Find Academic Supervisors

Robert BainesView profile

Professor

• Robotics
• Soft Robotics
• Adaptive Morphogenetic Systems
• +4 more

Dr. Robert Baines holds the Professorship for Robotic Systems at ETH Zürich, leading research in adaptive morphogenetic robots, soft robotics, and amphibious robotic systems. His work emphasizes material science integration, reconfigurable mechanisms, and environmental adaptability. Key research areas include morphing limb design, tensegrity robots, and reproducible soft robotics methodologies. His research focuses on developing robots capable of evolving on demand through modular architecture and self-reconfiguration, with applications in multi-environment navigation. He has pioneered studies on inflatable actuators, tensile jamming fibers, and bio-inspired designs for amphibious locomotion. Baines also advocates for standardized testing protocols in soft robotics to ensure reproducibility and cross-laboratory collaboration. Notable contributions include the RoboWrangler rope-based grasping system and the amphibious robotic turtle demonstrating aquatic-to-terrestrial transitions. His work bridges mechanical engineering, materials science, and artificial intelligence to create robust, context-aware robotic systems. Current projects explore variable stiffness mechanisms, multi-modal sensing, and energy-efficient morphing systems. Baines collaborates with industry partners to translate theoretical advancements into deployable robotic solutions for challenging environments.

Wolfgang LosertView profile

Professor

• Biophysics
• Data Science in Physics
• Neuroscience
• +6 more

Wolfgang Losert is a **Professor of Physics** and Interim Associate Dean for Research in the College of Computer, Mathematical, and Natural Sciences at the University of Maryland. He is also Co-Director of the UMD-NCI Partnership for Integrative Cancer Research and affiliated with the Institute for Physical Science and Technology (IPST). His research bridges physics, biology, and data science, focusing on the quantitative analysis of living systems, including cell migration, neural networks, and granular materials. Losert’s work emphasizes using physical signals (light, EM fields, texture) to control cell behavior, particularly in epithelial and neural systems. He has pioneered methods like **excitable systems theory** to study cellular sensing and response, with applications in cancer research, neurobiology, and environmental health. Key collaborations include projects on astrocyte dynamics, actin wave signaling, and the impact of nanotopography on cell behavior. His lab integrates advanced microscopy, machine learning, and computational modeling. Notably, he contributed to founding the APS Group on Data Science (GDS) and received an APS Innovation Award for developing the Data Science Education Community of Practice (DSECOP). His interdisciplinary approach addresses challenges in biophysics, materials science, and systems biology. **Awards**: APS Innovation Award (2021). **Labs**: Biodynamics Lab, Optics Lab. **Outreach**: Maryland Day demonstrations on cell tracking and granular physics.

• Bionics
• Marine Biology
• Underwater Robotics
• +3 more

Prof. Dr. William M Megill is a Professor of Bionics at the Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences. He holds additional roles as Lecturer in Bionics at Bremen University of Applied Sciences and has held visiting appointments at institutions globally. His research focuses on underwater robotics, marine biology, and bionic engineering, with a particular emphasis on biomimetic designs inspired by aquatic life. He leads projects like the ENSPIRE submarine team and FabLab.blue, fostering student innovation through hands-on prototyping. Education: BSc (Physics) from McGill University, PhD (Zoology) from University of British Columbia. Professional experience includes roles at University of Bath, University of California, and research foundations. He collaborates internationally with teams in Europe and North America. Research interests include robotic fish propulsion, environmental monitoring via bio-inspired sensors, and ecological studies of coastal zones. His work bridges engineering and biology, with applications in conservation and robotics. He teaches courses on bionics, biomechanics, and marine bioresources, emphasizing experiential learning through laboratory exercises. Grants and projects involve marine robotics, beaver ecology, and flow sensing technology. His lab, CBNT Research Centre, develops submersibles and sensors for field deployment. The HSRW submarine team under his leadership has achieved international acclaim, winning European championships. Labs/Teams: CBNT Research Centre, ENSPIRE teams, and FabLab.blue. His work integrates academic research with practical engineering challenges, promoting interdisciplinary solutions for marine science and technology.

Devinder KaurView profile

Professor

• Intelligent Engineering Applications
• Bio-inspired Computational Algorithms
• Fuzzy Logic Systems
• +4 more

Dr. Devinder Kaur is a Professor in the Department of Electrical Engineering and Computer Science at the University of Toledo's College of Engineering. She holds a PhD in Computer Engineering from Wayne State University (1989), alongside degrees from the University of Aberdeen and Panjab University. Her research focuses on bio-inspired computational algorithms for engineering applications, including fuzzy logic, neural networks, and swarm intelligence. She has published over 100 articles and secured grants from NSF, Daimler Chrysler, and others. Education: PhD and MS in Computer Engineering, Wayne State University (1989, 1985) M.Sc. in Medical Physics, University of Aberdeen (1976) M.Sc. (Hons.) in Physics (Electronics), Panjab University (1970) Research Interests: Integrating intelligence into engineering systems using bio-inspired methods, nature-inspired machine learning, and parallel computing architectures. Her work spans applications in energy systems, medical imaging, and autonomous systems. Key Achievements: Developed courses like 'Biologically Inspired Computing' and 'Fuzzy Systems and Applications.' Summer fellowships at NASA Glenn, AFRL Dayton, and Daimler Chrysler. Fulbright Senior Specialist Award (lectured at Nippon Institute of Technology and Tokyo Denki University). Recipient of Thomas Rumble Fellowship, Commonwealth Fellowship, and Panjab University Medal. Grants & Contributions: Over $2M in research grants, including NSF support and industry partnerships. Active in professional societies like IEEE, WSEAS, and INFORMS. Labs/Teams: Leads research in bio-inspired computing and smart energy systems. Collaborates on interdisciplinary projects involving AI, medical diagnostics, and autonomous UAV systems.

Seth HutchinsonView profile

Professor

• Robotics
• Control Systems
• Artificial Intelligence
• +3 more

Seth Hutchinson is a Professor at Northeastern University with joint appointments in the Khoury College of Computer Sciences and the Department of Electrical and Computer Engineering. His expertise spans robotics, control systems, and artificial intelligence, with a focus on robotics planning, sensing, control, and multi-robot systems. He holds a PhD in Electrical Engineering from Purdue University (1988) and is an IEEE Fellow. His research emphasizes bio-inspired robotics, human-robot interaction, and autonomous systems. He leads initiatives in the Institute for Experiential Robotics, advancing robotics applications in art, environmental monitoring, and human-centric systems. Key contributions include frameworks for robust control, task allocation in heterogeneous multi-robot teams, and sim-to-real transfer in autonomous systems. His work bridges theoretical control with practical applications, such as artistic brush painting with cable robots and emotion-aware bipedal navigation. Hutchinson advocates for diversity and inclusion in robotics through IEEE RAS initiatives and has contributed to foundational texts like Robot Modeling and Control . His recent projects address challenges in autonomous surface vehicle control, quadruped locomotion, and safety-aware task planning using large language models. Notable awards include the IEEE Fellow distinction, recognizing his impact on robotics research. He collaborates across disciplines, integrating vision-based localization, reinforcement learning, and sensor fusion into robotic systems. Current efforts include advancing resilient control policies and exploring the ethical dimensions of AI in academia and industry.

Michael A. WebbView profile

Assistant Professor

• Polymer Science
• Soft Materials
• Computational Materials Design
• +5 more

Michael A. Webb is an Assistant Professor in the Department of Chemical and Biological Engineering at Princeton University, where he leads the Webb Research Group focused on computational materials design. His research integrates theory, simulation, and machine learning to develop soft materials for sustainability and health applications, including batteries, water treatment, and drug delivery systems. Education Ph.D., Chemical Engineering, California Institute of Technology, 2016 M.S., Chemical Engineering, California Institute of Technology, 2015 B.S., Chemical and Biomolecular Engineering, University of California – Berkeley, 2011 Research Focus The Webb Group develops predictive computational frameworks to study charge transport in polymers, stimuli-responsive biopolymers, and interfacial phenomena. Key methodologies include coarse-grained molecular dynamics, machine learning, and multiscale modeling, with applications spanning energy storage, environmental remediation, and biomedical engineering. Current efforts emphasize data-driven design of architecturally complex polymers and biomimetic systems. Recent publication trends highlight three core themes: (1) Physics-guided machine learning for polymer property prediction, (2) Phase behavior of biomolecular condensates and disordered proteins, and (3) Sustainable material design through computational optimization. Methodological innovations in coarse-graining and neural networks underpin much of this work. Awards and Honors ACS COMP OpenEye Cadence Molecular Sciences Outstanding Junior Faculty Award (2025) NSF CAREER Award (2023) Howard B. Wentz, Jr. SEAS Junior Faculty Award (2022) Herbert Newby McCoy Award, Caltech (2016) Chemical Computing Group Excellence Award (2016) Resnick Sustainability Institute Fellowship (2012–2014) Research Group and Advising Professor Webb advises 10 graduate students working on polymer informatics, biomolecular simulations, and energy materials. The Webb Research Group collaborates extensively with experimentalists and employs high-throughput computational screening. Recent group achievements include machine-learning frameworks for enzyme-polymer hybrids and awards for poster presentations at major conferences.

Tim SchorkView profile

Professor

• Computational Design
• Robotic Fabrication
• Additive Manufacturing in Architecture
• +4 more

Tim Schork is a Professor at the Queensland University of Technology (QUT) , affiliated with the Faculty of Engineering and the School of Architecture & Built Environment . His research focuses on integrating computational design , robotic fabrication , and additive manufacturing in architectural and construction contexts.

• Computer Science
• Artificial Intelligence
• Optimization Algorithms
• +6 more

Viorica R. Chifu is a prolific researcher in computer science with a focus on optimization algorithms, energy systems, and health informatics. She has extensively collaborated with colleagues such as Cristina Bianca Pop, Ioan Salomie, and Tudor Cioara on projects involving web service composition , smart grid optimization , and health monitoring via wearables . Her work often integrates bio-inspired methods like whale optimization and ant-based clustering with practical applications in renewable energy and elderly care.

Alexander ChubykinView profile

Associate Professor

• Neural Circuits
• Synaptic Plasticity
• Optogenetics
• +6 more

Alexander Chubykin is an Associate Professor in the Department of Biological Sciences at Purdue University, part of the College of Science. His research focuses on neural circuits in the mouse visual cortex, synaptic plasticity, and optogenetic techniques to study autism and neural circuit dysfunction. Chubykin employs advanced imaging and electrophysiological methods to investigate how neuromodulatory systems influence visual perception and reward-dependent plasticity. His work integrates in vitro and in vivo approaches, including automated patch-clamp electrophysiology and optogenetics, to dissect neural circuit mechanisms. Key research areas include the role of neuroligins and neurexins in synapse formation, and the application of adaptive optics for high-resolution microscopy in thick tissues. Chubykin has developed novel wireless neural recording devices and contributed to neurotechnology advancements such as deep learning-driven adaptive optics for single-molecule localization microscopy. Publications highlight his contributions to understanding visual cortex plasticity in autism models, potassium channel roles in zebrafish fin patterning, and interdisciplinary studies combining materials science with neurobiology. His lab’s innovations span from molecular mechanisms to translational neurotechnology, with applications in brain-inspired computing and neural circuit restoration after injury.

Qi GuoView profile

Assistant Professor

• Computational Imaging
• Computer Vision
• Machine Learning
• +5 more

Qi Guo is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on computational imaging, computer vision, and applied optics, with an emphasis on developing next-generation visual sensors through the integration of optics, electronics, and signal processing. He leads a lab creating prototypes like Focal Split (a handheld depth camera inspired by spider vision) and MetaHDR (a metasurface-based HDR imaging system). Education: B.E. in Automation (Tsinghua University, 2015), M.S. and Ph.D. in Electrical Engineering (Harvard University, 2018 and 2021). His work bridges theory and practice, with recent breakthroughs in photon-limited depth estimation (Blurry-Edges) and robust boundary detection (CT-Bound). Research Interests: Computational Imaging Optical System Design Machine Learning for Vision 3D Sensing Metasurface Engineering Embedded Sensor Systems Notable Achievements: Best Paper Award (ECCV 2016) Best Demo Award (ICCP 2018) IEEE Technical Committee on Computational Imaging Editor of Open Journal of Signal Processing Lab Philosophy: Prioritizes 'maker mindset' for hands-on prototyping, requiring 10+ weekly hours from undergrad researchers. Offers SURF internships and collaborates on projects like satellite stereo imaging (Stellar dataset) and birefringent metasurface applications.

Hector GomezView profile

Professor

• Computational Mechanics
• Multiphase and Multiphysics Modeling
• Biomechanics
• +7 more

Hector Gomez is a Professor of Mechanical Engineering and Courtesy Professor of Biomedical Engineering at Purdue University’s School of Mechanical Engineering. He leads the Gomez Lab, focusing on computational mechanics, multiphase systems, and biomedical applications. His research bridges engineering and medicine through advanced modeling of interface problems, including tumor growth, fluid-structure interaction, and biomechanics. Education: Ph.D. in Civil Engineering from Universidade da Coruña, Spain. He also holds a Civil Engineering degree from the same institution. His work integrates isogeometric analysis, phase-field methods, and high-performance computing. Research Interests: Modeling multiphase and multiphysics systems using phase-field methods; isogeometric methods in fluid/solid mechanics; tumor growth simulation; computational fluid-structure interaction. His lab collaborates on prostate cancer growth forecasts using MRI data and personalized biomechanical models. Key Achievements: ERC Starting Grant (2012), MIT Technology Review Innovators Under 35 (2014), Princess of Girona Scientific Award (2017), USACM Fellow (2023). His team develops open-source tools for multiphysics simulation. Grants & Funding: Includes ERC grants, USACM awards, and Purdue University faculty scholarships. Active in computational oncology, drug delivery (subcutaneous injection modeling), and soft material mechanics. Labs/Teams: Gomez Lab at Purdue, specializing in computational engineering and medicine. Collaborations with biomedical researchers and industry on personalized medicine and medical device design.

• Robotics
• Mechanism Design
• Tactile Perception
• +2 more

Dr. Yu She is an Assistant Professor at the Edwardson School of Industrial Engineering, Purdue University, specializing in robotics, mechanism design, and tactile perception. His research focuses on developing advanced robotic systems for manufacturing and automation, with an emphasis on multi-sensor integration and machine learning applications. Education: Postdoc, Computer Science and Artificial Intelligence Lab, MIT (2018-2021) PhD in Mechanical Engineering, Ohio State University (2018) MS in Electrical and Computer Engineering, Ohio State University (2018) MS in Mechanical Engineering, Harbin Institute of Technology, Shenzhen (2014) BS in Mechanical Engineering, Taiyuan University of Technology (2011) His research explores the intersection of robotics, machine learning, and sensor technology, with applications ranging from delicate bio-product manipulation to industrial automation. Key areas include tactile perception systems, robotic gripper design, and reinforcement learning for control policies. Recent publications demonstrate a consistent focus on improving robotic dexterity through advanced tactile sensing and machine learning. Work shows strong momentum in visuo-tactile integration, differentiable trajectory optimization, and bio-inspired manipulation systems suitable for unstructured environments. Dr. She leads research on tactile-reactive grippers for complex tasks like cable untangling and deformable object manipulation. His lab develops novel hardware-software solutions for industrial applications including poultry processing automation and additive manufacturing.

Emma AlexanderView profile

Assistant Professor

• Computational Imaging
• Bio-Inspired Vision
• Physics-Based Computer Vision
• +3 more

Emma Alexander is an Assistant Professor of Computer Science at Northwestern University's McCormick School of Engineering, with a courtesy appointment in Electrical and Computer Engineering. Her work bridges computational imaging, biological vision principles, and sustainable AI systems. She leads the Bio Inspired Vision (BIV) Lab, focusing on developing cameras and algorithms inspired by natural vision systems. Key research areas include bio-inspired color sensing, depth estimation, and physics-informed machine learning for scientific imaging. Education: PhD and MS in Computer Science from Harvard University, BS in Physics and Computer Science from Yale University. Research emphasizes reverse-engineering biological vision to advance imaging technologies. Notable projects include hyperspectral colorization, zebrafish optic flow analysis, and metalens-based depth sensors. Awards include Best Student Paper (ECCV 2016) and Best Demo (ICCP 2018). Her lab collaborates with astrophysicists through the SkAI Institute and engages in outreach through courses like Natural and Artificial Vision. Teaching includes graduate Computational Optics and an advanced undergraduate course on vision science. She mentors over 10 students annually, with undergraduates contributing to publications at CVPR and MNRAS.

Po-Chun HsuView profile

Assistant Professor

• Dynamic Metamaterials
• Electrochemistry
• Heat Transfer
• +7 more

Po-Chun Hsu is an Assistant Professor at the Pritzker School of Molecular Engineering (PME) at the University of Chicago. He holds a BS from National Tsing Hua University and a PhD in Materials Science and Engineering from Stanford University, followed by postdoctoral research in Mechanical Engineering at Stanford. Prior to UChicago, he was an Assistant Professor at Duke University’s Department of Mechanical Engineering and Materials Science (2019–2022). His research focuses on dynamic metamaterials, electrochemistry, radiative thermoregulation, and sustainable energy solutions. Notable contributions include cooling textiles recognized by Scientific American and innovations in wearable thermal management systems. His work integrates nanotechnology, smart materials, and AI to address climate challenges, such as carbon capture and energy-efficient building systems. He leads the Hsu Group and serves on advisory boards for Nano Letters and EcoMat . Awards include the NSF CAREER Award (2022), MIT Technology Review’s Innovators Under 35 (2020), and Sony Faculty Innovation Award (2019). Research highlights include adaptive thermal devices, photocatalytic hydrogen production, and scalable manufacturing of radiative cooling materials. His lab explores intersections of materials science, environmental engineering, and biomedical applications, such as vascularized electrodes for batteries and bio-inspired membranes for tissue engineering. Current projects emphasize translating lab innovations into real-world solutions for climate resilience and energy sustainability.

Alexander AminiView profile

Researcher

• Autonomous Systems
• Machine Learning
• Robotics
• +9 more

Alexander Amini is a Postdoctoral Researcher at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) , working under the guidance of Prof. Daniela Rus. He earned his PhD (2022), Master of Science (2018), and Bachelor of Science (2017) in Computer Science from MIT, with a minor in Mathematics. Amini co-founded Liquid AI and Themis AI , and serves as a lead organizer and lecturer for MIT's deep learning course 6.S191 . His research focuses on the science and engineering of autonomy , particularly for safe decision-making in uncertain environments. Key contributions include Developing end-to-end control systems for autonomous agents Formulating confidence metrics in neural networks Creating mathematical models for human mobility analysis Innovating inertial refinement systems Amini's publications reveal trends across autonomous vehicle control , robust machine learning , and uncertainty quantification . His work spans continuous-time neural models, sensor optimization for soft robotics, and applications in molecular discovery and financial modeling. Notable collaborations include projects with NVIDIA and Harvard Medical School. Scientific recognition includes 2011 EU Contest for Young Scientists Grand Prize 2011 BT Young Scientist overall winner 2017 NSF Graduate Fellowship 2020 MIT Outstanding Mentor Award 2021 JP Morgan Fellowship As part of MIT's Distributed Robotics Laboratory , Amini works on systems that combine robotics with everyday life applications, supported by grants from FinTech@CSAIL and MachineLearningApplications@CSAIL for bias mitigation in financial and clinical domains.