Find Academic Supervisors

Pulkit AgrawalView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Pulkit Agrawal is an Associate Professor at the Massachusetts Institute of Technology (MIT), holding dual appointments in the departments of Artificial Intelligence and Decision-making (AI+D), Computer Science (CS), and Electrical Engineering (EE) within the School of Engineering. He is a core member of the Embodied Intelligence Community of Research, focusing on advancing intelligent systems that interact with the physical world through robotics, AI, and machine learning. His work emphasizes bridging simulation and real-world applications in robotics, human-robot interaction, and medical technology. Key research areas include reinforcement learning for robotic manipulation, simulation-to-real transfer, and embodied intelligence. Notable projects include developing methods for robust home robot training using real-to-sim-to-real approaches, tactile-driven object retrieval systems, and AI-driven glucose control models for post-surgical care. He also explores ethical AI in healthcare and assistive technologies for cognitive impairment monitoring. Dr. Agrawal’s contributions span robotics hardware design (e.g., DEXOS hand exoskeleton), neuro-inspired algorithms (e.g., grid cell-based map building), and scalable robot learning via crowdsourced environments. His work often integrates interdisciplinary methods from control theory, computer vision, and biomedical engineering. He leads the MIT CSAIL Embodied Intelligence group and collaborates on projects like OpenEQA for embodied question answering and DexHub for large-scale robot data collection. His research has been recognized through promotions within EECS and featured in outlets like MIT News for innovations in home robotics and AI-driven medical solutions. He advises on grants related to intelligent systems and maintains active collaborations across academia and industry.

Rika AntonovaView profile

Associate Professor

• Robotics
• Reinforcement Learning
• Machine Learning
• +7 more

Dr. Rika Antonova is an Associate Professor at the Department of Computer Science and Technology, University of Cambridge , UK. Her research focuses on robot learning , data-efficient reinforcement learning , and simulation-to-reality transfer . She offers fully funded PhD positions in robot learning, novel robot hardware design, and reinforcement learning (RL) at Cambridge, where she also teaches an RL course. Educational Background: PhD in Robotics from KTH Royal Institute of Technology, Sweden (advisor: Danica Kragic). MSc in Robotics from Carnegie Mellon University (advisor: Emma Brunskill). Research Interests: Antonova's work bridges Bayesian optimization , deformable object manipulation , and multimodal sensor design . Her recent projects include equivariant diffusion policies and GPU-accelerated control frameworks . She has contributed to data-efficient RL algorithms and topological representations for deformable objects . Recent Publications: Antonova's 2024 articles on EquiBot (CoRL) and Runtime Monitoring (CoRL) highlight her focus on generalizable policies and safe AI. Earlier works include DiffCloud (IROS 2022) for real-to-sim rendering and Bayesian Optimization in Variational Latent Spaces (CoRL 2020). Scientific Awards: NSF/CRA Computing Innovation Fellowship (postdoctoral). Research Community Involvement: Antonova serves on the Board of Directors of the Robot Learning Foundation and has been an Associate Editor for ICRA (2024), Chair for CoRL Demos (2024), and reviewer for JMLR, Nature Machine Intelligence, and top robotics conferences.

• Nanotechnology
• Biomedical Engineering
• Drug Delivery Systems
• +1 more

Professor Ken Tye Yong is Associate Dean (External Engagement) for the Faculty of Engineering and Professor in the School of Biomedical Engineering at the University of Sydney. His research spans nanotechnology, biomedical imaging, drug delivery systems, and biophotonics, with emphasis on nanoparticle-based therapies and diagnostic platforms. He leads initiatives in external engagement through the Warren Centre and oversees research on nanomaterial applications in healthcare. Current projects include neuromorphic imaging cytometry, SERS-based cellular monitoring, and biomimetic tissue scaffolds. Publications demonstrate consistent focus on interdisciplinary approaches combining nanomaterials engineering with clinical applications. Research outputs frequently involve microfluidic systems, targeted drug delivery, and advanced imaging techniques. Recent work emphasizes neuroprotective therapies, wearable biosensors, and mRNA vaccine delivery platforms.

Rebecca FriesenView profile

Assistant Professor

• Surface Haptic Displays
• Haptic Wearables
• Tactile Perception
• +2 more

Dr. Rebecca Friesen is Assistant Professor of Mechanical Engineering at Texas A&M, leading research on haptic interfaces for enhanced virtual interactions. Her lab develops surface haptic displays, wearable tactile devices, and refreshable braille systems that replicate complex tactile sensations. Current projects investigate: 1) Friction-modulating touchscreens for realistic texture rendering, 2) Wearable vibrotactile displays for virtual reality, 3) Refreshable pin arrays for tactile graphics, and 4) Medical palpation simulation using haptic feedback. Research combines psychophysical studies with engineering design to create efficient tactile displays leveraging perceptual phenomena.

Ville KyrkiView profile

Professor

• Intelligent Robotic Systems
• Computer Vision
• Robotics Manipulation
• +7 more

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.

• Physics Education
• Didactics of Physics
• Science Teaching and Learning
• +7 more

Bianca Watzka is a Professor of Physics and Technology Education at RWTH Aachen University, affiliated with the Department of Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. She conducts research and teaching in the didactics of physics, focusing on innovative pedagogical methods and technology integration. Her research interests include: Physics Education and Didactics Context-Based and Phenomenon-Based Learning Cognitive Load Theory in Instruction Educational Technology and Multimedia Learning Teacher Education and Diagnostic Competence Use of Sensors and Digital Tools (Arduino, H5P) in Science Classrooms Eye-Tracking in Learning Environments Her recent publications (2023–2025) reflect a strong focus on enhancing physics instruction through multimedia cues, cognitive load optimization, interactive tasks, context-rich problems, and teacher training. She investigates how auditory and visual supports, VR annotations, force diagrams, and real-world contexts like wind and thermodynamics improve learning outcomes. Her work increasingly integrates technology for formative assessment and reflective teaching. Scientific contributions include: Development of context-oriented physics modules Application of eye-tracking in educational diagnostics Promotion of inquiry-based methods like the Mystery method Integration of Cornell Notes and H5P in teacher education Use of authentic contexts such as CO2 sensors, LEDs, and bionics in teaching She advises pre-service physics teachers and contributes to improving teaching quality through student feedback and structured learning tools. Her work bridges theoretical research with practical classroom applications, particularly in secondary and higher education settings. While no formal lab or team is mentioned, her research implies collaboration in educational technology and teacher training projects.

Alexander AchbergerView profile

Researcher

• Human-Computer Interaction
• Virtual Reality
• Haptics
• +3 more

Dr. Alexander Achberger is a Research Associate at the Visualization Institute of the University of Stuttgart (VISUS) , affiliated with the Sedlmair Working Group . His research focuses on Human-Computer Interaction , Virtual Reality , and Haptics , particularly in Automotive Engineering contexts. He has published extensively on haptic feedback devices and immersive visualization tools since 2018. Current position: Research Associate at VISUS Affiliation: Sedlmair Working Group Location: Allmandring 19, Stuttgart, Germany (Room: 01.031) His work explores force feedback systems like PropellerHand , STROE , and STRIVE for automotive VR applications. He also investigates sitting posture recognition and motion guidance memorability in extended reality environments. Recent publications examine multi-type haptic feedback and upper limb representations in VR.

Timo Klein-SoetebierView profile

Researcher

• Table Tennis
• Motor Control
• Sports Cognition
• +5 more

Timo Klein-Soetebier is a researcher at the Institute of Exercise Training and Sport Informatics at the German Sport University Cologne, specializing in Cognition in Team Sports. With 66 publications and 11 research projects to his name, he focuses primarily on table tennis, motor control, and perception training in sports contexts. His research interests span across several key areas including table tennis technique analysis, motor learning, sports cognition, and perception training. Klein-Soetebier has made significant contributions to understanding gender differences in table tennis techniques, perception training methods, and the application of table tennis in therapeutic contexts such as Parkinson's disease rehabilitation. The 15 most recent publications reveal a consistent focus on practical applications of sports science, particularly in table tennis pedagogy, sports informatics, and motor learning principles. His work bridges theoretical sports science with practical applications in training, education, and health contexts, demonstrating a strong commitment to translating research into real-world impact. Klein-Soetebier actively collaborates with colleagues across multiple departments at the German Sport University Cologne, particularly with researchers in sports informatics, cognition, and sports games research. His work has generated significant media attention, particularly regarding the therapeutic applications of table tennis for Parkinson's disease patients. His current research projects include investigations into learning curves in table tennis skill acquisition, XR-based training methods in golf, and perception training in table tennis, demonstrating both depth in his primary area of expertise and breadth across different sports contexts. He maintains an active presence in both academic and public spheres, with 8 media appearances discussing the benefits of table tennis for health and performance, particularly highlighting its applications for neurological rehabilitation.

Xinli DuView profile

Associate Professor

• Robotic Surgery
• Biomedical Engineering
• Control Systems
• +4 more

Dr. Xinli Du is a Reader (equivalent to Associate Professor) in Robotics & Autonomous Systems at Brunel University London, within the Department of Mechanical and Aerospace Engineering in the College of Engineering, Design and Physical Sciences. He joined the Brunel Institute for Bioengineering as a Research Lecturer in August 2010, bringing expertise from his PhD at Loughborough University (2008) and a Research Associate position at Aston University. His educational background includes a PhD in Control Systems from Loughborough University (2008). Prior to Brunel, he worked as a Research Associate at Aston University. Dr. Du's research centers on biomedical engineering with a focus on robotic surgery, particularly for cochlear implantation and prosthetics. He has developed innovative smart surgical tools including a robotic micro-drill for cochleostomy and tactile sensing systems for stroke rehabilitation. His work integrates advanced control systems, robotics, and multi-sensor technologies to enhance surgical precision and patient rehabilitation outcomes. Analysis of his recent publications (2021-2025) shows a dominant focus on robotic cochlear implant surgery, featuring real-time impedance-based electrode array tracking, machine learning for trajectory correction, and 6-DOF parallel manipulators for atraumatic insertion. Significant parallel work in prosthetics utilizes 3D motion capture and smartphone photogrammetry for socket fitting assessment. Additional contributions span PEM fuel cell control and optical shape sensing for continuum robotics in minimally invasive surgery. Dr. Du has not been documented as receiving specific scientific awards in the provided materials. He supervises PhD students in Robotic Surgery and Control System Design. His research is supported by major grants from The Royal National Institute for Deaf People (2024-2027), The Royal Society (2024-2026), EPSRC (2023-2025), Innovate UK (2021-2023), and magAssist inc. (2020-2023). Current projects include innovative cochlear implant placement techniques, smart myocardial infarction detection devices, and hybrid mock circulatory systems for cardiac research. Dr. Du is an active member of the Brunel Institute for Bioengineering (BIB) and the Institute of Energy Futures and Health Sciences (IEHS). He collaborates extensively with researchers including Dr. Nikolaos Boulgouris, Dr. Amir Mohagheghi, and Dr. Yohan Noh on surgical robotics and biomedical sensing projects, contributing to Brunel's leadership in medical technology innovation.

• Drone Control Systems
• Reinforcement Learning
• Digital Twin Technology
• +3 more

Robert McMurray is a Lecturer at the School of Engineering, Ulster University, with active research contributions in drone control systems, reinforcement learning, and engineering education. He is based at the Belfast campus and serves as a key member of the Engineering Research group. Recent research focuses on applying digital twin technology and AI-driven control strategies to unmanned aerial vehicles (UAVs), particularly the MADNI drone platform. His work explores hybrid control systems for adverse conditions, obstacle avoidance algorithms, and tactile force prediction models using deep learning. Key Research Areas: Drone autonomy, materials testing, and pedagogical best practices 2024-2025 Trends: Integration of reinforcement learning with fault detection in UAVs, digital twin simulations Contact: rj.mcmurray@ulster.ac.uk

• Robotics
• Artificial Intelligence
• Machine Learning
• +4 more

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.

Hyock Ju KwonView profile

Professor

• Artificial Intelligence
• Machine Learning
• Manufacturing Engineering
• +5 more

Hyock Ju Kwon is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo . Active in research and teaching, he focuses on integrating Artificial Intelligence (AI) with manufacturing practices and biomedical ultrasound techniques . Doctorate in Mechanical Engineering, University of Alberta (2007) Master's in Mechanical Engineering, University of Alberta (2003) Master's in Mechanical Engineering, KAIST (1988) Bachelor's in Mechanical and Design Engineering, Seoul National University (1985) His research spans AI and machine learning , with applications in manufacturing (robotic assembly, NDT, predictive maintenance) and biomedical fields (therapeutic ultrasound, tactile neuroimaging). He leads the AI for Manufacturing Laboratory , emphasizing digital twin and multi-physics modeling . The 15 most recent articles reflect trends in AI-driven manufacturing , ultrasound diagnostic/therapeutic systems , and robotic path planning . Keywords include machine learning , medical imaging , and fluid dynamics , with subfields like predictive maintenance , nanoparticle-enhanced therapy , and topological navigation . As a Sole-Supervisory Privilege Status (SSPS) holder, he supervises graduate students. Teaching includes courses like ME 212 - Dynamics and ME 526 - Fatigue and Fracture Analysis . The AI for Manufacturing Laboratory develops cutting-edge technologies for industrial AI , collaborating with Korean municipal governments on large-scale projects. Areas include robotic assembly , non-destructive testing , and machine condition monitoring .

• Friction
• Adhesion
• Lubrication
• +10 more

Michele Scaraggi is an Associate Professor at the Department of Innovation Engineering , University of Salento , Italy. He leads the Advanced BioInterface Technologies Lab and Advanced Driving to X Simulation Lab , a joint lab with Porsche Engineering. His work bridges tribology , contact mechanics , and bio-inspired robotics . Education: PhD in Bio-Mechanical and Mechanical Design (Politecnico di Bari, 2009); MS and BS in Mechanical Engineering (Politecnico di Bari, 2006 and 2004). Research Interests: Tribology: Friction, adhesion, and lubrication in dry/wet contacts. Soft Robotics: Microbots, smart actuators, and bio-inspired designs. Computational Mechanics: Multiscale modeling of hierarchical surfaces. Biomechanics: Applications to seals, tire/road interactions, and biomedical devices. Projects: ELFIN: Electroactive grippers for micro-object manipulation. TRIBOSCORE: Tribological modeling for sustainable industrial interfaces. BRILLIANCE: RFID-enhanced tactile rehabilitation technologies. Scientific Awards: AIMETA Junior Researcher Award (2017). Multiple COST grants for collaborative research in Germany. FFABR 2017 Grant for basic research funding. Teaching: Applied Mechanics (BS Industrial Engineering). Vehicle Mechanics (MS Mechanical Engineering). Fluid Dynamics (MS Aerospace Engineering). Doctoral course: Fundamentals of Adhesion. Collaborations: Imperial College London, IIT Italy, FZ-Jülich, SKF, Freudenberg, and others.

Jinghua LiView profile

Assistant Professor

• Materials Science and Engineering
• Biosensors
• Neuroengineering
• +5 more

Jinghua Li is an Assistant Professor in the Department of Materials Science and Engineering at The Ohio State University , affiliated with the Fontana Laboratories. Their research focuses on Flexible Electronics , Biosensors , and Neuroengineering , with applications in Wireless Biosensors , Wearable Technology , and Biomedical Engineering . Research Interests: Flexible and implantable bioelectronic systems Wireless and self-powered sensing devices Electrochemical and microfluidic platforms for neurochemical monitoring Materials for chronic neural interfaces and biofluid encapsulation Wearable chemical sensors for noninvasive diagnostics Integration of VR/AR with gustatory/tactile feedback Article Trends: Their publications emphasize conductive biomaterials , implantable antennas , neurochemical sensors , and biofluid-compatible encapsulation , with a growing focus on VR/AR integration and remote sensory interfaces in recent years. Laboratory Affiliation: Jinghua Li collaborates with the Fontana Laboratories, located at 140 W 19th Ave, Columbus, OH, to develop advanced materials and systems for biomedical applications.

• Optical Fiber Sensors
• Brillouin Scattering
• Distributed Sensing
• +3 more

Kouhei Noda is a Researcher at Tokyo Institute of Technology , specializing in Optical Fiber Sensors , Brillouin Scattering , and Distributed Sensing . His work focuses on advancing Brillouin optical correlation-domain reflectometry (BOCDR) for high-resolution, real-time tactile and temperature sensing applications. Education: Doctor (Engineering), Tokyo Institute of Technology (2023). Research Interests: Noda's research explores Brillouin scattering for distributed strain/temperature sensing, tactile sensors using polymer optical fibers, and polarization-sensitive reflectometry to enhance measurement accuracy. He investigates low-coherence light sources and cost-effective fiber amplifier configurations to improve system performance. Recent Article Trends: His 15 most recent publications (2020-2025) emphasize high-speed distributed sensing , real-time touch/tactile detection , and systematic error compensation in BOCDR systems. Key subfields include noise floor distortion compensation , low-coherence Brillouin sensors , and polymer fiber Bragg grating applications . Technical Contributions: Noda has co-authored patents related to wheelchair motorization devices and multimode FBG temperature stabilization . His collaborations with researchers like Yosuke Mizuno and Kentaro Nakamura span experimental validation, simulation analysis, and hardware optimization for optical sensing systems.