Affiliations and Roles Professor Wang holds dual appointments as Professor of Physics and Mechanical and Aerospace Engineering at Cornell University. She is affiliated with the Sibley School of Mechanical and Aerospace Engineering and the College of Arts and Sciences. Education B.S. in Physics, Fudan University, Shanghai, China (1989) Ph.D. in Physics, University of Chicago (1996) NSF-NATO Postdoctoral Fellow, Theoretical Physics, Oxford University (1997) Visiting Member, Courant Institute of Mathematical Sciences, NYU (1997-1999) Research Her research focuses on the physics of living organisms, particularly insect flight dynamics , biophysics , and computational modeling . Key projects include: Dragonfly righting reflex mechanisms Neuro-mechanical control in fruit flies Unsteady aerodynamics and fluid-structure interactions Awards and Honors Simons Fellowship in Theoretical Physics (2020) Radcliffe Fellowship (2007) Cornell Provost's Award for Distinguished Scholarship (2005) David and Lucile Packard Fellowship (2002) Labs and Collaborations Her work integrates experimental and computational approaches, often conducted in collaboration with institutions like the Janelia Research Campus (HHMI) and the Joint Texas Experimental Tokamak (J-TEXT).
Eric Medvet is a professor specializing in evolutionary computation, genetic programming, and robotics. He is actively involved in research areas such as neuroevolution, soft robotics, and modular robotics. His work bridges theoretical advancements in evolutionary algorithms with practical applications in robotics and AI. Roles: Conference chair for EuroGP (2020-2022), co-chair of multiple workshops and sessions. Key Research: Focus on genetic programming, embodied intelligence, and the design of adaptive robotic systems. Research Interests: His work emphasizes the development of scalable and interpretable AI systems, particularly through evolutionary methods applied to robotics. He explores topics like neuroevolution for soft robots, quality diversity algorithms, and the integration of machine learning with evolutionary computation. Publications: His recent work highlights trends in interpretable AI, modular robotics control, and evolutionary algorithms for complex systems. Notable contributions include studies on MAP-Elites, graph-based genetic programming, and the application of LLMs in automated testing. Grants & Labs: Developed frameworks like JGEA for evolutionary computation experiments. Collaborates on projects integrating evolutionary methods with real-world robotics applications.
Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.
Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.
Caner Özer is a Researcher affiliated with Istanbul Technical University's Department of Artificial Intelligence and Data Engineering and the University of Twente's MIA group. He holds a PhD in Computer Engineering from Istanbul Technical University (2020), an MSc in Telecommunication Engineering (2017-2020), and a BSc in Electronics and Communications Engineering (2013-2017). His research focuses on medical imaging AI, explainable artificial intelligence (XAI), deep learning applications in healthcare, and computer vision techniques for artifact detection in medical imaging. He has conducted visiting research at the University of Twente (2024) and serves on academic committees at Istanbul Technical University. Research interests include developing explainable models for mammogram analysis, enhancing medical image quality assessment via transformers and neural networks, and addressing challenges in cardiovascular MRI segmentation through motion artifact detection. His work bridges deep learning theory with practical clinical applications, emphasizing transparency and accuracy in AI-driven medical diagnostics. Notable contributions include cross-domain artifact correction for cardiac MRI, joint CNN-RNN models for intracranial hemorrhage detection, and XAI methods for chest X-ray analysis. His research has been published in top-tier venues with a focus on medical imaging and deep learning advancements.
Dr. Babar Jamil is a Lecturer in Electrical Engineering at the University of York's School of Physics, Engineering and Technology. His expertise spans robotics, sensors, control engineering, and mechanism design. He holds a Ph.D. from Hanyang University (South Korea) and conducted postdoctoral research at Sungkyunkwan University, where he also served as a Research Professor. His current research focuses on safe human-robot collaboration systems, novel control algorithms for robotic systems, and smart structures through sensor integration. Education: Ph.D. in Electrical and Electronic Engineering, Hanyang University, South Korea Postdoctoral Researcher, Sungkyunkwan University, South Korea Research Interests: Developing hybrid robotic manipulators combining soft and rigid actuation Designing proprioceptive sensors for extreme environments Advances in pneumatic artificial muscles and soft actuators Integration of machine learning in robotics control systems Publications: Recent work emphasizes soft robotics actuators, sensor design, and human-robot interface innovations. Key themes include energy-efficient actuation, sensorized robotic fingers, and pumpless pneumatic systems. Labs/Teams: Leads robotics research at York, focusing on collaborative robotics and sensor-actuator integration. Maintains an active research group through his UoY Robotics website .
Kangkang Yin is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on computer animation, computer graphics, humanoid robotics, machine learning, and multimedia analysis. He teaches courses such as Computer Animation and Scientific Computing, and holds a PhD from the University of British Columbia (2007), MSc from Zhejiang University (2000), and BSc from Zhejiang University (1997). His work bridges robotics and animation through projects like physics-based character controllers, motion diffusion models, and robotic manipulation. Key contributions include the SIMBICON biped locomotion framework and research into emotion-driven dance animation. Recent efforts emphasize reinforcement learning applications in motion synthesis and robust visual navigation for unmanned ground vehicles. Yin's publications span over two decades, addressing challenges in motion control, physics-based simulation, and machine learning applications. His lab contributes to both academic advancements and practical robotics solutions. Current research trends show strong emphasis on combining generative AI with traditional animation techniques, as seen in recent work on auto-regressive motion models (AAMDM) and physics-augmented reinforcement learning (PARC).
Dr. Cheng-Chew Lim is a Professor in the School of Electrical and Mechanical Engineering at the University of Adelaide. He specializes in control theory, autonomous systems, and multi-agent reinforcement learning. His research focuses on trusted autonomous systems, secure cyber-physical networks, and decentralized decision-making models. He has published over 300 articles and supervised 50+ PhD and master’s students. Dr. Lim teaches courses in control systems, autonomous systems, and engineering project management. He has held editorial roles, including Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics, and is actively involved in professional associations like the IEEE Control and Aerospace Electronic Systems Joint Chapter. His current projects include physics-informed neural networks for medical imaging, secure distributed autonomous systems, and resilient formation control under cyberattacks. Dr. Lim has secured research grants from ARC and industry partnerships, emphasizing practical applications in robotics, cybersecurity, and smart systems.
Luyang Zhao is an incoming tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University (starting August 2025). He earned his PhD in Computer Science and double undergraduate degrees in Computer Science and Mathematics from Dartmouth College and the University of Minnesota respectively. Academic Affiliation : Clemson University (Assistant Professor) Education : PhD in Computer Science (Dartmouth College), BS in Computer Science & Mathematics (University of Minnesota) His research focuses on Robotics , particularly soft robotics, modular systems, and bio-inspired designs. Key areas include: Large Language Models for robotic design automation Modular tensegrity systems for self-assembling structures Swarm coordination strategies Multi-environment adaptability (land/aquatic/aerial) Simulation tool integration for design optimization Recent publications highlight his work on SoftSnap modular platforms, LLM-driven swarm intelligence, and bioinspired dolphin robots. He received the Neukom Outstanding Graduate Research Prize for his contributions. Industry Experience : Research internships at Amazon Robotics and TuSimple Mentorship : Advised 6+ graduate/undergraduate researchers Open-Source Contributions : Developed SoftSnap platform for rapid prototyping Academic Service : Workshop co-organization (IROS 2023), peer reviewing (RA-L, ICRA, IROS, RoboSoft, BioRob)
Dr. Zhao Na is a tenure-track Assistant Professor at the Singapore University of Technology and Design (SUTD), affiliated with the Institute of Sustainable Technology and Design (ISTD). She holds a Ph.D. in Computer Science from the National University of Singapore (NUS), where her thesis on 3D point cloud semantics earned the IMDA Excellence Prize. Her research bridges computer vision and machine learning, focusing on scene understanding, data-efficient learning, and domain generalization. Education: Ph.D. in Computer Science (NUS, 2021); Prior roles include Research Fellow at NUS. Research interests emphasize 3D scene analysis, object detection, semantic segmentation, and robust learning under noisy or limited data. Her work addresses challenges in multi-modal learning, continual learning, and open-world scenarios. Recent projects include geometry-semantics synergy in neural fields and cross-modal augmentation for visual grounding. Publications span top-tier venues like CVPR, ECCV, and ICCV, with a focus on 3D vision and AI. Key contributions include the PCTeacher framework for semi-supervised segmentation and Static-Dynamic Co-Teaching for incremental learning. Scientific Awards: IMDA Excellence Prize (2021). Active grants include a DSO Research Grant (2023–2026) and A*STAR MTC Grant (2023–2026). She leads the SUTD-ZJU Thematic Grant on 3D scene understanding (2022–2024). Laboratory/Team: Research group at ISTD/SUTD focuses on advancing AI-driven 3D perception and scene understanding systems.
Dr. Yu Xiang is an Assistant Professor of Computer Science at the University of Texas at Dallas (UT Dallas), leading the Intelligent Robotics and Vision Lab (IRVL) . He holds a Ph.D. in Electrical and Computer Engineering from the University of Michigan (2016) and prior roles include Senior Research Scientist at NVIDIA (2018–2021) and postdoctoral research at the University of Washington. Research Focus : His work centers on robotics and computer vision , particularly enabling robots to perceive 3D environments, plan actions, and interact autonomously in human-centric spaces. Key areas include unseen object segmentation, 6D pose estimation, manipulation trajectory optimization, and lifelong learning through robot-environment interaction. Key Contributions : Developed datasets like MultigripperGrasp and HO-Cap , and pioneered methods such as DeepIM for 6D pose estimation. His lab’s robot Ramp focuses on tasks like object manipulation and human-robot collaboration. Grants : NSF SMILE grant ($750K), DARPA Perceptually-enabled Task Guidance (co-PI), Sony Research Award (PI). Awards : NVIDIA Academic Grant (2024), Sony Research Award (2022), ECCV Best Paper (2018). Lab Activities : Engages in STEM outreach, including mentoring high school students in the 2024 Summer Bridge Camp. Current projects emphasize self-supervised learning and embodied AI for robotic systems.
Kawal Rhode is a Professor in Biomedical Engineering and the Head of Education at the School of Biomedical Engineering & Imaging Sciences , King’s College London. His work bridges engineering, clinical practice, and education, with a focus on image-guided interventions, medical robotics, and 3D printing in healthcare. He leads the educational strategy for multiple taught programs, including the BEng/MEng in Biomedical Engineering and MSc programs in Healthcare Technologies and Clinical Sciences. His educational background includes a BSc in Basic Medical Sciences and Radiological Sciences from Guy's & St. Thomas' Hospitals Medical School (1992) and a PhD in arterial blood flow analysis from University College London (2006). He joined King’s in 2001 as a postdoctoral researcher and progressed through academic ranks to full Professor in 2016. Prof. Rhode’s research centers on image-guided interventions , medical robotics , and innovative pedagogy . His team develops intelligent systems for catheter-based procedures, robotic ultrasound, and simulation platforms using 3D printing. His work integrates AI, biomechanics, and translational engineering to improve clinical outcomes. His recent publications (2024–2025) reflect a strong trend in autonomous robotic systems , AI-driven image analysis , and simulation-based training , particularly in cardiac and interventional applications. These works span journals and conferences in medical imaging, robotics, and biomedical engineering, showcasing interdisciplinary innovation. Wellcome EPSRC Centre for Medical Engineering (Co-Investigator) Three-Dimensional Hybrid Guidance System for Cardiac Interventional Procedures (PI, EPSRC-funded) SIE CDT: Haemodynamics of complex aortic aneurysms (Co-I, Artivion Inc) He leads the Success for Black Engineers initiative, funded by the Royal Academy of Engineering, to improve diversity and inclusion in engineering education. This includes outreach, mentoring, industry engagement, and wellbeing support for Black students. He has supervised numerous students and collaborators, many of whom appear as co-authors on recent publications. His lab is part of a broader network within the School of Biomedical Engineering & Imaging Sciences, collaborating with clinicians at St Thomas’ Hospital and industry partners.
Diego Patiño is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), a position he began in September 2024. He earned his Ph.D. in Computer Engineering from the National University of Colombia in 2020, following M.S. and B.S. degrees from the same institution. Prior to joining UTA, he served as a Postdoctoral Fellow at Drexel University and a Postdoctoral Researcher at the GRASP Laboratory, University of Pennsylvania. B.S. in Computer Engineering, National University of Colombia, 2010 M.S. in Computer Engineering, National University of Colombia, 2012 Ph.D. in Computer Engineering, National University of Colombia, 2020 Dr. Patiño's research centers on geometric computer vision and machine learning, with applications in robotics and 3D vision. His primary interests include 3D reconstruction, graph neural networks, symmetry detection, physics-informed machine learning, and reinforcement learning. He develops algorithms that integrate geometric priors and physical constraints into deep learning models to improve robustness and generalization in real-world robotic systems. His recent publications demonstrate a strong trend in leveraging implicit neural representations for 3D shape reconstruction, applying graph neural networks to swarm robotics, and enhancing computer vision tasks with self-supervised and physics-informed learning. Work spans high-impact venues such as IEEE RA-L, ICRA, ICPR, and MICCAI, showing a consistent focus on geometric reasoning, robotic perception, and medical imaging applications. His scientific contributions have been recognized with awards from the UTA Division of Student Affairs for exceptional dedication and positive impact (2024 and 2025). He is actively involved in securing research funding, with multiple grants under review from NSF, Air Force SBIR, and industry partners like Sony. Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (December 9, 2024) Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (April 30, 2025) Dr. Patiño advises and serves on committees for multiple graduate students in computer science and engineering, including doctoral and master’s candidates. He is also leading or co-leading several research grants under review, covering topics such as aerial swarm navigation, neuromorphic sensing, and industrial computer vision. He teaches graduate courses in computer vision and is involved in service roles including PhD admissions and faculty appointments committees. He is affiliated with research initiatives at UTA, including the UTARI Research Institute, where he has presented on geometric modeling and physics-informed learning. His lab focuses on developing next-generation computer vision algorithms for robotics, industrial inspection, and safety-critical systems.
Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.
Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.
