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Kostas Bekris is a Professor in the Department of Computer Science at Rutgers University, specializing in Robotics and Artificial Intelligence. His research focuses on motion planning, autonomous manipulation, and robot control, with notable contributions to tensegrity robotics, perception-driven systems, and large-scale package handling. He leads a team conducting groundbreaking work in robotics, supported by grants from NSF, NASA, and industry collaborators like ExxonMobil. His group emphasizes interdisciplinary approaches, combining machine learning, topological methods, and differentiable physics modeling to advance robot capabilities in complex environments. Education details are not explicitly stated in the provided texts, but his academic career has included significant mentorship of PhD students and postdoctoral researchers. Key projects involve vision-driven manipulation pipelines, obstacle detection systems (PROBE), and resilient robot designs inspired by biological structures. He has been recognized for his work through prestigious awards including the NASA Early Career Grant and multiple NSF grants, as well as team achievements in robotics competitions like the Amazon Picking Challenge. Research interests span robotics subfields such as: Autonomous manipulation in cluttered environments Learning-based control for dynamic systems Topological data analysis for motion reasoning Tensegrity and soft robotics architectures Sim-to-real transfer in robotic tasks His team's work has produced open-source software tools and datasets, advancing benchmarks in manipulation and perception. Recent articles emphasize scalable solutions for industrial automation and robust navigation strategies in unstructured settings. Scientific achievements include: Development of PROBE for proprioceptive obstacle detection Advances in differentiable physics engines for tensegrity systems NSF-funded projects on robotic rearrangement and modular morphologies Advising contributions span over a decade, with current advisees focusing on topics like non-prehensile manipulation and large-scale storage optimization. Collaborations with industry (e.g., ExxonMobil) and academic partners (Yale University) reflect his commitment to applied robotics research. Labs and teams under his leadership include the Rutgers CS Robotics Group, contributing to projects like the ARIAC challenge platform and packing/industrial automation systems. Future work targets improved robot resilience in disaster scenarios and enhanced human-robot collaboration paradigms.

• Robotics
• Artificial Intelligence
• Computer Vision
• +7 more

Vishesh Vikas is an Associate Professor in the Department of Mechanical Engineering at the University of Alabama, College of Engineering. He is based in the South Engineering Research Center (SERC) and leads the Agile Robotics Laboratory (ARL@UA), which focuses on bio-inspired, soft, and tensegrity robotics, as well as inertial sensing and estimation. Education: PhD, Mechanical Engineering, University of Florida, 2011 MS, Mechanical Engineering, 2008 B.Tech., IIT Guwahati, 2005 His research spans autonomous systems, wearable technologies, biomedical devices, guidance and control, intelligent systems, space robotics, and engineering education . The lab’s work integrates mechanical design, sensing, and advanced control to create agile, adaptive robotic systems for unstructured environments. The recent publications highlight a strong trend in soft and tensegrity robotics , with focus on gait synthesis, locomotion planning, shape and joint estimation, and dexterous manipulation. These works combine modeling, data-driven control, and sensor fusion, often using accelerometers, IMUs, and vision for real-time feedback. The research is published in top robotics venues such as IEEE TRO, RA-L, and ASME journals. Scientific Awards: No awards explicitly mentioned in the text. Vishesh Vikas actively advises graduate students, including PhD and Master’s candidates, and has successfully guided several to thesis and dissertation completion. His lab is affiliated with multiple research centers including the Alabama Center for the Advancement of AI, Center for Advanced Manufacturing, and Center for Advanced Public Safety. He is involved in outreach and education, including mechatronics workshops and seminar hosting. Current projects include exosuits for spine support and robotic systems for mobility in extreme environments. Laboratory and Team: The Agile Robotics Lab (ARL@UA) fosters interdisciplinary research in nature-inspired robotics, combining principles from biology, mechanics, and control. The team has hosted eminent scholars and is featured in university and college news for its innovative work on wearable robotics and soft manipulators.

• Robotics
• Soft Robotics
• Tensegrity Robotics
• +12 more

Jiefeng Sun serves as Assistant Professor in the Department of Aerospace and Mechanical Engineering within Arizona State University's School for Engineering of Matter, Transport and Energy. His research program centers on designing artificial-muscle-driven robots that replicate biological adaptivity through advanced modeling and control systems. His academic credentials include: Ph.D. in Robotics and Control from Colorado State University (2022) M.S. in Mechanical Engineering from Dalian University of Technology (2017) B.S. in Mechanical Engineering from Lanzhou University of Technology (2014) Dr. Sun's research integrates soft robotics, artificial muscles, and adaptive control to create morphologically intelligent systems. His work spans aerial robotics, wearable exoskeletons, and biomimetic locomotion, with emphasis on shape-changing mechanisms and energy-efficient actuation that enables robots to operate in unstructured environments. Analysis of his recent publications reveals dominant themes in twisted-and-coiled actuators, tensegrity structures, and physics-informed control methods. Key trends include variable-stiffness systems for wearable devices, data-efficient simulation techniques using Koopman operators, and bistable mechanisms for aerial grasping applications. His research excellence has been recognized through: Finalist for Best Student Paper Award at IEEE/RSJ IROS 2018 Reviewer of the Year 2021 for Smart Materials and Structures Journal 2022 DARPA Riser designation Dr. Sun actively recruits graduate students for robotics research and has secured significant funding including DARPA support. He teaches core courses including System Dynamics and Control I (MAE 318) while supervising thesis research and applied projects through MAE 599 and MAE 792. He directs the Sun Robotics Lab (https://sunroboticslab.github.io), which collaborates across biomechanics, materials science, and control theory to develop next-generation adaptive robotic systems with applications in healthcare, exploration, and human augmentation.

• Soft Robotics
• Artificial Muscles
• Robotics
• +3 more

Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.

• Soft Robotics
• Physical Reservoir Computing
• Physical Deep Learning
• +5 more

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 .

• Robotics
• Control Engineering
• Sensors
• +5 more

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)

• Robotics
• Artificial Intelligence
• Soft Robotics
• +9 more

Tim Dallas is an Associate Dean of the Graduate School and Professor of Electrical and Computer Engineering at Texas Tech University's Whitacre College of Engineering. His roles include overseeing graduate fellowship programs and developing innovative MEMS-based educational tools. Dr. Dallas is renowned for co-creating the Solar-Powered Digital Classroom-in-a-Box, deployed in off-the-grid African regions using pico projectors. Co-founded Class on a Chip, Inc. (2008) for commercializing micro-experimental devices Established the Technology Start-up Lab (2014) in partnership with business classes Principal Investigator for NSF-REU, CCLI, and S-STEM grants His research spans renewable energy systems, biometric authentication, and interdisciplinary learning. Dr. Dallas has secured funding from Keck and Welch Foundations for MEMS-based education technologies and served as Associate Editor for IEEE Transactions on Education. He is a Senior Member of IEEE and affiliated with ASEE and SPIE.

• MEMS-based Education
• Renewable Energy Systems
• Engineering Education
• +5 more

Louis Theran is a Lecturer in Mathematics at the University of St Andrews , School of Mathematics and Statistics. His research bridges geometry, combinatorics, and algorithmic problems, with applications in physics, materials, and machine learning. Education: Ph.D. in Computer Science, University of Massachusetts, Amherst (2010) M.S. in Computer Science, University of Massachusetts, Amherst (2007) B.S. in Computer Science and Mathematics, University of Massachusetts, Amherst (2006) Theran’s research focuses on the rigidity theory of frameworks , exploring how geometric and combinatorial properties determine structural stability. He investigates discrete geometry, sparse hypergraphs, and pebble game algorithms, connecting these to machine learning (e.g., low-rank matrix completion) and materials science (e.g., auxetic metamaterials, sticky disks). His recent work analyzes rigidity transitions in random graphs , universal rigidity in one-dimensional frameworks, and maximum likelihood thresholds via graph rigidity. Collaborations span computational geometry, algebraic statistics, and physics, emphasizing interdisciplinary applications. Scientific Awards: Heilbronn small grants scheme (2021) NSF/KOSEF East Asia and Pacific Summer Institutes Fellowship (2006) Theran has supervised numerous BSc, MMath, and PhD projects , including topics like tensegrities, graphons, and geometric constraint systems. He has also contributed to Gaussian graphical models and universality theorems for Delaunay triangulations.

• Rigidity Theory
• Discrete Geometry
• Combinatorics
• +4 more

Jelena Milošević is an Assistant Professor at the Department of Architectural Technologies, University of Belgrade - Faculty of Architecture. She serves as Vice-Dean for Teaching and Student Affairs and specializes in structural systems, spatial structures, morphology, and optimization of structures. Her research combines computational design, digital fabrication, and sustainable construction methods. Education: Graduated in Architecture, University of Belgrade (2006) Enrolled in doctoral studies (2009) Her research focuses on generative design approaches for architectural structures, performance-based optimization, and applications of 3D printing in construction. She investigates biomimetic pattern applications in structural design and develops parametric workflows for complex geometries. Recent work explores circular economy potentials in architectural production through recycled materials and additive manufacturing. Publications demonstrate a strong focus on digital fabrication technologies in architectural education and practice. Recent articles examine hybrid pedagogical approaches integrating 3D printing technologies into design studios, material efficiency in additive-manufactured structural systems, and sustainable applications of recycled materials in digital fabrication. She has participated in research projects including 'Development and application of scientific methods in design and construction of highly economical structural systems using new technologies' funded by the Ministry of Education, Science and Technological Development of Serbia.

• Structural Systems
• Spatial Structures
• Structural Morphology
• +2 more

Prof. Dr. Myfanwy Evans is a Professor of Applied Geometry and Topology at the University of Potsdam . Her research focuses on geometric and topological modeling of soft and biological systems, with emphasis on analyzing complex microstructures through experimental and theoretical approaches. She is affiliated with the Institute of Mathematics, leading projects in geometric constraints in biological and synthetic materials. Current Position: Professor, University of Potsdam (2020–present) Previous Roles: Emmy Noether Research Group Leader (2015–2020), Humboldt Postdoctoral Fellow (2011–2014) Research Highlights: Evans investigates topological potentials in protein assembly, curvature in biological systems, and geometric optimization in tensegrity frameworks. Her work spans 3-periodic entanglements, network theory, and bicontinuous structures. Funding & Collaborations: Participates in Matters of Activity and MATH+ excellence clusters, collaborating across Berlin universities and Germany. She contributes to research on discretization in geometry and dynamical systems. Software Development: Co-created SPIRE , a tool for bicontinuous phase recognition applied to plastid cubic membranes and biological structures. Technical Expertise: Utilizes Riemannian optimization for geometric modeling, explores auxetic materials, and studies foam coarsening dynamics.

• Geometric Modeling
• Topological Analysis
• Soft Matter Physics
• +2 more

Raktim Bhattacharya is a Professor in the Department of Aerospace Engineering at Texas A&M University, serving as Director of the Graduate Studies Program. He holds a Ph.D. in Aerospace Engineering from the University of Minnesota (2003) and a B.Tech from IIT Kharagpur (1996). His research focuses on uncertainty quantification, robust control, and nonlinear systems, with applications in aerospace systems design and control. Dr. Bhattacharya leads the Intelligent Systems Research Laboratory (ISRL), advancing algorithms for next-generation aerospace systems operating in uncertain environments. His work integrates optimal control, stochastic modeling, and data-driven methods to enhance system reliability and performance. Notable contributions include probabilistic robustness analysis, model validation frameworks, and sparse sensing architectures. Recent research trends emphasize optimal transport theory for state estimation, privacy-aware machine learning, and sensor-actuator co-design for resource-constrained systems. His publications span topics like UAV configuration optimization, LPV control frameworks, and invariant set estimation using physics-informed neural networks. Dr. Bhattacharya’s lab collaborates on projects involving tensegrity structures, cyber-physical systems, and space situational awareness. His work addresses challenges in hypersonic flight dynamics, celestial navigation, and resilient control under actuator degradation.

• Uncertainty Quantification
• Robust Control
• Nonlinear Systems
• +3 more

Philippe Poignet is a Professor at the University of Montpellier, affiliated with the Institut Universitaire de Technologie (IUT) and conducting research at the LIRMM (Laboratory of Informatics, Robotics, and Microelectronics of Montpellier). He served as Director of LIRMM from July 2015 to October 2023 and co-heads the IRP with Stanford University since 2025. His work focuses on Surgical Robotics, with a particular emphasis on medical device development, control systems, and biomedical applications. Co-founder of startup ACUSURGICAL (retinal surgery robotics) Scientific collaborator with STERLAB (flexible ureteroscopy robotics) Co-organized Summer School on Surgical Robotics (SSSR) for 20 years His research spans medical robotics , control theory , and biomedical imaging , with applications in needle steering, tissue interaction, and surgical precision. Recent publications highlight advances in soft tensegrity design , model predictive control , and multi-modality imaging registration . Scientific recognition includes: Best Paper Award at ARK’22 Prix de l’Innovation de l’I-Site MUSE (2020) Chevalier des Palmes Académiques (2019) He supervises doctoral students in projects related to flexible robotics , bioimpression , and robotic shoulder surgery , with collaborations across Europe and industry partners like CARANX Medical and CEDRAT Technologies.

• Surgical Robotics
• Medical Robotics
• Robotic Systems
• +7 more

Huizhong Cao is a researcher at Production Systems , Chalmers University of Technology. With a background in architecture and sustainable urban planning, she bridges design and technology through projects involving XR platforms , digital twins , and human-centered design . MSc in Sustainable Urban Planning (Chalmers) BSc in Architecture (Wuhan University) Her research focuses on mental workload assessment , XR interface design , and sustainable smart cities . Key projects include: RILIK - Immersive learning framework XR-TBP - Extended reality training for battery production TRIREME - Digital & Green Skills development DIGITALIS - Cognitive work optimization Recent publications analyze: VR interface design for collaborative manufacturing XR applications in environmental sustainability Digital twin integration with IoT Scientific recognition includes: IPOET Scholarship LEXUS Design Award Finalist KTH Innovative Incubator Program UIA-HYP Cup Third Prize She has contributed to UX design and XR development across multiple projects, often combining participatory design with game engine technology . Contact: huizhong@chalmers.se | huizhongcao1@gmail.com

• Cognitive Ergonomics
• Mental Workload
• Manufacturing Digitalization Innovation
• +5 more

Pierre Renaud is a Professor at INSA Strasbourg and Deputy Director of the ICube laboratory, specializing in Medical and Surgical Robotics, Mechatronics, and Additive Manufacturing. His research focuses on developing advanced robotic systems for healthcare applications, including surgical robots, compliant mechanisms, and MRI-compatible devices. He leads projects such as SPIRIT (multi-material additive manufacturing for medical robotics) and contributes to national initiatives like TIRREX and LABEX CAMI. Education: PhD in Mechanics from Université Clermont-Auvergne (2003), M.Sc. from ENS Cachan (1998), and Agrégation in Mechanical Engineering (1999). Visiting Associate Professor at Stanford University (2010–2011) as a Fulbright Fellow. Research Themes: Mechatronics for medical robotics, compliant systems, additive manufacturing integration, and tensegrity-based robots. Collaborates with IHU Strasbourg for surgical technology development and Axilum Robotics for industrial applications. Key Projects: Robotic assistance for interventional radiology, magnetic elastography, and beating heart surgery. Active in international collaborations (e.g., ANR, H2020 ITN ATLAS). Labs/Teams: Head of the Robotics, Data Science, and Healthcare Technologies group at ICube. Engaged in Equipex IRIS and ROBOTEX platforms for robotic innovation.

• Medical and Surgical Robotics
• Robotics
• Mechatronics
• +5 more

DAI Jiansheng is a Chair Professor at Southern University of Science and Technology (SUSTech) and Director of the Robotics Research Institute. He is a Fellow of the Royal Academy of Engineering (FREng), Fellow of the Academia Europaea, and holds multiple fellowships including IEEE, ASME, RSA, and IMechE. As Editor-in-Chief of the international journal Robotica, he has established himself as a leading authority in mechanisms and robotics research. His educational background includes a PhD from the University of Salford (1989-1993), a Master's degree from Shanghai Jiao Tong University (1982-1984), and a Bachelor's degree from the same institution (1978-1982). His academic journey has taken him from postdoctoral work at Salford University to research positions at Unilever Liverpool Research Centre before becoming a faculty member at the University of Sunderland and ultimately King's College London, where he served as Reader and then Chair Professor from 2007 until his current position at SUSTech. Professor Dai's research spans theoretical kinematics, screw theory, Lie algebra, and their applications to metamorphic and reconfigurable mechanisms. His pioneering work bridges the gap between versatile but expensive robots and efficient but non-flexible machines. His research interests include origami-inspired robotics, rehabilitation robotics for ankle treatment, soft robotics, and industrial applications in packaging and manufacturing. His theoretical framework has enabled significant advances in reconfigurable parallel mechanisms and metamorphic robotics. His extensive publication record shows a clear progression from fundamental theoretical work on screw algebra and Lie groups to practical applications in rehabilitation, manufacturing, and soft robotics. Recent publications demonstrate increasing focus on soft robotics, variable stiffness actuators, and continuum robots with Shape Memory Alloy applications, while maintaining strong theoretical foundations in screw theory and kinematic analysis of metamorphic mechanisms. ASME Mechanisms and Robotics Award (2015) ASME Machine Design Award (2020) IFToMM Excellence Award (2023) Tianjin Municipal Natural Science First Prize (2021) Crossley Award (2018) AT Yang Award in Theoretical Kinematics (2019) Professor Dai has supervised over 50 PhD students who now hold faculty positions at world-leading universities including University College London, Queen Mary University London, Purdue University, and Wollongong University. His research has been supported by numerous grants enabling the establishment of advanced robotics laboratories and international collaborations. He founded the IEEE Triennial International Conference on Reconfigurable Mechanisms and Robots (ReMAR), creating a major platform for international scholarly exchange in this specialized field. His research group maintains strong industry partnerships with companies including Cambridge Consultants, Goldman Sachs, and Amazon. The Robotics Research Institute he directs at SUSTech serves as a hub for interdisciplinary research, bringing together experts in mechanical engineering, computer science, biomedical engineering, and materials science. The institute focuses on both fundamental theoretical advances in mechanism design and practical applications in healthcare, manufacturing, and service robotics, with particular emphasis on metamorphic and reconfigurable systems that can adapt to multiple tasks.

• Theoretical Kinematics
• Screw Theory
• Lie Algebra
• +7 more