جست‌وجوی استاد و پژوهشگر

Zhengzhong Tuمشاهده پروفایل

استادیار

• Spatial Intelligence
• 3D Vision
• Multimodal and Generative AI
• +۳ مورد دیگر

Zhengzhong Tu is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, College of Engineering. His research focuses on spatial intelligence, 3D vision, multimodal AI, autonomous driving, and trustworthy large language models. He holds a Ph.D. from The University of Texas at Austin (2022) and M.S./B.S. degrees from Fudan University (2018/2016). Education Ph.D., Electrical and Computer Engineering, The University of Texas at Austin – 2022 M.S., Electrical Engineering, Fudan University – 2018 B.S., Electrical Engineering, Fudan University – 2016 Research Interests Tu’s work bridges generative AI, autonomous systems, and multimodal perception. He explores challenges in 3D reconstruction, cross-modal alignment, and safety-critical applications like autonomous driving. His recent projects focus on enhancing model trustworthiness through robust watermarking and constraint optimization, while advancing benchmarks for vision-language models in spatial reasoning. Publications Trends His 2025 articles emphasize generative AI applications in autonomous driving (e.g., crash prediction, sensor fusion), multimodal systems (e.g., vision-language navigation), and video quality enhancement. He also contributes to benchmark development (e.g., NTIRE challenges) and ethical AI practices. Awards No scientific awards explicitly mentioned in the text. Advising & Grants No student advisees or grant information listed. His research is supported by institutional affiliations and collaborative projects. Labs & Teams Not explicitly mentioned in the provided text. His work likely involves Texas A&M’s autonomous systems and AI research groups.

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.

William H Warrenمشاهده پروفایل

استاد

• Perception and Action
• Spatial Navigation
• Visual Control of Locomotion
• +۳ مورد دیگر

William H. Warren is Chancellor's Professor of Cognitive and Psychological Sciences at Brown University. He leads the Virtual Environment Navigation Lab (VENLab) and has held this position since 1982. His research focuses on the visual control of human locomotion and navigation, applying dynamical systems theory and virtual reality techniques. He has served as Chair of Brown's Department of Cognitive and Linguistic Sciences (2002-2010) and remains active in academic leadership roles. Education: BA (Hampshire College, 1976), PhD (Experimental Psychology, University of Connecticut, 1982) Postdoctoral Training: University of Edinburgh (1983) Research interests include human crowd behavior, obstacle avoidance, and the interplay between vision and action. Key contributions involve modeling collective motion using agent-based simulations and analyzing spatial navigation through path integration and cognitive graphs. His work bridges basic science and applied domains like robotics and urban design. Notable awards include the Ken Nakayama Medal (2023), NIH Career Development Award, and Brown's Elizabeth Leduc Teaching Award. Active grants focus on crowd dynamics and locomotion control, totaling over $3.5M in funding since 2019. Teaching includes courses such as Perception and Action and Core Concepts in Cognitive Sciences . Academic service includes leadership roles in the International Society for Ecological Psychology and editorial boards in top journals.

Sherry Yangمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Generative Modeling
• +۶ مورد دیگر

Sherry Yang is an Assistant Professor of Computer Science at NYU Courant Institute of Mathematical Sciences and a Staff Research Scientist at Google DeepMind. She leads the Computational Intelligence, Vision, and Robotics Lab (CILVR) at NYU, focusing on cutting-edge research at the intersection of machine learning, robotics, and AI for science. Dr. Yang received her B.S. and M.Eng. from MIT, followed by a Ph.D. from UC Berkeley under the supervision of Pieter Abbeel. She then completed a postdoctoral fellowship at Stanford University working with Percy Liang. Her research focuses on machine learning with particular emphasis on reinforcement learning and generative modeling. Recently, she has been exploring problems at the intersection of foundation models and decision making, including learning world models and agents, with applications in robotics and AI for science. Her work bridges theoretical advances with practical implementations in real-world scenarios. Dr. Yang's publications demonstrate significant contributions across multiple AI domains, showing an evolution from foundational reinforcement learning techniques to their application in complex domains like materials science and robotics, with particular emphasis on video understanding, world models, and agent systems. Outstanding Paper Award at ICLR 2024 for UniSim: Learning Interactive Real-World Simulators As an advisor, Dr. Yang mentors multiple PhD, Master's, and undergraduate students across various research projects. She has served on program committees for major conferences including NeurIPS, ICML, and ICLR, and has organized influential workshops on foundation models for decision making. Her research has attracted significant funding supporting innovation in machine learning engineering and scientific discovery. Dr. Yang leads the Computational Intelligence, Vision, and Robotics Lab (CILVR) at NYU, which maintains strong collaborations with Google DeepMind and other leading AI research institutions, creating a vibrant research ecosystem that spans academia and industry.

Paul Munroمشاهده پروفایل

دانشیار

• Connectionist systems
• Neural information processing
• Image processing
• +۵ مورد دیگر

Paul Munro is an Associate Professor at the University of Pittsburgh’s School of Computing and Information, where he holds a PhD from Brown University. His research focuses on connectionist systems, neural information processing, cognitive science, and artificial life. He has contributed to advancements in neural network architectures, including applications in healthcare and spatial cognition modeling. His educational background includes a PhD in a field related to computational neuroscience, with a strong emphasis on interdisciplinary approaches. Research Interests: Connectionist systems; neural information processing; image processing; modeling and simulation; cognitive science; models of learning; visualization; genetic algorithms/artificial life. Munro’s publications span over three decades, with notable contributions to understanding neural learning rules (e.g., BCM theory), adversarial machine learning, and applications of neural networks in healthcare. Recent work includes predicting falls in elderly populations and studying Alzheimer’s disease risk factors through large-scale health data analysis. Despite prolific publishing, no scientific awards or grants are explicitly listed in the provided texts. His advising record is also unspecified, though his research group likely engages in collaborative projects given the interdisciplinary nature of his work. He is reachable via pwm@pitt.edu at the Information Sciences Building.

Ville Kyrkiمشاهده پروفایل

استاد

• Intelligent Robotic Systems
• Computer Vision
• Robotics Manipulation
• +۷ مورد دیگر

Professor Ville Kyrki is a Full Professor at Aalto University's School of Electrical Engineering, leading the Intelligent Robotics research group. His work focuses on intelligent robotic systems, particularly addressing challenges in imperfect knowledge and uncertain sensory data. Key research areas include computer vision, tactile sensing, robotic manipulation, and machine learning applications in robotics. Education details are not explicitly provided in the text, but his academic trajectory includes a progression from Associate Professor (appointed 2012) to Full Professor at Aalto University. Research interests span feature extraction, visual servoing, sensor fusion, and planning under uncertainty. He has pioneered methods for deformable object manipulation, including data-driven grasp synthesis and imitation learning approaches for dynamic tasks like cloth folding. His work integrates reinforcement learning with domain adaptation techniques to bridge simulation-to-real gaps. Awards include the 2022 Best Paper Award and a 2024 nomination for the Best Safety, Security, and Rescue Robotics Paper. His contributions address critical challenges in robotics such as safe human-robot collaboration and autonomous decision-making in dynamic environments. Lab activities center on the Intelligent Robotics group, which develops advanced systems for industrial automation, autonomous navigation, and human-centric robotics. Current research emphasizes scalable solutions for UAV localization, multi-agent coordination, and robust policy learning in uncertain conditions.

Tomasz Kucnerمشاهده پروفایل

استادیار

• Robotics
• Autonomous Systems
• Machine Learning
• +۵ مورد دیگر

Dr. Tomasz Kucner is an Assistant Professor in the Department of Electrical Engineering and Automation at Aalto University, specializing in robotics and autonomous systems. His research focuses on enabling robots to operate safely and intelligently in human-robot shared environments through contextual learning, self-assessment, and understanding human behavior dynamics. His work emphasizes multi-modal perception, trajectory prediction, and human-aware navigation. Key projects include the ILIAD Safety Stack for industrial mobile robots and development of datasets like THÖR-MAGNI for motion capture analysis. He collaborates with institutions globally and leads the Mobile Robotics research group. Publications highlight advancements in radar-lidar fusion, pedestrian intent prediction, and generative terrain modeling. His work bridges theoretical robotics research with practical applications in autonomous driving and service robotics. Research Group: Mobile Robotics Key Focus Areas: Human-Robot Interaction, Autonomous Systems, Motion Prediction

• Intelligent Vehicles
• Traffic Technologies
• Advanced Driver Assistance Systems (ADAS)
• +۷ مورد دیگر

Iván García Daza is a Professor at the Department of Automática, University of Alcalá, Spain. He leads research in Intelligent Vehicles and Traffic Technologies (INVETT group), focusing on autonomous systems, digital twins, and ADAS technologies. His work integrates computer vision, deep learning, and human-vehicle interaction analysis. Education: PhD in Systems Engineering from University of Alcalá (2011), with a thesis on "Driver Fatigue Detection via ADAS Fusion" supervised by Rafael Barea Navarro and Luis Miguel Bergasa Pascual. Research interests include autonomous vehicle algorithms, real-time sensor fusion, virtual reality simulations for human-factor testing, and ecological conservation projects. His recent work emphasizes digital twin technology to bridge simulation-to-reality gaps in autonomous systems. Publications span autonomous driving validation frameworks, LiDAR-based navigation, and ecological studies. Notable contributions include VR-based human-vehicle interaction testing and synthetic data generation for ADAS systems. Labs/Teams: Director of the INVETT lab at the University of Alcalá, collaborating on EU-funded projects involving automotive industries and environmental agencies.

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Rodrigo Gutiérrez Moreno is an Assistant Professor in the Department of Electronics at Universidad Autónoma de Madrid (UAM), affiliated with the Robesafe research group focused on Service Robotics and e-Safety Technologies. His work bridges robotics, autonomous systems, and AI applications in transportation and healthcare. He collaborates extensively with the CARLA simulator for autonomous driving research. Research interests include autonomous vehicle decision-making, reinforcement learning integration with classical control, motion prediction, and human-robot interaction. His group explores hybrid control systems, V2X communication validation, and ethical considerations in autonomous systems. Recent projects address lane change maneuvers, urban scenario navigation, and driver behavior analysis using gaze tracking and vehicle data. Publications span multimodal sensor fusion, graph-based prediction models, and modular autonomous driving architectures. His work emphasizes transitioning simulation-based algorithms to real-world implementations while maintaining safety and reliability standards. Active in open-source tools like ROS for robotic development, he also investigates healthcare robotics and biomedical engineering applications.

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

Pablo Muñoz Martínez is a permanent professor at the Department of Automation, Universidad de Alcalá. His research focuses on autonomous systems, machine learning applications in healthcare and space weather, and robotics. He is affiliated with the Space Weather (SWE) group and the Intelligent Systems Group (ISG). He holds a PhD from Universidad de Alcalá (2016), supervised by Dr. María Dolores Rodríguez Moreno. His research interests span AI-driven teleassistance systems, autonomous controller frameworks, and path-planning algorithms for robotics. Notable projects include LARES (AI-based emergency teleassistance) and TERRA (cooperative UGV-UAV exploration). He has contributed to space weather forecasting using neural networks and developed frameworks like OGATE for autonomous system evaluation. His recent work emphasizes healthcare predictive analytics, such as emergency department patient forecasting, and energy-efficient hardware techniques. His publications reflect a blend of theoretical advancements and applied solutions in robotics, AI, and space science. Awards: None explicitly mentioned. Advising and Grants: No specific grants or advisee names listed, but his research projects imply collaborative grant-funded initiatives. His work on fall detection simulators and teleassistance systems suggests involvement in healthcare technology grants. Labs/Teams: Active in SWE (Space Weather) and ISG (Intelligent Systems Group), focusing on AI, robotics autonomy, and space environment modeling.

• Artificial Intelligence
• Robotics
• Smart Grids
• +۴ مورد دیگر

María Dolores Rodríguez Moreno is a Full Professor at the Universidad de Alcalá in the Department of Automation . She holds a PhD in Computer Science from the same institution, specializing in planning tasks with time and resource constraints. Her research focuses on Artificial Intelligence , Robotics , and Smart Grids , with notable contributions to autonomous systems, energy management, and healthcare analytics. Her work integrates machine learning, multi-agent systems, and optimization techniques to address real-world challenges like emergency department forecasting, microgrid coordination, and Mars rover navigation. She leads the Intelligent Systems Group (ISG) , advancing applications in WSNs for military operations and CAPTCHA security analysis. Key research trends include: Generative AI for healthcare resource planning Bio-inspired control strategies for sustainable energy systems Multi-agent coordination in complex environments Her publications emphasize practical solutions for energy efficiency, emergency management, and cybersecurity. Current projects include frameworks for autonomous controller assessment (OGATE) and AI-driven solutions for the Energy-Water-Food Nexus. She advises the Intelligent Systems Group and collaborates on initiatives like SOPRENE (predictive maintenance for naval assets) and LARES (AI-based teleassistance systems for elderly care).

Kathryn Daltorioمشاهده پروفایل

دانشیار

• Biologically-Inspired Robotics
• Robot Locomotion
• Soft Robotics
• +۳ مورد دیگر

Kathryn Daltorio is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Case Western Reserve University's Case School of Engineering. Her research focuses on developing biologically-inspired robots that navigate complex environments, drawing insights from animal behaviors such as earthworm locomotion and cockroach decision-making. She leads the Biologically-Inspired Robotic Lab, where her team designs robots using advanced materials like soft polymers, shape-memory alloys, and gecko-inspired adhesives. Education: PhD in Mechanical & Aerospace Engineering (Case Western Reserve University, 2013) MS in Mechanical & Aerospace Engineering (Case Western Reserve University, 2007) BS in Mechanical & Aerospace Engineering (Case Western Reserve University, 2005) Research Interests: Robotics inspired by animal locomotion (e.g., worms, crabs, insects) Neurobiologically-inspired control systems Soft robotics and compliant materials Adaptive legged systems for amphibious environments Publications highlight advancements in peristaltic robots, heteroclinic channel-based control, and crab-inspired leg designs. Recent work emphasizes energy-efficient traversal in unstructured terrain and multimodal locomotion enabled by metamaterials. Awards include the NSF CAREER Award (2021) and ONR YIP Award (2019). She is a member of ASME and IEEE, contributing to both academic and industry-oriented robotics projects. Labs/Teams: Biologically-Inspired Robotic Lab (BIRL) focuses on prototyping robots that integrate biological principles with cutting-edge engineering to solve real-world challenges in search-and-rescue, environmental monitoring, and disaster response.

Silvia Ferrariمشاهده پروفایل

استاد

• Robotics and Autonomy
• Artificial Intelligence
• Neuroscience
• +۵ مورد دیگر

Silvia Ferrari is the John Brancaccio Professor of Mechanical and Aerospace Engineering at Cornell University, leading the Laboratory for Intelligent Systems and Controls (LISC) in Ithaca and co-directing the Cornell-Unibo Věho Institute on Vehicle Intelligence at Cornell Tech. She holds academic affiliations with both Cornell Engineering and Cornell Tech, focusing on interdisciplinary research in autonomy, robotics, and intelligent systems. Education: B.S., Aerospace Engineering, Embry-Riddle Aeronautical University (1997) M.A. and Ph.D., Mechanical and Aerospace Engineering, Princeton University (1999–2002) Research Interests: Her work spans computational intelligence, sensorimotor learning, adaptive control, and robotic autonomy. Key areas include active perception, neural network modeling, distributed sensor networks, and decision-making under uncertainty. She has pioneered methods in information-driven path planning and reinforcement learning, with applications to robotics, neuroscience, and environmental monitoring. Awards: Presidential Early Career Award for Scientists and Engineers (2006) NSF CAREER Award (2005) ONR Young Investigator Award (2004) Fellow of ASME and AIAA (2020) Labs & Leadership: As Director of LISC and co-Director of the Věho Institute, she fosters cross-campus collaborations in autonomy and urban technology. Her recent initiatives include organizing the Autonomy and Mobility Workshop, advancing Cornell’s ‘One Cornell’ research strategy.

• Artificial Intelligence
• Computer Vision
• Multimodal Learning
• +۳ مورد دیگر

Juan Carlos Niebles is an Adjunct Professor in the Department of Computer Science at Stanford University, School of Engineering. His research focuses on artificial intelligence, computer vision, and multimodal learning with applications in robotics, healthcare, and human activity analysis. He leads projects on video-language models, action recognition, and privacy-preserving optics for human pose estimation. Recently, his work has explored diffusion transformers, cooperative-contrastive learning, and systems for large action models. Education: Ph.D. in Computer Science (Stanford University, 2010) His research interests include designing AI systems that understand complex spatiotemporal data, with breakthroughs in video question answering, multimodal alignment, and agent-based learning. He has pioneered frameworks like ActionStudio and Apigen-MT, advancing data generation for autonomous agents. Publications span foundational topics like causal layering, Parkinson’s disease motor severity estimation from video, and tools like the AI Index Report. His work bridges theory and practice, addressing challenges in model robustness, scalability, and real-world deployment. No scientific awards explicitly mentioned in text.

Daniel Yaminsمشاهده پروفایل

دانشیار

• Neuroscience
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Associate Professor Daniel Yamins holds dual appointments in Stanford University's Department of Psychology and Department of Computer Science. His research focuses on the intersection of neuroscience and artificial intelligence, particularly in understanding how biological and artificial systems process visual information and learn physical dynamics. He leads the NeuroAILab, which develops computational models to explain neural processes in perception, cognition, and development. Yamins earned his Ph.D. in Applied Mathematics from Harvard University in 2008. His work integrates machine learning, cognitive science, and neuroimaging to bridge gaps between biological and artificial intelligence. Key projects include the BabyView dataset, which captures infants' visual experiences, and Physion++, a benchmark for evaluating physical scene understanding in humans and machines. His research interests span object perception, counterfactual reasoning, 3D scene understanding, and developmental robotics. Recent work emphasizes self-supervised learning, world modeling, and the design of image-computable neural network architectures that mirror cortical processing. Yamins has contributed to open-source platforms like ThreedWorld for physically realistic simulation and Hyperopt for hyperparameter optimization. His grants and collaborations include NSF-funded research on neural mechanisms of physical scene understanding and NIH-backed studies on neurodevelopmental processes. He actively bridges theoretical neuroscience with practical AI applications, advocating for benchmark-driven model evaluation aligned with biological plausibility.