Algorithms for Optimization (2019), and Algorithms for Decision Making (2022). Recognitions include the 2017 DARPA Young Faculty Award. His work bridges theoretical AI with real-world applications in aviation, healthcare, and environmental systems. Labs and collaborations include SISL, the Center for AI Safety, and interdisciplinary teams at Stanford. He advises students from multiple departments and actively participates in policy discussions on AI governance and ethics.

Rika Antonovaمشاهده پروفایل

دانشیار

• Robotics
• Reinforcement Learning
• Machine Learning
• +۷ مورد دیگر

Dr. Rika Antonova is an Associate Professor at the Department of Computer Science and Technology, University of Cambridge , UK. Her research focuses on robot learning , data-efficient reinforcement learning , and simulation-to-reality transfer . She offers fully funded PhD positions in robot learning, novel robot hardware design, and reinforcement learning (RL) at Cambridge, where she also teaches an RL course. Educational Background: PhD in Robotics from KTH Royal Institute of Technology, Sweden (advisor: Danica Kragic). MSc in Robotics from Carnegie Mellon University (advisor: Emma Brunskill). Research Interests: Antonova's work bridges Bayesian optimization , deformable object manipulation , and multimodal sensor design . Her recent projects include equivariant diffusion policies and GPU-accelerated control frameworks . She has contributed to data-efficient RL algorithms and topological representations for deformable objects . Recent Publications: Antonova's 2024 articles on EquiBot (CoRL) and Runtime Monitoring (CoRL) highlight her focus on generalizable policies and safe AI. Earlier works include DiffCloud (IROS 2022) for real-to-sim rendering and Bayesian Optimization in Variational Latent Spaces (CoRL 2020). Scientific Awards: NSF/CRA Computing Innovation Fellowship (postdoctoral). Research Community Involvement: Antonova serves on the Board of Directors of the Robot Learning Foundation and has been an Associate Editor for ICRA (2024), Chair for CoRL Demos (2024), and reviewer for JMLR, Nature Machine Intelligence, and top robotics conferences.

• Brain-Machine Interfaces
• Neural Decoding
• Computational Neuroscience
• +۱ مورد دیگر

Dr. Jonathan Kao is an Assistant Professor in Electrical Engineering at UCLA and member of the Brain Research Institute. His research develops computational techniques to understand neural population dynamics and create next-generation neural prosthetics. Education: Ph.D. in Electrical Engineering, Stanford University (2016) His laboratory investigates neural computational mechanisms using statistical and machine learning methods, with applications in brain-machine interfaces (BMIs). Key research areas include neural decoding algorithms, dynamics of neural populations, and high-performance BMI systems. Recent work explores recurrent neural network designs for robust intracortical interfaces and computational neuroscience approaches to model decision-making circuits. Publication analysis reveals three major themes: 1) advanced neural decoding architectures for prosthetics, 2) computational models of motor control and decision-making, and 3) evaluation of AI-brain correspondence. His group maintains collaborations with clinical neurosurgery teams for translational applications.

• Human-Robot Interaction
• Autonomous Vehicle Communication
• Urban Robotics
• +۴ مورد دیگر

Dr. Marius Hoggenmueller is a Lecturer at The University of Sydney's School of Architecture, Design and Planning, specializing in Human-Robot Interaction (HRI), urban robotics, and autonomous vehicle communication. His research focuses on designing socially acceptable and context-aware technologies for urban environments, including public interfaces, pervasive displays, and AV-pedestrian interaction systems. He leads projects exploring robot help-seeking strategies, adaptive urban interfaces, and ethical deployment of autonomous systems. Current research students include Abhinav Sood (Creative Collaborative AI), Xinyan Yu (Human-Robot Collaboration), and Qiuming Zhang (Urban Robotics Safety). Dr. Hoggenmueller has received the University of Sydney Strategic Education Grant 2024 and holds an extensive record of publications in top venues like ACM CHI, IEEE HRI, and Frontiers in Computer Science. Education: PhD in Media Architecture and Interaction Design (university unspecified) His work bridges design research and technical innovation through projects like 'Woodie' (urban placemaking robot) and 'Context-based Interface Prototyping.' Key contributions include scalable AV-pedestrian communication systems, emotional robotic eyes for AVs, and frameworks for probing future urban technologies. Dr. Hoggenmueller collaborates with industry partners on smart city initiatives and has organized workshops on holistic HMI design for automated vehicles. His lab explores tangible toolkits for AV interface design and participatory methods for urban technology deployment.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۳ مورد دیگر

Alexander Ilin is a Visiting Professor and part-time teacher in the Department of Computer Science at Aalto University, specializing in Artificial Intelligence and Machine Learning. He holds roles in both the Computer Science - Artificial Intelligence and Machine Learning (AIML) research area and the Professors of Practice group. His research focuses on Machine Learning, Reinforcement Learning, and Deep Learning applications, with contributions to areas like neural networks, generative models, and healthcare informatics. Dr. Ilin earned a Doctoral degree in Engineering and Technology from Helsinki University of Technology in 2006. His work aligns with UN Sustainable Development Goals, particularly in advancing education and innovation. Key projects include the Finnish Center for Artificial Intelligence (FCAI) and the APPIA project on privacy-aware AI applications. His research explores cutting-edge topics such as diffusion models for dynamical systems, reinforcement learning for robotics, and self-supervised forecasting in healthcare. He has led projects like APPIA (2020–2021) and FCAI (2020–2026), securing grants from the Academy of Finland and Business Finland. His work bridges theory and practice, with applications in autonomous systems, nanotechnology, and medical diagnostics. Notable activities include visiting research at the UK Met Office Hadley Centre and presentations at top conferences like NeurIPS and AAMAS. His lab focuses on scalable AI solutions for real-world challenges, emphasizing ethical AI and privacy-preserving techniques.

Gale Lucasمشاهده پروفایل

عضو هیئت علمی

• Human-Computer Interaction
• Affective Computing
• Trust-in-Automation
• +۵ مورد دیگر

Gale Lucas is a Research Assistant Professor affiliated with both the Institute for Creative Technologies and the USC Viterbi School of Engineering Computer Science Department . Her work focuses on human-computer interaction, affective computing, and trust dynamics in automated systems. She explores how environmental factors, AI negotiation strategies, and virtual training impact human behavior in high-stress scenarios such as workplace stress management and active shooter incident preparedness. Her research combines technical innovation with social science insights, leveraging machine learning and virtual reality to address challenges in workplace safety, healthcare preparedness, and human-robot collaboration. Notable projects include developing AI-driven negotiation agents (e.g., ASTRA), VR-based crisis training platforms, and frameworks for calibrating trust in automated systems. Lucas also investigates the interplay between indoor environmental quality and worker productivity through multimodal data analysis. Her work frequently involves cross-disciplinary collaboration across computer science, psychology, and architecture. Recent studies have examined the efficacy of VR training for emergency response, the role of scent in office environments, and the ethical implications of AI recommendation systems. Lucas has contributed to both theoretical frameworks (e.g., EVOLVE methodology for active shooter analysis) and practical tools like intelligent desk systems for office workers. Publications highlight her expertise in human factors engineering, emergency preparedness technologies, and algorithmic decision-making. While no formal grants or students are listed, her projects reflect significant stakeholder engagement through collaborations with healthcare facilities and construction industries.

• Sustainable Architecture
• Building Science
• Computational Design
• +۳ مورد دیگر

David Gerber is an Associate Professor of Civil and Environmental Engineering Practice and Architecture at the University of Southern California (USC), holding a joint appointment between the Viterbi School of Engineering and the USC School of Architecture. He serves as Program Director for the Civil Engineering Building Science undergraduate program and teaches in both schools. His professional experience spans architectural, engineering, and technology firms such as Zaha Hadid Architects, Gehry Technologies, and Arup, where he led global research initiatives. Gerber’s research focuses on integrating computational tools, robotics, and multi-agent systems to advance sustainable design methodologies and building performance. Key areas include energy-efficient architecture, circular economy principles in urban housing, and user-centric design through immersive virtual environments. His work is funded by industry partnerships, fellowships, and the NSF. Education: BA in Architecture (UC Berkeley, 1996), MArch (Architectural Association, 2000), MDesS (Harvard GSD, 2003), Doctor of Design (Harvard GSD, 2007). Research trends in his articles emphasize sustainability certification systems, autonomous robotic construction, and behavioral form-finding. He actively advises PhD students exploring intersections of computer science, robotics, and architecture. Advising and Grants: Gerber’s interdisciplinary approach bridges academia and industry, supported by grants and collaborations with leading firms. His past roles as Global Research Manager at Arup highlight his industry-academia synergy.