جست‌وجوی استاد و پژوهشگر

• Legged locomotion
• Humanoid and quadrupedal robots
• Wearable robotics
• +۵ مورد دیگر

Yan Gu is an Associate Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He is affiliated with the School of Mechanical Engineering within the College of Engineering. His research focuses on legged locomotion, humanoid and quadrupedal robots, wearable robotics, hybrid dynamical systems, control systems, state estimation, and dynamics. He leads the TRACE Lab and has been recognized with prestigious awards including the NSF CAREER Award (2021) and multiple teaching accolades. Gu holds a Ph.D. from Purdue University (2017) and a B.S. from Zhejiang University, China (2011). His work emphasizes robust control strategies for legged robots in dynamic environments, including adaptive ankle torque control, time-varying foot-placement algorithms, and state estimation techniques for non-inertial surfaces. His recent publications explore multimodal datasets in animal-robot interaction and the stabilization of quadrupedal locomotion on accelerating platforms. His research has been supported through grants such as the NSF CAREER Award, and his contributions span both theoretical advancements in hybrid control systems and practical applications in wearable robotics and exoskeleton design. Gu’s TRACE Lab serves as a hub for innovative robotics research, addressing challenges in robot-environment interaction and dynamic stability.

David Hsuمشاهده پروفایل

استاد

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

David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.

YingLi Tianمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

YingLi Tian is a CUNY Distinguished Professor in the Department of Electrical Engineering at The City University of New York. Their work focuses on computer vision, machine learning, and medical imaging. Key areas include sign language recognition, medical image analysis, and AI-driven healthcare solutions. Research Interests: Artificial Intelligence applications in healthcare 3D point cloud and scene understanding Self-supervised learning and domain adaptation Sign language recognition systems Medical imaging segmentation and diagnosis Human-robot interaction and assistive technologies Notable Projects: Developed AI systems for American Sign Language recognition using RGB-D data Pioneered self-supervised feature learning techniques in medical imaging Created virtual contrast enhancement tools for CT scans Advanced sea ice motion prediction using deep learning Labs & Teams: Leads the Media and Information Technology Lab at CCNY, focusing on multimodal AI and healthcare technology innovations.

Somil Bansalمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Control Theory
• +۳ مورد دیگر

Somil Bansal is an Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University, part of the School of Engineering. Previously, he served as an Assistant Professor in the Electrical and Computer Engineering (ECE) department at the University of Southern California. He holds a B.Tech. from IIT Kanpur, an MS, and a Ph.D. from UC Berkeley’s EECS department. Research Focus: Development of mathematical tools and algorithms for safety-critical autonomous systems, emphasizing learning-enabled systems’ safety. Key Collaborations: Waymo, Skydio, Google, Boeing, NASA AMES/JPL. Awards: NSF CAREER Award, Eli Jury Award, RSS Pioneer Award, and Outstanding Graduate Instructor Award. His research integrates control theory and machine learning to ensure safety in autonomous systems, focusing on safe learning frameworks, anomaly detection, and real-time safety guarantees. He leads the Safe and Intelligent Autonomy (SIA) Lab, which explores applications in robotics, autonomous driving, and aerospace systems. Teaching: Courses include Introduction to Control Design Techniques and Principles of Safety-Critical Autonomy. He advises doctoral students and supervises research projects in his lab.

Michael Farberمشاهده پروفایل

استاد

• Applied and Computational Topology
• Topological Robotics
• Stochastic Topology
• +۳ مورد دیگر

Michael Farber is a Professor of Mathematics at Queen Mary University of London's School of Mathematical Sciences. Previously, he held professorships at the Universities of Warwick, Durham, and Tel Aviv. His research focuses on applied and computational topology, topological robotics, stochastic topology, and their applications in distributed computing, genomics, and brain connectivity modeling. He has authored influential monographs such as Invitation to Topological Robotics and Topology of Closed One-Forms . Farber's current research includes projects funded by the Leverhulme Trust and EPSRC, addressing probabilistic and deterministic topology, automated motion planning, and topological robotics. He advises PhD students including Lewin Strauss, Gabriele Beltramo, and Lewis Mead. His work has been recognized with the Royal Society Wolfson Research Merit Award. Key research interests include parametrized topological complexity, sequential motion planning algorithms, and the intersection of topology with AI and robotics. His collaborations span interdisciplinary fields, such as using topological methods in cancer research and genomic analysis. Grants and funding include the Leverhulme Trust's 'Probabilistic and Deterministic Topology' and EPSRC's 'Topology of Automated Motion Planning.' Farber is affiliated with Queen Mary's Centre for Geometry, Analysis, and Gravitation, contributing to advancing topological methodologies in algorithmic and stochastic systems.

Quan Zhouمشاهده پروفایل

استاد

• Miniaturized Robotics
• Robotic Manipulation
• Mechatronics
• +۹ مورد دیگر

Quan Zhou is a Professor leading the Robotic Instruments Group at the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Finland. He holds an M.Sc. in Control Engineering and a Dr.Tech. in Automation Technology from Tampere University of Technology. His research focuses on miniaturized robotics, robotic manipulation using contact, acoustic, magnetic, interfacial, and fluidic methods, integrating physics, mechatronics, and machine learning to address challenges in dexterous manipulation with applications in biomedicine, materials science, and industrial technologies. He directs the Master’s Programme in Automation and Electrical Engineering (AEE) at Aalto and coordinates the European Robotics Association’s Topic Group on Miniaturized Robotics. He has led the EU FP7 project FAB2ASM and chaired international conferences like MARSS 2019. Notably, he received the 2018 Anton Paar Research Award for Instrumental Analytics and Characterization. His research spans fundamental methodologies and practical applications, emphasizing interdisciplinary innovation. Recent work includes advancements in fluid-driven manipulation, biomimetic robotics, and acoustic particle control. His contributions bridge theoretical frameworks and real-world automation solutions, with publications in journals like Advanced Intelligent Systems , Nature , and Physical Review E . Prof. Zhou’s leadership roles include coordinating the EIT Digital Master's Programme in Autonomous Systems and chairing IEEE Finland robotics chapters. His work has been recognized through grants and awards, reflecting his impact on robotics and automation research and education.

Nikolai Matniمشاهده پروفایل

استادیار

• Machine Learning
• Control Theory
• Robotics
• +۱۰ مورد دیگر

Nikolai Matni is an Assistant Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He holds a secondary appointment in the Department of Computer and Information Science and is a member of the Applied Mathematics and Computational Sciences (AMCS) graduate group. His research focuses on integrating learning, optimization, and control for safety-critical and data-driven cyber-physical systems, with applications in robotics, autonomy, and distributed control. Secondary Appointment: Computer and Information Science (University of Pennsylvania) Labs/Centers: GRASP Lab, PRECISE Center His research bridges machine learning, robust control, and autonomous systems, particularly in developing guaranteed-safe strategies for cyber-physical systems. He emphasizes the importance of reliability and robustness in learning-based control for applications like self-driving vehicles and agile robots, where failures could be catastrophic. Recent publications highlight advancements in vision-based robotic control, neural ODEs for motion planning, adversarial exploration strategies, and stability-constrained learning. These works span conferences such as ICRA, CoRL, WACV, IROS, and L4DC, with a focus on safety-critical applications. Notable scientific awards include the NSF CAREER Award, George S Axelby Award (for his work on System Level Synthesis), AFOSR YIP award, and Google Research Scholar Award. He was also elevated to IEEE Senior Member. Matni advises Ph.D. students in Computer and Information Science (CIS), Electrical and Systems Engineering (ESE), and Applied Mathematics and Computational Sciences (AMCS). His teaching includes courses like ESE 2030 (Linear Algebra with Engineering/AI applications) and others focused on control theory and robotics.

• Biomedical Engineering
• Control Systems
• Brain-Computer Interfaces
• +۷ مورد دیگر

Andrzej Majkowski is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering, Warsaw University of Technology. His career spans over two decades of research in biomedical engineering, focusing on brain-computer interfaces, signal processing, and emotion recognition. Active in both teaching and research, he contributes to advancing methodologies in electrophysiological signal analysis. Warsaw University of Technology Institute of the Theory of Electrical Engineering, Measurement and Information Systems Faculty of Electrical Engineering Specializing in biomedical engineering , Majkowski's research bridges control systems and information technologies with neuroscience applications. His work explores brain-computer interfaces , EEG/EMG signal processing , and emotion recognition using multimodal physiological data. Recent studies focus on deep learning architectures for artifact removal and classification tasks. Recent publications highlight trends in CNN-LSTM hybrid models for signal denoising, convolutional networks for seizure detection, and machine learning applications in visual evoked potential analysis. His work spans both clinical applications (epilepsy monitoring) and human-computer interaction (emotion recognition, sign language detection). With over 98 documented publications and significant bibliometric indicators (h-index 13 in Scopus), Majkowski has supervised 95 promoted theses. His research includes one funded project and collaborations in biomedical instrumentation, though specific award details remain unspecified in available records.

Brad Knoxمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Human-Computer Interaction
• Artificial Intelligence
• +۴ مورد دیگر

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).

Florent Cosandierمشاهده پروفایل

مدرس

• Compliant Mechanisms
• Flexure Mechanisms
• Precision Engineering
• +۵ مورد دیگر

Florent Cosandier serves as a Lecturer in the Section of Microtechnology and Research Associate at the Micromechanical and Horological Design Laboratory (INSTANT-LAB) within the School of Engineering at Swiss Federal Institute of Technology Lausanne (EPFL). His dual roles bridge precision engineering education and advanced research in mechanical systems design. His research centers on compliant and flexure mechanisms with applications spanning horology, space instrumentation, and micro-technology. Key focus areas include parasitic error minimization in translation stages, dynamic balancing for mechanical oscillators, and additive manufacturing of complex compliant systems. Recent work demonstrates experimental validation of zero-force mechanisms and novel pivot designs for high-precision positioning. Analysis of his 15 most recent publications reveals a dominant trend in rectilinear stage development (6 articles), horological applications (4 articles), and space telescope assembly systems (3 articles). His work consistently emphasizes experimental validation, large-range motion capabilities, and parasitic shift elimination through innovative parallel mechanism configurations. Cosandier has advised at least one PhD student at EPFL: Kruis Johannes Richard Cornelis Geerit. He contributes to teaching through courses like "Advanced mechanisms for extreme environments" within the SMT-ENS unit. As a core member of INSTANT-LAB, he collaborates on projects involving metallic additive manufacturing for damping systems, micro-vibration suppression platforms, and silicon-based flexure mechanisms. His current research trajectory shows increasing focus on space applications and bi-material additive manufacturing techniques.

• Conservation ecology
• Population ecology
• Landscape ecology
• +۳ مورد دیگر

Robert Fletcher is a Researcher at the University of Cambridge, affiliated with the Department of Zoology and the C-CLEAR Doctoral Training Partnership . His work focuses on applied ecology and conservation science, utilizing landscape and population ecology to address biodiversity challenges globally. Research Areas : Conservation biology, population ecology, landscape ecology, environmental informatics Collaborations : Partners in North America, Europe, Africa, and Southeast Asia Key Themes : Species extinction prevention, landscape conservation prioritization, and rapid biodiversity data delivery Email : rf497@cam.ac.uk Fletcher's interdisciplinary approach integrates fieldwork (e.g., Everglades endangered species, African elephants) with advanced modeling of habitat loss, fragmentation, invasive species, and climate change impacts. His recent work emphasizes: Drivers of species decline and recovery strategies Landscape management and restoration techniques Interdisciplinary collaborations with engineers, social scientists, and computer scientists His publications span topics like savanna ecosystem dynamics, community science applications, and conservation forecasting, reflecting a commitment to actionable science for global biodiversity preservation.

Sebastian Schmidtمشاهده پروفایل

پژوهشگر

• Active Learning
• Out-of-distribution Detection
• Uncertainty Estimation
• +۳ مورد دیگر

Sebastian Schmidt is a researcher at the Technical University of Munich (TUM) within the Department of Informatics - I26 (Data Analytics and Machine Learning) under Prof. Dr. Stephan Günnemann. His work is situated in the TUM School of Computation, Information and Technology, where he contributes to cutting-edge research in machine learning applications. His research focuses on active learning methodologies with particular emphasis on out-of-distribution detection and uncertainty estimation in open-world perception systems. His work bridges theoretical machine learning with practical applications in computer vision and robotics, as evidenced by publications in top-tier conferences like CVPR, ICRA, and IROS. His recent publications demonstrate a consistent research trajectory in developing active learning frameworks that address real-world challenges in perception systems. The publications show increasing sophistication from temporal stream processing to multi-agent robotic applications and safety-constrained sensor fusion. As a member of the Data Analytics and Machine Learning group, he collaborates extensively with colleagues including Leo Schwinn, Stephan Günnemann, and others across multiple publications. His research has practical implications for autonomous systems requiring reliable perception under uncertainty.

Jonathan Külzمشاهده پروفایل

پژوهشگر

• Modular Robotics
• Robotics
• Reinforcement Learning
• +۳ مورد دیگر

Jonathan Külz is a Researcher at the Technical University of Munich (TUM) , affiliated with the Department of Informatics 6 - Chair for Cyber Physical Systems under Prof. Matthias Althoff. His research focuses on Modular Robotics , Reinforcement Learning , Cyber-Physical Systems , and Control Systems . His work includes algorithmic synthesis of modular robot compositions, model-based manipulator co-design, and unifying benchmarks for robotics. He has supervised multiple Master’s theses Bachelor’s theses Practical courses on topics like Robot Workspace Representation and Dynamic Model Identification . Notable supervised projects include Autonomous Navigation of Reachbot and Task-Based Modular Robot Configuration Synthesis . His recent publications span Robotics , Benchmarking , and Computational Social Science . Key trends include Computationally efficient assessment of robot capabilities Deep reinforcement learning for robotics Analysis of political discourse polarization Jonathan emphasizes structured thesis supervision, requiring exposés, shared folders, and protocol-driven meetings. He advocates for LaTeX in scientific writing and tools like NotebookLM and Zettlr for research documentation.

Luca Sebastianiمشاهده پروفایل

استاد

• Plant Physiology
• Molecular Plant Stress Responses
• Nutraceuticals in Plants
• +۳ مورد دیگر

Luca Sebastiani is a Full Professor in Horticultural Sciences (AGR/03) at Scuola Superiore Sant'Anna in Pisa, Italy, since 2014. He currently coordinates the PhD Course in AgroBioSciences and has previously served as Director of the Institute of Life Sciences (2016-2021). His academic career includes roles as Associate Professor (2002-2014) and Assistant Professor (1998-2002) at the same institution. PhD in Plant Biology from Scuola Superiore Sant'Anna (1996) MSc in Agricultural Sciences from University of Pisa (cum laude, 1991) Postdoctoral research in agricultural biotechnology (1996-1998) Research Interests: Focus on plant-environment interactions, particularly abiotic and biotic stress responses in crops. Key areas include: Physiological and molecular responses to climate change stressors Nutraceutical enhancement of food crops Plant germplasm conservation using molecular markers Agriculture 4.0 integrating AI, IoT, and robotics Phytoremediation using poplar and Brassica species Scientific Contributions: His work bridges molecular mechanisms (aquaporin function, heavy metal transport) with ecosystem-level applications (precision irrigation, contaminant phytoremediation). Recent publications emphasize genome sequencing, stress tolerance modeling, and nutraceutical food development. ISHS Medal for SapFlow Workshop organization (2011) Giovanni Spitali Foundation Award for PhD dissertation (1998) Collaborations: Extensive international collaborations with institutions like Beijing Forestry University, Comenius University, and Purdue University. Currently supervises projects in plant phenotyping, omics technologies, and sustainable crop management.

Ruohan Gaoمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Multisensory Machine Intelligence
• +۴ مورد دیگر

Ruohan Gao is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park , with affiliate appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) , Maryland Robotics Center (MRC) , and Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . His research focuses on Computer Vision and Machine Learning , emphasizing Multisensory Machine Intelligence that integrates sight, sound, and touch . He aims to enable machines to perceive, understand, and interact with the world as humans do, with applications in robotic manipulation , audio-visual localization , and differentiable rendering . Article Trends : Span 2018–2025 , centering on audio-visual perception , multisensory datasets , and robotics . Recurring themes include object-centric learning , sound synthesis , and cross-modal consistency . Scientific Awards : Michael H. Granof Award (UT Austin’s Top 1 Doctoral Dissertation, 2021) Best Paper Award Runner-Up (BMVC 2021) Best Paper Award Finalist (CVPR 2019) Highlight Paper (CVPR 2023) He leads the UMD Multisensory Machine Intelligence Lab and collaborates with institutions like Stanford and The University of Texas at Austin . Contact: rhgao@umd.edu .