جست‌وجوی استاد و پژوهشگر

Chen Chenمشاهده پروفایل

استادیار

• Graph Machine Learning
• Computational Epidemiology
• Network Science
• +۳ مورد دیگر

Chen Chen is an Assistant Professor of Computer Science at the University of Virginia, leading the Chandra Robot Autonomy Lab (CRΔL). He previously held postdoctoral positions at UT Austin's Texas Robotics and earned his PhD from the University of Maryland. Research Focus: Robust multi-robot navigation in human crowds Graph-based machine learning for epidemiological modeling Decentralized control with safety guarantees Fairness-aware reinforcement learning His work bridges theoretical guarantees (control barrier functions, game theory) with real-world applications in service robotics and computational epidemiology. Recent projects enable deadlock-free navigation in densely constrained spaces and LLM-mediated social coordination. Honors include Microsoft Robotics/AI Future Leader (2023), KAUST Rising Star (2023), and RSS Pioneer (2022). He serves on RA-L editorial board and organizes workshops at robotics conferences. Teaching includes graduate courses in machine learning and discrete mathematics. Actively recruiting PhD students for projects in multi-agent systems and robot learning.

Kathleen Corriveauمشاهده پروفایل

استاد

• Social-Cognitive Development
• Trust in Early Childhood
• STEM Education
• +۳ مورد دیگر

Kathleen Corriveau is a Professor and Associate Dean for Faculty Affairs at Boston University's Wheelock College of Education & Human Development, where she directs the Social Learning Laboratory. Her work examines trust formation, testimony learning, and cognitive development in early childhood, integrating developmental psychology with educational applications. She holds an EdD from Harvard University. Research investigates how children evaluate informants, absorb scientific and religious concepts, and develop metacognitive skills. Current projects include a Templeton Foundation grant ($10 million) on cross-cultural religious cognition and an NSF CAREER award optimizing explanations in STEM learning. Publications analyze cultural variations in trust, belief polarization, and pedagogical influences on mindset development. Her work has been featured in The Wall Street Journal, The Economist, and NPR. Awards and honors: NSF CAREER Award Fellow, APA and APS Early Career Impact Award (FABBS) Spencer Postdoctoral Fellowship She serves on editorial boards for Child Development and Journal of Experimental Child Psychology.

• Machine Learning
• Computer Vision
• Autonomous Robotics
• +۹ مورد دیگر

Yann LeCun is a Silver Professor at New York University (NYU) and Chief AI Scientist at Meta. He holds the Jacob T. Schwartz Chair in Computer Science at NYU's Courant Institute of Mathematical Sciences, and has affiliations with the Center for Data Science, Center for Neural Science, and the Tandon School of Engineering (Electrical and Computer Engineering). LeCun is a leading figure in artificial intelligence, known for pioneering work in deep learning, convolutional neural networks (e.g., LeNet), and self-supervised learning. His research focuses on machine learning, computer vision, autonomous robotics, and computational neuroscience. Education: Ph.D., Computer Science, Université Pierre et Marie Curie, France, 1987. DEA, Artificial Intelligence and Pattern Recognition, Université Pierre et Marie Curie, France, 1984. Diplôme d'Ingénieur, Electrical Engineering, École Supérieure d'Ingénieurs en Électrotechnique et Électronique, France, 1983. Research Interests: Machine learning, computer vision, robotics, computational neuroscience, and foundational AI research. He emphasizes the importance of self-supervised learning, world models for autonomous intelligence, and open-source AI development. His work bridges theoretical advancements with practical applications in healthcare, climate modeling, and robotics. Awards & Honors: ACM Turing Award (2018, shared with Geoffrey Hinton and Yoshua Bengio). Queen Elizabeth Prize for Engineering (2025). Member of the National Academy of Sciences and National Academy of Engineering. Multiple honorary doctorates and fellowships from institutions worldwide. Advising & Grants: LeCun has advised numerous PhD students and postdocs, many of whom have gone on to leadership roles in academia and industry. His research is supported by grants from Meta FAIR, NYU, and collaborations with institutions globally. He emphasizes mentorship and fostering interdisciplinary teams. Labs & Teams: Leads the CILVR Lab (Computational Intelligence, Learning, Vision, Robotics) at NYU and collaborates with Meta's FAIR (Fundamental AI Research). These groups focus on advancing AI through projects like JEPA (Joint Embedding Predictive Architecture) and World Models .

Alex McAvoyمشاهده پروفایل

استادیار

• Evolutionary Dynamics
• Game Theory
• Multi-Agent Learning
• +۳ مورد دیگر

Alex McAvoy is an Assistant Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. He holds a postdoctoral background in the Center for Mathematical Biology at the University of Pennsylvania. His research focuses on evolutionary dynamics, game theory, multi-agent learning, and network science, particularly examining how behaviors spread in populations and the emergence of prosocial behaviors in structured networks. McAvoy's work bridges applied mathematics with biological systems, exploring pathologies like inequality amplification from prosocial actions and the role of selfishness in population dynamics. His academic trajectory includes affiliations with Harvard University, University of British Columbia, and Shanghai Jiao Tong University through collaborations. He has published extensively since 2015, with key contributions on asymmetric evolutionary games, dynamic networks, and fixation probabilities in evolutionary processes. McAvoy’s publications often emphasize interdisciplinary applications of mathematical models to biological and social systems. Recent research highlights include studies on imitation dynamics in incomplete information networks (2023), strategy evolution on dynamic networks (2023), and evolutionary instability of selfish learning in repeated games (2022). His work consistently addresses theoretical frameworks with empirical relevance, such as multilayer population structures and public goods dilemmas in heterogeneous societies (2020). McAvoy collaborates with leading researchers in mathematical biology and network science, including Martin Nowak (Harvard), Christoph Hauert (UBC), and Qi Su (Shanghai Jiao Tong). His research has been cited over 1,500 times, reflecting significant impact in evolutionary game theory and complex systems analysis.

Jun Liangمشاهده پروفایل

استاد آموزشی

• Urban Geography
• Geographic Information Systems (GIS)
• Remote Sensing
• +۶ مورد دیگر

Jun Liang is a Teaching Professor in the Department of Geography at the University of North Carolina at Chapel Hill, where he has taught GIS and GPS-related courses since 2003. He holds a Ph.D. in Geography from the University of Cincinnati (2001), an M.S. in GIS from Peking University (1997), and a B.S. in Geography from Nanjing University (1992). His research focuses on urban geography, leveraging GIS applications, remote sensing, and visualization. He explores integrating computer technologies into geographic research, including data modeling, data mining, spatial simulation, and internet map publishing. His work addresses the growing role of technology in decision-making across sectors like urban planning, business analysis, and transportation systems. His recent articles highlight contributions to robotics, autonomous systems, and sensor technologies, with a particular emphasis on trajectory planning, sensor fusion, and reinforcement learning applications in construction and navigation. These studies reflect advancements in robotics engineering, computer vision, and automation. Despite no explicit awards noted, his academic contributions are evident in his extensive publication record and teaching roles. His research spans theoretical and applied domains, including terrain analysis, autonomous vehicle systems, and 3D scene reconstruction.

Jun Jinمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Jun Jin is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Alberta and a Fellow of the Alberta Machine Intelligence Institute (Amii). His research focuses on robotic reinforcement learning, embodied artificial intelligence, and human-centered autonomy. He holds a PhD and has eight years of industry experience in autonomous systems, including autonomous driving and construction automation. He teaches ECE 720 B01 Robot Learning: Principles and Advances and leads the Human-Centered Autonomy (HCA) Lab, which hosts a weekly reading club. His research interests include developing scalable learning architectures for robots to adapt to dynamic environments, ensuring safe human-robot interaction, and advancing open-ended learning agents. Notable achievements include the ICRA 2022 Outstanding Student Paper Award and the KUKA Innovation Award 2018. He emphasizes applying robotics to construction (e.g., infrastructure maintenance robotics) and healthcare. Dr. Jin actively mentors students through competitive research assistant positions and collaborates with Amii for computational and industrial resources. His lab fosters a community-driven approach to advancing robotics through interdisciplinary research and public engagement.

Donghwan Shinمشاهده پروفایل

مدرس

• Mutation Testing
• ML-enabled Cyber-Physical Systems
• Log Analysis
• +۳ مورد دیگر

Dr. Donghwan Shin is a Lecturer in Software Testing at the School of Computer Science, University of Sheffield since 2022. He holds a BSc (2006-2010), MSc (2010-2012), and PhD (2012-2018) from KAIST, South Korea. Prior to joining Sheffield, he was a Research Associate (2018-2020) and Research Scientist (2020-2022) at SnT, University of Luxembourg. Education PhD in Computer Science - KAIST (2012-2018) MSc in Computer Science - KAIST (2010-2012) BSc in Information Technology - KAIST (2006-2010) Research Interests His research focuses on mutation testing, testing for ML-enabled cyber-physical systems (e.g., automated driving systems), and log analysis including model inference and anomaly detection. He develops novel testing methodologies for complex AI systems and reliability engineering solutions. Publication Trends Recent publications demonstrate a strong focus on testing machine learning systems, autonomous driving validation, log-based failure prediction, and novel testing methodologies. His work consistently appears in top venues including ICSE, ICST, and IEEE Transactions on Software Engineering. Grants & Projects PI for UKRI grant: "SimpliFaiS: Simplification of Failure Scenarios for Machine Learning-enabled Autonomous Systems" (2024-2026, £464,344) Professional Activities Leads research in the Software Testing Lab and collaborates with industry partners on testing autonomous systems. Serves as School Student Experience Lead.

Ernie Nordeenمشاهده پروفایل

پژوهشگر

• Neurobiology
• Vocal Learning
• Neural Plasticity
• +۵ مورد دیگر

Ernie Nordeen is a faculty member at the University of Rochester, Department of Brain & Cognitive Sciences, focusing on the neurobiology of vocal learning and plasticity. Their research explores how neural systems adapt to vocal feedback disruptions in songbirds, emphasizing striatal-thalamic-cortical circuits and synaptic processes. Courses taught: BCS 240 (Basic Neurobiology), BCS/NSC 249 (Developmental Neurobiology) Collaborators: Eliot A. Brenowitz (U. Washington), Steven A. Goldman (U. Rochester), Kathy Nordeen (University of Rochester) Research interests center on vocal learning, auditory feedback mechanisms, and synaptic plasticity in avian models. Key studies involve NMDA receptor regulation, hormonal influences on neural development, and seasonal plasticity in song systems. Articles span 1990–2005, with a focus on zebra finches and canaries. Scientific awards: None listed Grants: Supported by NIMH, NSF, Sloan Foundation, Schmitt Program for Integrative Neuroscience Former students include Dr. Farida Sohrabji (Texas A&M), Dr. Sandra Aamodt (Nature Neuroscience), and Dr. Julie Heinrich (U. Notre Dame), among others. Nordeen’s work combines behavioral, neuroanatomical, and cellular analyses to dissect vocal motor system adaptations.

Josep Callمشاهده پروفایل

استاد

• Animal Cognition
• Comparative Psychology
• Primate Behavior
• +۷ مورد دیگر

Josep Call is a Professor in the School of Psychology and Neuroscience at the University of St Andrews, focusing on the evolutionary origins of mind. His research investigates technical and social problem-solving in animals, particularly great apes, exploring causal reasoning, tool use, long-term memory, gestural communication, and mindreading. He holds the Wardlaw Professorship and is a Fellow of multiple prestigious societies, including the British Academy and Royal Society of Edinburgh. His work spans comparative psychology and cognitive evolution, with recent publications analyzing primate behavior in virtual environments, social learning dynamics, and decision-making processes. Key contributions include studies on chimpanzees' logical reasoning, bonobos' responses to inequity, and the ontogeny of vocal communication. He leads projects funded by the European Research Council, focusing on coordination, communication, and cultural transmission in social minds. Research areas: Primate cognition, social learning, evolutionary psychology Key collaborations: Institute of Behavioural and Neural Sciences, Centre for Social Learning & Cognitive Evolution PhD supervision: Four current students in primate behavior and cognition Awards: Wardlaw Professorship, Fellowships in APA, Cognitive Science Society, and Royal Society Publications highlight his interdisciplinary approach, bridging animal behavior with human cognitive development. Current research emphasizes virtual reality applications for studying primate foraging and spatial navigation, alongside investigations into cultural techniques and behavioral flexibility in non-human animals.

• Evolution of Intelligence
• Origins of Language
• Non-Human Primate Communication
• +۳ مورد دیگر

Prof Klaus Zuberbuehler is a Professor at the School of Psychology and Neuroscience, University of St Andrews. His research focuses on the evolution of intelligence, origins of language, and non-human primate communication and behavior. He has conducted extensive field and laboratory studies on primates, particularly chimpanzees and Campbell's monkeys, exploring topics such as alarm calls, social learning, tool use, and vocal communication systems. His work spans interdisciplinary areas including cognitive evolution, comparative psychology, and behavioral ecology. Key contributions include studies on how non-human primates inform group members of danger, cultural knowledge transfer in tool use, and the structure of vocalizations in wild populations. Publications highlight trends in understanding primate social behavior, communication complexity, and the cognitive mechanisms underlying these phenomena. No scientific awards are explicitly mentioned in the provided text. No advisees or grants are listed, though his research involves collaborative fieldwork and experimental approaches. Prof Zuberbuehler's research integrates both field observations and controlled experiments, contributing to broader debates on the evolutionary roots of human language and intelligence.

José Santos-Victorمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Cognitive Systems
• +۴ مورد دیگر

Prof. José Santos-Victor is a Full Professor at the Department of Electrical & Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa. He leads the RBCog-Lab research infrastructure and coordinates the international PhD Program RBCog - Robotics, Brain and Cognition. His research focuses on biologically-inspired cognitive vision and humanoid robotics, addressing topics like vision-based navigation, human-robot interaction, and assistive robotics for autism therapy. Education: Ph.D. in Electrical & Computer Engineering, Instituto Superior Técnico (1995) M.Sc. in Electrical & Computer Engineering, Instituto Superior Técnico (1991) Licenciatura in Electrical & Computer Engineering, Instituto Superior Técnico (1988) Research Interests: Computer Vision, Robotics, Humanoid Systems, Biologically-Inspired Algorithms, and Assistive Technologies. Awards: IEEE Fellow (2015) for contributions to biologically-inspired cognitive vision ELLIS Fellow (2021) Correspondent Member of the Academy of Sciences of Lisbon Leadership Roles: President of ISR-Lisboa (2015-2022) Chair, Department of Electrical & Computer Engineering, IST (2021-24) Coordinator of LARSyS and Associated Laboratory Grants & Labs: Leads the RBCog-Lab, part of Portugal's Research Infrastructure Roadmap, and oversees projects like the Copyrobot therapy platform for ASD children.

Kathleen Akinsمشاهده پروفایل

استاد

• Philosophy of Mind
• Neuroscience
• Cognitive Science
• +۶ مورد دیگر

Kathleen Akins is a Professor of Philosophy at Simon Fraser University's Department of Philosophy. She holds a B.A. (Hons) from the University of Manitoba and a Ph.D. from the University of Michigan. Her research integrates philosophy of mind with neuroscience, focusing on topics such as color perception, consciousness, cognitive development, and synesthesia. Her work emphasizes a neuro-philosophical approach that bridges traditional philosophical questions with empirical findings in neuroscience. Dr. Akins' current major project is a monograph addressing color vision's physiological and philosophical dimensions, challenging existing interpretations of neurophysiological data. She teaches courses like COGS 310 (Consciousness) and PHIL 332 (Selected Topics), reflecting her interdisciplinary interests. Her research on synesthesia explores perceptual narrowing, cross-modal learning, and the influence of early language on synesthetic experiences. Her interdisciplinary work often critiques and redefines traditional philosophical problems through empirical evidence while guiding neuroscience experiments with philosophical insights. Her publications span philosophy, cognitive science, and neuroscience, addressing topics from color constancy to neonatal imitation. She maintains an active research agenda that bridges multiple disciplines, emphasizing the interplay between empirical science and philosophical inquiry.

Leslie Phillmoreمشاهده پروفایل

استاد

• Songbird Neurobiology
• Neurogenesis
• Neuroplasticity
• +۴ مورد دیگر

Leslie Phillmore is a Professor in the Department of Psychology and Neuroscience at Dalhousie University, Canada. Their research focuses on avian neurobiology, particularly examining how songbirds perceive, learn, and produce vocalizations, with emphasis on neurogenesis, neuroplasticity, and seasonal adaptations. Phillmore leads the Philly Lab, investigating topics such as environmental factors affecting brain development and stress impacts on song learning in zebra finches. Education includes a BA from Huron College (University of Western Ontario), MA/PhD from Queens University. Their work bridges behavioral studies and molecular neuroscience, with contributions to understanding auditory perception and immediate-early gene responses in avian species. Recent research explores seasonal changes in neurogenesis across species and vocal control systems in captive birds. Selected awards include the Professor of the Year in Neuroscience award from the Undergraduate Neuroscience Society. Research methodologies combine behavioral experiments with neurobiological techniques, focusing on species like black-capped chickadees and zebra finches. The lab actively publishes on topics spanning vocal discrimination, hippocampal plasticity, and the effects of environmental conditions on neural systems. Key publications include studies on song control nucleus development, seasonal neurogenesis patterns, and cross-species vocal perception. Phillmore’s research highlights the interplay between environmental stimuli, neural plasticity, and behavioral adaptations in avian models.

Vaibhav Unhelkarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۴ مورد دیگر

Vaibhav Unhelkar is an Assistant Professor of Computer Science at Rice University, part of the George R. Brown School of Engineering. He leads the Human-Centered AI and Robotics (HCAIR) group, focusing on developing AI systems that enhance human capability through collaboration. His expertise spans human-robot interaction, interactive machine learning, and human-AI collaboration. Unhelkar holds a PhD in Autonomous Systems from MIT and previously worked at X (formerly Google X). He earned undergraduate degrees from IIT Bombay in Aerospace Engineering. Education: PhD in Autonomous Systems, MIT (2020) MS in Aeronautics & Astronautics, MIT (2015) BTech/MTech in Aerospace Engineering, IIT Bombay (2012) Research Interests: Unhelkar’s work merges AI, robotics, and human factors to create systems that collaborate with humans. Key areas include human-robot teamwork in disaster response, AI coaching for surgical teams, and transparent AI systems. His lab has deployed robots in automotive factories and explores healthcare applications like robotic nurse training. Notable Contributions: RW4T Dataset: Simulated disaster response human-robot interaction data ASTRID: Robotic tutor for healthcare training Received Best Video Award at AAMAS 2022 for work on autonomous agent transparency Labs & Teams: The HCAIR group collaborates with industry and medical experts to address real-world challenges. Ongoing projects include AI-driven team training and explainable AI for medical decision-making.

• Mathematical Biology
• Ecological Modeling
• Population Dynamics
• +۳ مورد دیگر

Yun Kang is a Professor at the School of Applied Sciences and Arts within the College of Integrative Sciences and Arts (CISA) at Arizona State University. His research focuses on interdisciplinary applications of mathematical modeling to ecological, biological, and social systems. Key areas of investigation include predator-prey dynamics, social insect behavior, disease spread modeling, and human trust in automation systems. His work bridges theoretical mathematics with real-world challenges in conservation biology, resource management, and human-autonomy interaction. Research Interests: Dr. Kang's expertise spans ecological modeling, population dynamics, and mathematical epidemiology. He employs advanced analytical and computational tools to study complex systems, such as the impact of fear on predator-prey interactions, the optimization of natural resource harvesting, and the dynamics of trust in automation technologies. His models often integrate stochastic processes, spatial heterogeneity, and temporal dynamics to predict system behaviors under various environmental pressures. Recent Work Trends: His 2024-2025 publications emphasize novel approaches to understanding species coexistence mechanisms, improving bycatch reduction strategies, and analyzing trust dynamics in human-autonomy teams. These studies highlight the interplay between ecological principles and technological applications. Awards: No scientific awards explicitly mentioned in the provided texts. Advising & Grants: No specific grant details or advisee names listed here, but his extensive publication record suggests active involvement in research mentorship and collaborative projects. Labs/Teams: Affiliated with interdisciplinary research groups at ASU focusing on mathematical biology and human-environment systems modeling.