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Patrick KungView profile

Associate Professor

• Nanotechnology
• Quantum Computing
• Terahertz Spectroscopy
• +6 more

Dr. Patrick Kung serves as Associate Professor and Associate Department Head for Undergraduate Programs in the Department of Electrical and Computer Engineering at the University of Alabama's College of Engineering. His research spans nanotechnology, quantum computing, and terahertz photonics with significant contributions to metamaterials and optical systems. Research Focus: Dr. Kung specializes in terahertz spectroscopy, polarization-sensitive imaging, and nanoscale material engineering. His work integrates machine learning with optical systems for applications in underwater imaging, quantum networking, and biodegradable polymers. Recent projects include $1 million Department of Energy funding for quantum networking research (2024) and development of materials for slowing light propagation. Publication Trends: His recent publications (2022-2025) demonstrate a clear trajectory toward multimodal sensing systems combining terahertz technology, polarization control, and AI-driven image processing. Key themes include underwater object recognition using single-photon LiDAR, compact drone-compatible imaging platforms, and cryogenic photonic components for quantum applications. The work consistently bridges fundamental nanophotonics with practical engineering solutions. Department of Energy Funding ($1 Million for Quantum Networking Research, 2024) Dr. Kung actively mentors students in EPA-funded water disinfection projects using UV-LED technology and collaborates with industry partners through the Southeast Executives-on-Roster program. His laboratory work focuses on nanowire-based thin films and metamaterial absorbers, with applications in environmental monitoring and quantum communication hardware.

Taskin PadirView profile

Professor

• Experiential Robotics
• Human-Robot Teaming
• Embodied Artificial Intelligence
• +4 more

Taskin Padir is a Professor in the Department of Electrical and Computer Engineering at Northeastern University and concurrently serves as an Amazon Scholar. He holds a PhD and MS from Purdue University and a BS from Middle East Technical University. His research focuses on experiential robotics, human-robot teaming, and embodied AI, with leadership roles in the Robotics and Intelligent Vehicles Research Laboratory (RIVeR Lab) and the Institute for Experiential Robotics. Padir has led projects for DARPA, NASA, and industry partners, advancing autonomous systems for extreme environments and human-robot collaboration. Education: PhD, Electrical and Computer Engineering, Purdue University (2004) MS, Electrical and Computer Engineering, Purdue University (1997) BS, Electrical and Electronic Engineering, Middle East Technical University (1993) Research Interests: Shared autonomy and human-in-the-loop robotics Embodied artificial intelligence Human-robot teaming in extreme environments (e.g., space, disaster zones) Collaborative robotics for industrial applications His work bridges robotics, AI, and real-world challenges, with recent projects addressing seafood processing automation, robotic navigation in unstructured terrains, and spectroscopy-based environmental monitoring. Awards: Recipient of the 2024 Faculty Research Team Award, 2023 Impact Award, and 2022 Amazon Scholar distinction. His research has been funded by NSF, DARPA, NASA, and industry collaborators like Amazon Robotics and Intel. Labs: Director of the RIVeR Lab and Institute for Experiential Robotics, fostering interdisciplinary research in autonomous systems and intelligent vehicles. Current projects include CRISP (Co-worker Robots for Seafood Processing) and PROSPECT (robotic spectroscopy tools).

Peng ZhouView profile

Assistant Professor

• Robotics
• Machine Learning
• Computer Vision
• +3 more

Peng Zhou is an Assistant Professor at the School of Advanced Engineering, The Great Bay University , and the Principal Investigator of the Embodied Manipulation Intelligence (EMAIL) Robotics Lab . His research integrates robotics, machine learning, and computer vision, with a strong focus on deformable object manipulation, robot perception, and task-motion planning. Education: Ph.D. in Robotics, The Hong Kong Polytechnic University (Supervised by Dr. David Navarro-Alarcon) Postdoctoral Research Fellow, The University of Hong Kong (Advised by Dr. Pan Jia) Exchange Ph.D. Student, KTH Royal Institute of Technology (Supervised by Prof. Danica Kragic) Research Interests: Dr. Zhou's work spans robotics , machine learning , and computer vision , with specialized expertise in deformable object manipulation , robot perception and learning , and task and motion planning . His lab, EMAIL, pioneers solutions for robotic manipulation of soft and deformable materials. Scientific Awards & Honors: 2024 : Track 3 Champion, Zhuhai International Dexterous Manipulation Challenge 2023 : IEEE R10 Outstanding Volunteer Award 2022 : Outstanding Young Researcher Award, National Engineering Research Center 2022 : Best AI Implementation Award, Hong Kong AI Open Competition 2022 : IEEE MGA Young Professional Achievement Award Editorial & Leadership Roles: Dr. Zhou serves as an Associate Editor for IEEE Robotics and Automation Letters and has organized key workshops like the IROS 2025 Workshop on Contact and Impact-aware Manipulation . He is also a Guest Editor for special issues in Electronics and Frontiers in Robotics and AI .

Paul HendersonView profile

Lecturer

• Generative AI
• Probabilistic Machine Learning
• 3D Computer Vision
• +3 more

Dr. Paul Henderson is a Lecturer in Machine Learning at the School of Computing Science, University of Glasgow. He holds a BA in Mathematics (University of Cambridge, 2009), an MSc in Informatics (University of Edinburgh, 2010), and a PhD in Computer Vision (University of Edinburgh, 2018). His research focuses on generative AI, probabilistic machine learning, and minimally-supervised approaches to 3D computer vision, with applications in healthcare, computer graphics, and physical sciences. Education: PhD in Computer Vision (University of Edinburgh, 2018) MSc in Informatics (University of Edinburgh, 2010) BA in Mathematics (University of Cambridge, 2009) His work spans generative models, medical imaging, and robotics. Notable contributions include datasets like Flat’n’Fold and techniques in diffusion models for text-to-image retrieval. He has received grants including the Royal Society Research Grant (2022-2023) and the Vesuvius Challenge Autosegmentation Prize (2025). He supervises PhD students in topics such as medical image segmentation and generative AI. Teaching: CS5002 Advanced Programming, CS4061/CS5014 Machine Learning.

Changyang LiView profile

Lecturer

• Robotics
• Fusion Reactor Engineering
• Remote Maintenance Systems
• +2 more

Changyang Li serves as a University teacher in the Department of Mechanical Engineering at LUT University's School of Energy Systems in Lappeenranta, Finland. His institutional contact includes email Li.Changyang@lut.fi and phone +358 50 301 7482. Dr. Li's research specializes in robotics for nuclear fusion infrastructure, with emphasis on remote maintenance systems for tokamak reactors. His work addresses critical engineering challenges in vacuum vessel assembly, port-based maintenance, and heavy-duty manipulator design for next-generation reactors like DEMO and CFETR. Key methodologies include multi-objective optimization, kinematic mechanism analysis, and data-driven modeling of robotic systems operating in high-radiation environments. Analysis of his 15 publications (2019-2025) reveals concentrated expertise in DEMO reactor maintenance robotics, particularly mobile parallel mechanisms and cable-driven systems. His research demonstrates consistent focus on enhancing remote maintainability through innovations in elephant trunk robots, port closure tools, and in-situ machining solutions, directly supporting international fusion energy initiatives. No scientific awards are documented in available sources. No information is available regarding student supervision or research grant acquisitions. Specific laboratory affiliations or research team structures are not disclosed in the source materials.

Sylvain LefebvreView profile

Researcher

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +4 more

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

Fei LiuView profile

Assistant Professor

• Robotics
• Surgical Robotics
• Medical Robotics
• +3 more

Fei Liu is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His research focuses on surgical robotics, medical robotics, and control systems. He holds a PhD in Robotics from the University of Lyon (INSA de Lyon), France, an MSc in Control Systems and Automation Engineering from INSA de Lyon, and a BSc in Control Systems and Automation Engineering from Northwestern Polytechnical University, China. Fei's research interests include autonomous robotic systems, deformable object manipulation, and perception frameworks for surgical applications. His work emphasizes bridging real-world and simulation environments through advanced modeling and control techniques. Recent projects involve optimizing robotic actions using multi-modal demonstrations, improving tool-tissue interaction tracking, and developing frameworks for boundary parameter estimation in surgical settings. His articles highlight contributions to surgical robotics, including real-to-sim matching of deformable tissues, autonomous suturing, and trajectory optimization for wound care. He has also explored applications in haptic training systems and medical telerobotics. Fei's work often combines machine learning, physics-based simulation, and real-time control to address challenges in robotic surgery. Fei is affiliated with the Tickle College of Engineering and maintains an active research profile with collaborations in robotics and medical engineering domains. His lab focuses on advancing robotic autonomy in healthcare environments through interdisciplinary approaches.

Franziska Mathis-UllrichView profile

Professor

• Surgical Robotics
• Medical Robotics
• Artificial Intelligence in Medicine
• +3 more

Prof. Dr. Franziska Mathis-Ullrich is a Professor at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) leading the Surgical Planning and Robotic Cognition Lab (SPARC) in the Department of Artificial Intelligence in Biomedical Engineering. Previously, she was an Assistant Professor at Karlsruhe Institute of Technology (KIT) from 2019 to 2023. Her research focuses on minimally invasive robotic systems, soft robotics, and embedded machine learning for surgical applications. She holds a PhD in Microrobotics from ETH Zurich (2017), with earlier degrees from the same institution. Education: B.Sc. and M.Sc. in Mechanical Engineering and Robotics (ETH Zurich, 2009–2012) Ph.D. in Microrobotics (ETH Zurich, 2017) Research Interests: Minimally invasive medical robotics, soft robotic systems, AI-driven surgical assistance, microrobotics, and robot-assisted surgery. Her work emphasizes translating robotics innovations into clinical applications through interdisciplinary collaboration. Key Awards: IEEE ICRA Best Paper Award in Medical Robotics (2014) IEEE BioRob Best Student Paper Award (2016) ICRA Microassembly Challenge First Prize (2014 & 2015) Forbes 30 under 30 (2017) Grants & Projects: Leading a Bavarian State Ministry-funded project on endometriosis diagnostics (€3M). Active in multidisciplinary collaborations with Erlangen University Hospital. Serves as Vice-President of the German Society for Computer- and Robot-assisted Surgery (CURAC). Labs & Teams: Directs the SPARC Lab, which develops cognitive robotic systems for surgical planning and execution. Collaborates with institutions like Max Planck, Fraunhofer, and Helmholtz.

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +5 more

Lung-Pan Cheng is an Associate Professor in the Department of Computer Science and Information Engineering at National Taiwan University, where he leads the Human-Computer Interaction Laboratory. His research focuses on bridging the gap between physical and virtual realities through innovative interface technologies. Cheng's research expertise spans human-computer interaction, augmented/virtual reality, and tactile feedback systems. His work explores how people exist across three types of reality: physical reality governed by physics, imagined realities where things act by wishes, and programmable virtual realities. By examining the mismatches between these realities, Cheng develops novel interfaces, techniques, and devices that extend and interface these realities toward an ultimate unified experience. His recent publications reveal strong trends in haptic feedback systems, procedural generation in VR, and tangible interaction techniques. Cheng's work often involves creating physical props and mechanisms that enhance virtual experiences, with a particular focus on how humans can interact with and manipulate virtual objects through physical interfaces. His research has significant implications for VR training, gaming, and therapeutic applications. Best Paper Award at CHI 2022 for AirRacket: Perceptual Design of Ungrounded, Directional Force Feedback to Improve Virtual Racket Sports Experiences SIC People's Choice Award at UIST 2022 for Garnish into Thin Air Cheng has secured numerous research grants supporting his work in VR and haptic interfaces, with collaborations spanning multiple institutions. His laboratory develops working prototypes that demonstrate practical applications of his theoretical concepts, often resulting in award-winning publications at top HCI conferences. Current projects appear to focus on advanced fabrication techniques for interactive objects, perceptual illusions in VR, and novel haptic feedback mechanisms that don't require traditional grounded hardware. The Human-Computer Interaction Laboratory under Cheng's direction consists of graduate students and researchers working on various aspects of tangible and virtual interfaces. The team frequently publishes in premier venues like CHI, UIST, and IEEE VR, demonstrating consistent productivity and impact in the field. Current research directions include density-varying soft fabrication, polarized light mosaics, and systems for creating infinite virtual spaces within limited physical environments.

Gökhan AlcanView profile

Assistant Professor

• Robotics
• Machine Learning
• Control Theory
• +5 more

Gökhan Alcan is an Assistant Professor in Robotics and Machine Learning at the Automation Technology and Mechanical Engineering Unit of Tampere University, Finland. He leads the Advanced Learning, Control and AutomatioN (ALCAN) Research Group, focusing on safe model predictive control, constrained optimal control theory, reinforcement learning, and their applications to dynamical systems. His research addresses challenges in robotic manipulation, safe navigation, and human-robot collaboration through projects like the Aurora initiative on automated and connected machines. Education: B.Sc., M.Sc., and Ph.D. in Mechatronics Engineering from Sabanci University (2008–2019). Postdoctoral research at Sabanci University (2019) and Aalto University (2020–2024). Research Interests: Robotics, control theory, system identification, autonomous systems, and machine learning applied to robotic manipulation, autonomous vehicles, and safety-critical systems. Notable work includes trajectory optimization for hybrid systems, magnetic manipulation for medical applications, and sim-to-real gap analysis in cloth manipulation. Awards: Third Place in Aalto Open Science Award 2023, Elginkan Foundation Technology Award (2016), and multiple scholarships. Advised Ph.D. student David Blanco Mulero, who defended his thesis on robotic manipulation of deformable objects. Labs/Teams: ALCAN Research Group, former roles in Aalto University's Intelligent Robotics Group and Sabanci University's Control, Vision, and Robotics (CVR) Group.

• Robotics
• Artificial Intelligence
• Tactile Sensing
• +6 more

Satoshi Funabashi is an Assistant Professor in the Department of Intermedia Art and Science at Waseda University's School of Fundamental Science and Engineering, Japan. He is affiliated with the Graduate Program for Embodiment Informatics under Waseda University's Program for Leading Graduate Schools and contributes to multiple graduate schools including the Graduate School of Creative Science and Engineering. Education: Doctor of Engineering (Waseda University, 2017-2021) Research Focus: Robotics, tactile sensing, deep learning, and embodiment informatics Academic Appointments: Assistant Professor (non-tenure-track) His research centers on symbiotic robotics and tactile-driven manipulation, with recent publications exploring graph convolutional networks, vision-touch fusion, and morphology-specific deep learning for robotic hands. He has secured multiple competitive research grants including JSPS KAKENHI and JST ACT-I programs. Scientific Awards: Grant-in-Aid for Scientific Research (B) (KAKENHI), JSPS (2024-2027) Grant-in-Aid for Early-Career Scientists, JSPS (2022-2024) JST ACT-I Research Fellow (2020-2022, 2018-2020) JSPS Research Fellowship DC1 (2017-2020) He collaborates with the Intelligent Dynamics and Representation Lab (Prof. Tetsuya Ogata) and the Intelligent Machine Lab (Prof. Shigeki Sugano) at Waseda University. He has interned at MIT's CSAIL (2018-2019) and conducted research at UC Davis (2015). His work has been cited over 500 times with an h-index of 14 according to Google Scholar.

Changhyun ChoiView profile

Assistant Professor

• Visual perception for robotic manipulation
• Deep learning for object grasping
• Assembly manipulation
• +8 more

Changhyun Choi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Minnesota (UofM), Twin Cities. His research focuses on visual perception for robotic manipulation using deep learning. Assistant Professor, UofM Electrical and Computer Engineering (2018–present) Postdoctoral Associate, MIT CSAIL (prior to 2018) Research Interests : Visual perception for robotic manipulation Deep learning for object grasping and assembly Soft manipulation techniques Object pose estimation and tracking Active perception and reinforcement learning Combining vision with manipulation Scientific Awards : NSF CAREER Award (2022) Sony Research Award (Faculty Innovation Award, 2021 & 2024) Russell J. Penrose Excellence in Teaching Award (2021) IEEE ICRA 2022 Outstanding Student Paper Award Advising & Collaborative Research : He advises 5 PhD students (Jiacheng Yuan, Alireza Rezazadeh, Houjian Yu, Ross Worobel, Mingen Li) and 2 Master's students (Chase Anderson, Nikhilanj Venkata Pelluri). His work involves grants from NSF, MnRI, and NRF (Korea).

Alan KuntzView profile

Assistant Professor

• Robot Motion Planning
• Autonomous Systems
• Robot Design Optimization
• +4 more

Alan Kuntz is an Assistant Professor at the University of Utah's Kahlert School of Computing (KSoC) and a core member of the Robotics Center. He leads the interdisciplinary Kuntz Research Lab, focusing on robotics and computational methods with medical applications, particularly in healthcare and surgery. His work spans robot motion planning, autonomous systems, and robot design optimization. Education: Ph.D. in Computer Science from the University of North Carolina at Chapel Hill, with research in the Computational Robotics Research Group. Previously a postdoctoral scholar at Vanderbilt University's Medical Engineering and Discovery Lab. Research interests include surgical robotics, continuum robots, needle steering, and medical device design. Recent projects include autonomous needle navigation, continuum lung staplers, and metamaterial-based robots. His team has published extensively on topics like kinematic modeling, uncertainty quantification, and medical intervention systems. Notable awards include the 2022 IEEE Access Best Video Award for his group's work, and mentoring over 15 students through the University of Utah's Undergraduate Research Opportunities Program. The Kuntz Lab actively collaborates on clinical applications, presenting at top conferences like IROS, Hamlyn Symposium, and ISMR. Labs/Teams: Directs the Kuntz Research Lab, known for its innovative medical robotics projects. The lab's work has been featured in Forbes and other media outlets for breakthroughs like in vivo needle steering demonstrations.

Peter K. AllenView profile

Professor

• Robotics
• Computer Vision
• Medical Robotics
• +6 more

Peter K. Allen is a Professor of Computer Science at Columbia University's School of Engineering and Applied Science, with a career spanning over three decades in robotics research. His work focuses on robotic grasping , 3D vision and modeling , and medical robotics , where he has made significant contributions to autonomous manipulation and sensor integration. Current affiliation: Columbia University Robotics Lab Academic rank: Professor Key research areas: Robotics, Computer Vision, Artificial Intelligence Education A.B. in Mathematics-Economics from Brown University M.S. in Computer Science from University of Oregon Ph.D. in Computer Science from University of Pennsylvania (recipient of CBS Foundation Fellowship, Army Research Office Fellowship) Research Interests Allen's research bridges fundamental robotics challenges with applied domains. His work on robotic grasping explores low-dimensional subspaces and semantic task suitability, while 3D vision contributions include illumination coherence and texture registration methods. In medical robotics , he develops surgical imaging tools and BCI-enabled grasping systems. Recent publications show trends in: Deep learning for robotic manipulation (2017-2022) Human-robot interaction through BCI and augmented reality Deformable object manipulation (garments, thin shells) Multi-modal sensing (vision-tactile fusion) Scientific Recognition NSF Presidential Young Investigator Award Best Student Paper Award (2007) for collaborative work Over 30 years of continuous funding from NSF, Army Research Office, and medical grants Teaching and Mentorship He has taught graduate courses in robotics (COMS 4733/6731) since 2010, emphasizing hands-on projects with advanced platforms like Baxter, PR2, and Fetch robots. His lab provides immersive training in: 3D photography Humanoid robotics Autonomous navigation Grasp planning

• Machine Learning
• Control Systems
• Robotics
• +3 more

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.