جست‌وجوی استاد و پژوهشگر

Sethu Vijayakumarمشاهده پروفایل

استاد

• Robotics
• Machine Learning
• Motor Control
• +۵ مورد دیگر

Professor Sethu Vijayakumar is a globally recognized expert in School of Informatics , University of Edinburgh , holding a Personal Chair in Robotics and the Microsoft Research-RAEng Chair . He serves as Programme Director for Robotics and AI at The Alan Turing Institute and Director of the Edinburgh Centre for Robotics . PhD in Computer Science (1998) from Tokyo Institute of Technology Adjunct Faculty at University of Southern California Visiting Research Scientist at ATR Computational Neuroscience Labs , Kyoto Research Interests focus on Robotics , Statistical Machine Learning , Humanoid Locomotion , Shared Autonomy , and Computational Neuroscience . His work bridges anthropomorphic robot control , prosthetics , and dynamic environment interaction , exemplified by collaborations with NASA on the Valkyrie humanoid robot for Mars missions. Scientific Contributions include over 160 publications, with notable RSS Best Paper Finalist (2019), CoRL Best Paper Finalist (2018), and IEEE T-RO Best Paper Award (2013). His Statistical Learning & Motor Control (SLMC) Lab pioneers real-time motion planning , impedance control , and human-robot symbiosis . Outreach : TED Talks, BBC micro:bit initiative, and Robot Wars judge (Series 8-10) Awards : 2015 Tam Dalyell Prize IEEE Vincent Bendix Award Royal Society of Edinburgh Fellow RAEng Senior Research Fellowship

Scott Beardsleyمشاهده پروفایل

دانشیار

• Neuroengineering
• Neuroplasticity
• Human Visuo-Motor Processing
• +۳ مورد دیگر

Dr. Scott Beardsley is an Associate Professor and Director of Undergraduate Studies in the Joint Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin (MCW). His research focuses on neuroengineering, neuroplasticity, and sensorimotor control, with applications in stroke rehabilitation, multiple sclerosis, and epilepsy. He holds a B.S. in Physics and Mathematics from the University of Wisconsin-Whitewater, a Ph.D. in Biomedical Engineering from Boston University, and completed a postdoctoral fellowship there. His research integrates experimental and computational approaches to study neural coding, functional neuroimaging, and clinical neuroscience. Key areas include EEG-fMRI coupling, cortical activity in movement disorders, and neuroprosthetic design. He leads the Integrative Neural Systems Laboratory (INSL) and teaches courses like Biomedical Instrumentation Design and Neural Engineering. Recent work involves predicting seizure onset zones via machine learning and exploring visual-motor deficits in neurological disorders. He collaborates on grants focusing on sensorimotor error correction and clinical neurology research. His contributions advance translational neuroscience, bridging engineering and clinical practice.

• Computer Vision
• Sensor Fusion for Mobile Robotics
• Low Power Computing
• +۴ مورد دیگر

Jorge Lobo is an Assistant Professor at the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Coimbra. His research focuses on computer vision, sensor fusion for mobile robotics, and low power computing, with recent emphasis on quantum computing and bio-inspired computational approaches. B.Sc/M.Sc in Electrical Engineering (University of Coimbra) Ph.D. in Electrical and Computer Engineering (2007, thesis: 'Integration of Vision and Inertial Sensing') Research Interests: Specializes in artificial perception systems combining computer vision and inertial sensing for robotics. Develops probabilistic models for sensor fusion and explores unconventional computing architectures like stochastic circuits and quantum computing for efficient Bayesian inference. Key applications include autonomous robotic grasping and disaster response systems. Scientific Leadership: Principal Investigator for Q-Bet: Bridging classical/quantum computing via HPC and bio-inspired methods Founding member of Quantum@UC interest group Coordinator of quantum computing specialization courses (collaboration with Physics & Computer Science departments) Key Projects: Contributed to European initiatives BACS (Bayesian Cognitive Systems), HANDLE (robotic dexterity), BAMBI (Bayesian inference hardware), and ECOBOTICS.SEA (marine ecosystem robotics). Academic Recognition: IEEE Senior Member and former president of the Portuguese IEEE RAS chapter. Developed innovative remote labs for stochastic computing education.

Guanhua Chenمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Applied Machine Learning
• +۴ مورد دیگر

Dr. Guanhua Chen is an Assistant Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). His research focuses on Natural Language Processing (NLP), Large Language Models (LLMs), and Multimodal AI, with applications in drug discovery and urban planning. He previously earned his Ph.D. from the University of Hong Kong and B.S./M.S. from Tsinghua University, with internships at Microsoft Research Asia and Huawei Noah’s Ark Lab. Education: Ph.D. in Computer Science (University of Hong Kong, 2022), B.S. and M.S. in Computer Science (Tsinghua University, 2012 & 2014) Dr. Chen's research explores LLMs, data synthesis, multimodal systems, and model compression. He leads a lab equipped with 16 RTX 4090 GPUs, 16 L40 GPUs, and 4 A100 GPUs, providing resources for students to experiment with open-source and proprietary LLM APIs. His recent publications highlight innovations in LLM jailbreaking, parameter-efficient tuning (MiLoRA), cross-lingual transfer (mCLIP), and evaluation frameworks for robustness testing. Notably, he was awarded the Microsoft Research Asia StarTrack Scholar in 2025. Key Publications: mCLIP (ACL 2023), ImPart (ACL 2025), MiLoRA (NAACL 2025) Dr. Chen actively seeks motivated PostDoc, PhD, and Master's students. He serves as an Area Chair for ACL and EMNLP and contributes to advancing AI through open-source projects like mCLIP on GitHub.

Paul R. Schraterمشاهده پروفایل

دانشیار

• Machine Learning
• Probabilistic Inference
• Perception
• +۵ مورد دیگر

Paul R. Schrater is an Associate Professor with a joint faculty appointment in the Departments of Psychology and Computer Science at the University of Minnesota. His research bridges computational approaches with cognitive and neural sciences, focusing on understanding human perception, decision-making, and motor control through probabilistic and machine learning frameworks. Dr. Schrater's research interests span across several interconnected domains in cognitive science and artificial intelligence. His work primarily investigates how humans use probabilistic methods to navigate perception and motor control challenges. He approaches these questions through a structured interdisciplinary lens, placing computational theory at the forefront to drive empirical work. His research aims to develop precise, testable theories of brain computations that can be validated through behavioral measures and brain imaging techniques. His publication record reveals consistent work in human perception, decision theory, and motor control, with a strong emphasis on Bayesian modeling approaches. Over the past decade, his research has evolved to incorporate increasingly sophisticated probabilistic models to explain phenomena ranging from perceptual bistability to optimal grasping strategies under uncertainty. His work demonstrates a clear trajectory toward integrating multiple sensory modalities and cognitive processes within unified computational frameworks. Dr. Schrater directs the Computational Perception and Action Laboratory, located in Elliott Hall at the University of Minnesota. The lab is equipped with a Visuo-Haptic workbench for testing visuo-motor behavior with virtual objects, and an adjustable reach laboratory for studying reaching behavior with actual objects. His teaching includes graduate courses such as "Mathematical Models of Human Behavior" (PSY 5018H) and "Artificial Intelligence II" (CSCI 5512), reflecting his interdisciplinary approach that bridges psychology and computer science.

George D Konidarisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۲ مورد دیگر

George D Konidaris serves as Associate Professor of Computer Science at Brown University, where his research bridges artificial intelligence, machine learning, and robotics with emphasis on autonomous decision-making systems. His work focuses on developing algorithms that enable robots and AI agents to learn hierarchical structures, discover reusable skills, and operate effectively in complex environments. Education: 2010: PhD, University of Massachusetts, Amherst 2003: MS, University of Edinburgh 2001: BS, University of the Witwatersrand 2000: BS, University of the Witwatersrand His research spans reinforcement learning , robotic motion planning , and hierarchical abstraction , with significant contributions to skill discovery, temporal abstraction, and model-based methods. Current work integrates visuo-haptic perception for manipulation tasks and explores language-guided robotics using large language models. His approach emphasizes creating systems that learn compact world representations for efficient long-horizon planning in partially observable environments. Analysis of his 2025 publications reveals strong trends in model-based reinforcement learning with focus on memory mechanisms, uncertainty quantification, and hierarchical skill composition. Key themes include temporal abstraction for planning efficiency, visuo-haptic fusion for robotic manipulation, and language grounding for task specification. His work increasingly connects cognitive science concepts like theory of mind with AI capabilities. Teaching responsibilities include CSCI 1410 (Artificial Intelligence) and CSCI 2951X (Reintegrating AI), where he bridges theoretical foundations with practical robotics applications.

Mohsen Kaboliمشاهده پروفایل

استادیار

• Embodied Robotics
• Tactile Intelligence
• Machine Learning
• +۳ مورد دیگر

Mohsen Kaboli is an Assistant Professor (Part-time) at Eindhoven University of Technology (TU/e) in the Netherlands, affiliated with the Department of Electrical Engineering and the Electronic Systems group. Since 2018, he has served as head of the AI, Robotics & Cognitive Vehicle research lab at BMW Group’s Center of Invention in Munich, Germany. Previously, he held positions at Radboud University’s Institute for Brain and Cognition (2019-2022) and Technical University of Munich (TUM) as a postdoctoral research fellow. His research focuses on Embodied Robotics, Visuo-Tactile Interactive Perception, and Machine Learning, with applications in Robotic Grasping, Human-Robot Collaboration, and Neuromorphic Computing. He leads European-funded projects like PHASTRAC (Oscillatory Neural Networks for AI Edge Computing) and INTUITIVE (Tactile User Interfaces). Keywords: Tactile Intelligence, AI, Robotics Application Domains: Mobile Robotics, Medical Instrumentation, Neuroprosthetics His recent publications (e.g., ViTract, Shared Visuo-Tactile Perception) emphasize robust object pose estimation and shape reconstruction using Bayesian filtering and Graph Neural Networks. He has authored ~40 academic works and contributed to 20 patents. Scientific Awards : IEEE Senior Member (2018) Georges Giralt Ph.D. Award (Finalist) IEEE ICRA Outstanding Paper Award (Finalist, 2023) IEEE FLEPS Outstanding Student Award (Winner, 2022) IEEE ROSE Outstanding Paper Award (Winner, 2024) Mohsen Kaboli serves as Editor/Associate Editor for IEEE ICRA, IROS, R-AL, T-RO, and IJRR. His work bridges tactile perception, AI, and robotics, with impacts in wearables, medical devices, and autonomous systems.