جستجوی استاد راهنمای دانشگاهی

• Robotics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Prof. Dr. Berthold Bäuml serves as a full Professor at the Technical University of Munich (TUM) within the TUM School of Computation, Information and Technology, holding the Chair for Learning AI for Dextrous Robots. His research bridges artificial intelligence and robotics to develop advanced manipulation capabilities in humanoid systems, with particular emphasis on tactile sensing and learning-based control architectures. His core research interests span Robotics, Artificial Intelligence, Machine Learning, Dextrous Manipulation, Tactile Sensing, and Reinforcement Learning. Bäuml investigates how robots can autonomously learn complex manipulation skills through tactile feedback, focusing on in-hand manipulation, grasping of unknown objects, and sim-to-real transfer for multi-fingered hands. His work integrates state estimation with reinforcement learning to achieve robust performance in unstructured environments. Analysis of his 2022-2025 publications reveals a concentrated research trajectory in tactile dexterous manipulation, characterized by modular reinforcement learning architectures, shape-conditioned agents, and torque prediction systems. Key trends include the development of time-optimal controllers, self-contained calibration methods, and collision-aware motion planning, all leveraging deep learning for real-time performance in humanoid platforms like Agile Justin.

Andrzej Wasowskiمشاهده پروفایل

استاد

• Software Engineering
• Software Product Lines
• Model Driven Development
• +۵ مورد دیگر

Andrzej Wasowski is a Professor and research group leader at the IT University of Copenhagen , specializing in Software Engineering and Software Quality Research (SQUARE) . He leads projects involving underwater robotics , AI verification , and probabilistic programming , with a focus on model-driven development and variability in systems . Key Collaborations: Innovation Fund Denmark, European Commission, Danmarks Frie Forskningsfond Research Trends include underwater image segmentation , safety analysis of autonomous vehicles , and symbolic methods in probabilistic programs . His work integrates formal methods with real-world applications in marine technology. Projects like VARIETE and DIREC explore dependable systems and verifiable AI . He has contributed datasets to ZENODO and participated in workshops on domain-specific language design and software testing .

Seth Hutchinsonمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Control Theory
• +۵ مورد دیگر

Seth Hutchinson is a distinguished roboticist currently serving as Professor and KUKA Chair for Robotics in the School of Interactive Computing at the Georgia Institute of Technology (2018-2024), where he also served as Executive Director of the Institute for Robotics and Intelligent Machines starting in 2019. He is additionally an Emeritus Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. Beginning January 2025, he will be joining Northeastern University. His academic journey spans multiple prestigious institutions with a focus on robotics, control theory, and computer vision. Dr. Hutchinson's research interests span a broad range of topics in robotics, including: Robotics and Autonomous Systems Computer Vision and Visual Servoing Control Theory and Motion Planning Machine Learning and Reinforcement Learning Applications Human-Robot Interaction Multi-Robot Systems His recent publications demonstrate a strong focus on cutting-edge robotics research, with particular emphasis on reinforcement learning for locomotion, safety-aware planning, and vision-based navigation. His work bridges theoretical foundations with practical applications, spanning from quadrupedal locomotion to autonomous surface vehicles and human-robot interaction. The publications reveal a consistent thread of addressing fundamental challenges in robotics while pushing the boundaries of what's possible in areas like sim-to-real transfer, emotion-aware navigation, and architectural-scale robotic art. Dr. Hutchinson has held significant leadership positions, including serving as Executive Director of the Institute for Robotics and Intelligent Machines at Georgia Tech. His academic contributions extend beyond research to include educational leadership and professional service within the robotics community. His academic journey includes a sabbatical period in Rome during Fall 2013, where he engaged with La Sapienza (the University of Roma) while continuing his robotics research. This international experience demonstrates his commitment to global collaboration in advancing robotics science.

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

Pan Xu is a tenure-track Assistant Professor at Duke University with joint appointments in the Department of Biostatistics & Bioinformatics , Department of Computer Science , and Department of Electrical & Computer Engineering . He earned his Ph.D. in Computer Science from the University of California, Los Angeles (2021) and completed postdoctoral training at the California Institute of Technology (2021-2022) . Research Focus: Machine Learning, Reinforcement Learning, Optimization, and High-Dimensional Statistics with applications in Bioinformatics and Healthcare. Key Contributions: Developing algorithms for robust sequential decision-making under uncertainty, improving Thompson Sampling efficiency, and advancing multi-agent reinforcement learning. Scientific Recognition: NSF award for exploration in decision-making (2023) Whitehead Scholar award (2023) PIMCO Postdoctoral Fellowship (2022) Best Paper Award at ACM FAccT (2023) Teaching: Offers graduate courses in machine learning and decision-making frameworks. Service: Serves as Area Chair for ICML, NeurIPS, and AAAI; Action Editor for TMLR.

Hazem Torfahمشاهده پروفایل

استادیار

• cyber-physical systems
• formal verification
• runtime monitoring
• +۵ مورد دیگر

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.

Abhishek Guptaمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Robotics
• +۷ مورد دیگر

Abhishek Gupta is an Assistant Professor in the Department of Computer Science and Engineering at the Paul G. Allen School of Computer Science & Engineering, University of Washington, where he leads the Washington Embodied Intelligence and Robotics Development (WEIRD) Lab. His research focuses on developing algorithms that enable robotic systems to learn complex tasks in unstructured environments like offices and homes. Dr. Gupta completed his Ph.D. in Machine Learning and Robotics at Berkeley Artificial Intelligence Research (BAIR) at UC Berkeley, advised by Professors Sergey Levine and Pieter Abbeel. Prior to his faculty position, he was a post-doctoral scholar at MIT, collaborating with Russ Tedrake and Pulkit Agarwal. He also earned his bachelor's degree from UC Berkeley. His research interests span computer vision, machine learning, and robotics with particular focus on deep reinforcement learning algorithms that can learn in real-world environments with and around humans. His recent work explores deployment time reinforcement learning, foundation models from off-domain data, fast real-world adaptation, human-in-the-loop interaction, and real-to-sim-to-real policy learning methods. His publications reveal a strong emphasis on practical robotic applications, especially in dexterous manipulation, assistive robotics, and embodied intelligence. Dr. Gupta has received several prestigious recognitions including a Best Paper award at LLARLA workshop at ICML 2018, a RSS 2023 Best Systems Paper Finalist, and multiple spotlight/oral presentations at top conferences including NeurIPS, ICLR, and CoRL. As a principal investigator, Dr. Gupta leads the WEIRD Lab which focuses on developing embodied intelligence for robotics. He actively collaborates with major institutions including MIT, UC Berkeley, and industry partners. His research is funded by grants supporting fundamental advances in robotics and AI, with applications ranging from assistive technologies to industrial automation. Dr. Gupta is currently recruiting highly motivated Ph.D. students and postdoctoral researchers to join his lab.

Lorenzo Rosascoمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Robotics
• +۵ مورد دیگر

Lorenzo Rosasco is a Full Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) at the University of Genoa. He holds roles including University contact for the thematic area "Artificial Intelligence" and membership on the Department Board. His research focuses on machine learning theory, kernel methods, optimization, robotics, and their applications in control systems, domain adaptation, and interdisciplinary fields like biomedical engineering. His educational background includes advanced expertise in mathematical foundations of machine learning, though specific academic credentials are not detailed here. Teaching responsibilities include courses such as MACHINE LEARNING, SEQUENTIAL PREDICTION AND REINFORCEMENT LEARNING, and INFORMATION THEORY AND INFERENCE at both undergraduate and graduate levels. Research interests revolve around developing efficient algorithms for large-scale machine learning, including iterative regularization, transfer learning, and control systems using Koopman operator theory. Notable contributions include work on fast kernel methods, optimization in neural networks, and applications in robotics for tasks like humanoid locomotion and tactile sensing. Recent publications explore topics such as double descent phenomena, sim-to-real transfer learning, and adaptive control strategies. His work bridges theoretical insights with practical applications, emphasizing computational efficiency and interdisciplinary impact.

Assane Gueyeمشاهده پروفایل

دانشگاهی

• Computer Networks
• Cybersecurity
• Telecommunication Systems
• +۳ مورد دیگر

Assane Gueye is an Assistant Teaching Professor at Carnegie Mellon University Africa (CMU Africa) in the Department of Electrical and Computer Engineering, part of the College of Engineering. He previously held a faculty position at the ICT Department of the University Alioune Diop of Bambey, Senegal, where he leads the TIC4Dev research group. He also holds a guest researcher role at the National Institute of Standards and Technology (NIST) in the USA. Gueye earned his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2011) and a Master’s in Communication Systems Engineering from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2004). His research focuses on two core areas: 1) performance evaluation and security of large-scale communication systems, and 2) ICT for Development (ICT4D), particularly in African contexts. His work includes projects on privacy-preserving mobile money systems, rural connectivity solutions, federated learning for healthcare, and software security vulnerability analysis. He has contributed to initiatives such as the MIRA project measuring internet resilience in Africa and the design of low-cost infrastructure for digital public goods. Key awards include being named a Next Einstein Forum Fellow (2016) and an inaugural EAI Fellow (2019). His articles reflect a focus on interdisciplinary solutions for underserved regions, with recent work emphasizing cybersecurity in financial systems, healthcare innovation via federated learning, and infrastructure democratization in space-based communications. He actively collaborates with global institutions to address challenges in technology access and security in developing economies. Awards: Next Einstein Forum Fellow, EAI Fellow Key Projects: HumekaFL (neonatal asphyxia detection), F4PW (fog computing for maternal health), MIRA (internet resilience in Africa)

Wenshuai Zhaoمشاهده پروفایل

پژوهشگر

• Multi-Agent Reinforcement Learning
• Humanoid Robotics
• Reinforcement Learning
• +۴ مورد دیگر

Wenshuai Zhao is a Doctoral Student in the School of Electrical Engineering and a Postdoctoral Researcher in the Department of Computer Science. His research focuses on Multi-Agent Reinforcement Learning, Humanoid Robotics, and Temporal Consistency in Deep Learning. Master of Engineering and Technology, Fudan University (2020) Master of Natural Sciences, University of Turku (2020) His work spans dynamic models, sample efficiency, and policy optimization. Recent publications include AgentMixer (2025) for policy factorization, Bi-Level Motion Imitation (2025) for humanoid robots, and Optimistic Policy Gradient (2024) for multi-agent coordination. Collaborations include the EU's Recovery and Resilience Facility and Business Finland projects.

Niko Suenderhaufمشاهده پروفایل

استاد

• Robotic Vision
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Professor Niko Suenderhauf is a leading researcher in robotic vision and AI at Queensland University of Technology (QUT). He focuses on enabling robots to understand and interact with their environment through advancements in SLAM, scene understanding, and semantic mapping. His work bridges robotics, computer vision, and machine learning, aiming to create reliable, relatable, and sustainable robotic systems that collaborate with humans to address societal challenges like climate change and aging populations. His key achievements include pioneering graph-based SLAM methods integrated into the ROS ecosystem, developing object-based semantic SLAM systems, and demonstrating the efficacy of deep learning for place recognition. Collaborations with institutions like DeepMind and Amazon reflect his interdisciplinary approach. Awards include recognition for groundbreaking SLAM research and highly cited publications. Research interests emphasize open-world navigation, privacy-preserving vision, and embodied AI, with a focus on practical applications in construction, environmental monitoring, and domestic robotics. His work on uncertainty quantification and robust perception ensures systems operate safely in unpredictable environments. Professor Suenderhauf actively engages in education, mentoring students across undergraduate and postgraduate levels, and emphasizes the societal impact of technology through collaborations with neuroscience, law, and design experts.

Miguel Serras Vascoمشاهده پروفایل

پژوهشگر

• Multimodal Perception
• Reinforcement Learning
• Representation Learning
• +۴ مورد دیگر

Miguel Serras Vasco is a postdoctoral researcher at KTH Royal Institute of Technology in Stockholm, Sweden, affiliated with the Robotics, Perception and Learning (RPL) division. He holds a PhD from Instituto Superior Técnico, University of Lisbon (2023), where his work earned the Best PhD Thesis in AI in Portugal award. His research focuses on multimodal perception, reinforcement learning, and aligning artificial agents with human perception. He previously worked as an RSS Pioneer and research intern at Sony AI. Education: PhD in Artificial Intelligence, Instituto Superior Técnico, University of Lisbon (2023) Research Interests: Vasco’s work bridges robotics, neuroscience, and AI, emphasizing embodied agents that co-exist with humans. He explores representation learning, human-aligned image models, and sample-efficient reinforcement learning. His recent projects include super-human autonomous racing agents and olfactory perception modeling with transformers. Key Contributions: Vasco co-developed the GT Sophy racing agent, achieved outstanding results in visual decoding from brain activity, and proposed methods like FLoRA for preference-based RL. His work on NeuralSolver advances algorithm extrapolation in reinforcement learning. Awards: Best PhD Thesis in AI in Portugal (APPIA, 2023) Outstanding Paper Award at RLC 2024 (for autonomous racing research) Grants/Advising: Vasco advises students on multimodal and reinforcement learning topics. He actively organizes conferences like the Reinforcement Learning and Video Games Workshop (RLVG) at RLC 2025 and collaborates with institutions like INESC-ID (Lisbon). Labs/Teams: Associated with the Collaborative Autonomous Systems unit at KTH and previously with the GAIPS Lab (INESC-ID, Lisbon).

• Machine Learning
• Biologically Inspired Computing
• Hyper-heuristics
• +۴ مورد دیگر

Dr. Kevin Sim is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University, with research interests spanning machine learning, biologically inspired computing, hyper-heuristics, and combinatorial optimization. His work focuses on developing advanced algorithm selection techniques and optimization methodologies applicable to real-world domains such as logistics, forestry, and infrastructure engineering. PhD in Hyper-heuristics for Optimization (Edinburgh Napier University, 2014) MSc in Advanced Software Engineering (Edinburgh Napier University, 2010) BSc in Software Technology (Edinburgh Napier University, 2009) His research explores: Feature-free algorithm selection models using recurrent neural networks Evolutionary approaches to instance-space layout optimization Hybrid feature construction methods for environmental risk prediction Ensemble hyper-heuristics for scheduling and packing problems Integration of large language models with evolutionary algorithms Scientific awards include: Bronze Award, International Humies Competition (2018) for wind damage prediction research Recent publications demonstrate expertise in: Neural algorithm selection LLM-evolved heuristics Wind damage modeling Instance-space visualization Feature engineering Real-time optimization Kevin supervises research students in algorithm design and optimization, and has secured funding from the Data Lab (£19,599) and EPSRC (£238,068) for projects related to infrastructure optimization and lifelong learning systems.

Eric Medvetمشاهده پروفایل

دانشیار

• Evolutionary Computation
• Machine Learning
• Grammatical Evolution
• +۳ مورد دیگر

Eric Medvet is an Associate Professor of Computer Engineering at the Department of Engineering and Architecture (DIA), University of Trieste, Italy. He leads the Evolutionary Robotics and Artificial Life Lab and co-leads the Machine Learning Lab. His research focuses on Evolutionary Computation, Machine Learning, and their applications to robotics, including Grammatical Evolution, Genetic Programming, and soft robotics. He received the Google Faculty Research Award 2019 for a project on modular soft robots and co-authored a best paper at EuroGP 2024. His recent work emphasizes interpretable control policies, quality-diversity optimization, and evolutionary learning of formal specifications. Medvet teaches advanced courses on machine learning, evolutionary robotics, and programming, consistently updating curricula to reflect cutting-edge research. His labs are hubs for interdisciplinary projects, blending theory with practical implementations in robotics and AI. Research Interests: Medvet’s research spans evolutionary algorithms applied to robotics, with a focus on modular systems. He explores how genetic programming and grammatical evolution can optimize robotic designs and controllers. His work on ‘totipotent neural controllers’ and ‘body-brain co-evolution’ exemplifies efforts to create adaptable, specialized robots. He also investigates formal methods (e.g., STL specifications) for system validation and interpretable AI models. Recent trends in his publications reflect a shift toward practical applications, such as wearable movement analysis and sim-to-real transfer in robotics. Awards: Google Faculty Research Award 2019, EuroGP 2024 Best Paper Award Labs: Evolutionary Robotics and Artificial Life Lab, Machine Learning Lab Teaching: Courses include Advanced Programming, Introduction to Machine Learning, and Evolutionary Robotics, with a focus on Java and practical software development. Medvet’s approach integrates theoretical rigor with hands-on experimentation, emphasizing open-source tools like JGEA and 2D-VSR-Sim. His work bridges computational intelligence and real-world robotics challenges, driving innovation in both fields.

• Autonomous Navigation for Robots
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jesús Tordesillas Torres is an Assistant Professor in the Department of Electronics, Automation, and Communications at the School of Engineering, Comillas Pontifical University. He joined the institution in June 2024, bringing extensive experience from postdoctoral research at MIT and ETH Zurich, and prior academic training from MIT and the Polytechnic University of Madrid. Education: PhD in Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), 2022 MS in Aeronautics and Astronautics, MIT, 2019 MS in Industrial Engineering, Polytechnic University of Madrid, 2019 BS in Industrial Engineering, Polytechnic University of Madrid, 2016 His research focuses on robotics, particularly autonomous navigation, trajectory planning, and optimization under uncertainty. He integrates deep learning and control theory to develop systems capable of safe, fast, and perception-aware navigation in dynamic and unknown environments. His work spans aerial and ground robots, multiagent systems, and challenging terrains, with strong emphasis on real-world deployment and robustness. The recent publications highlight a consistent trend in trajectory optimization, perception-aware planning, and multiagent coordination. His work bridges theoretical advances in optimization and learning with practical robotic applications, especially in safety-critical and communication-constrained scenarios. Scientific Awards: Best Paper Award, IEEE ICRA 2023 1st Place, Urban Circuit, DARPA Subterranean Challenge (2020) 2nd Place, Tunnel Circuit, DARPA Subterranean Challenge (2021) Finalist, Best Paper, IEEE IROS 2019 Jesús Tordesillas Torres has been actively involved in research grants and projects, including the ADS FERRARI WP-2 Project funded by Airbus Defence and Space (2025–2025). He mentors students and collaborates with leading institutions such as MIT, ETH Zurich, and the University of Pennsylvania. He also serves as a reviewer for top-tier journals including IEEE Transactions on Robotics , International Journal of Robotics Research , and IEEE Robotics and Automation Letters , as well as major conferences like ICRA and IROS. He leads and participates in research teams focused on autonomous systems, with affiliations to the Institute for Research in Technology (IIT) at Comillas. His invited talks at ETH Robotics Summer School and University of Pennsylvania reflect his growing influence in the robotics community.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۳ مورد دیگر

Dr. Robert Haschke serves as a Professor and Responsible Investigator in the Cognitive Systems and Social Interaction Group at Bielefeld University's Faculty of Engineering. He is affiliated with the Center for Cognitive Interaction Technology (CITEC) and serves on the Examination Board for the Intelligent Interactive Systems Master's program. His office is located at CITEC 2-035, and he can be reached at rhaschke@techfak.uni-bielefeld.de or +49 521 106-12122. Professor Haschke's research spans multiple domains of robotics and artificial intelligence, with particular emphasis on tactile sensing systems, robotic manipulation, and human-robot interaction. His work explores advanced methods for enabling robots to perceive their environment through touch, with applications in assistive robotics and industrial automation. He investigates how machines can learn from human interactions and adapt their behavior through reinforcement learning and sensor fusion techniques. His research bridges theoretical advances with practical implementations, focusing on transferring knowledge from simulation to real-world robotic systems. Analysis of Professor Haschke's recent publications reveals a strong trajectory toward more sophisticated tactile perception systems and their integration with language and vision for natural human-robot collaboration. His work consistently addresses the simulation-to-reality gap, developing methods for transferring control policies from virtual environments to physical robots. The research shows increasing integration of multimodal sensing (tactile, visual, linguistic) to enable more capable and adaptable robotic manipulation in unstructured environments. As an educator, Professor Haschke teaches advanced courses including Robot Manipulators (39-Inf-RM), Advanced Artificial Intelligence (39-M-Inf-AI-adv_a), Advanced Artificial Intelligence (focus) (39-M-Inf-AI-adv-foc), and Basics of Artificial Intelligence (39-M-Inf-AI-bas). His teaching reflects his research expertise, providing students with both theoretical foundations and practical skills in robotics and AI. Professor Haschke is an integral member of Bielefeld University's Cognitive Systems and Social Interaction Group within CITEC. His work contributes significantly to the university's Socio-Technical World research area, particularly in developing capabilities that enable agents (humans, robots, and AI systems) to act, communicate, and learn in complex environments. His research group focuses on creating robotic systems that can interact naturally with humans through advanced perception and adaptive control mechanisms.