Prof. Hedan Bai is an Assistant Professor at the Department of Materials, ETH Zürich, specializing in robotics materials, bio-inspired systems, and soft robotics. Their research focuses on developing advanced materials for sensing, energy-efficient systems, and biomedical applications. Notable projects include the SmartSuit architecture for space exploration and self-healing optical sensors for soft robots. Research interests span bioelectronics, stretchable sensors, haptic interfaces, and biomimetic materials. Bai's work integrates material science with robotics to create adaptive, sustainable, and intelligent systems. Key areas include wireless implants for neuromodulation, environmental-responsive textiles, and energy-harvesting devices. Publications highlight innovations in optical waveguides, self-healing materials, and wearable technologies. Their interdisciplinary approach bridges robotics, biomedical engineering, and aerospace applications. No formal awards are listed, but contributions to next-gen materials for robotics are prominent. Advising and grants are not detailed in the text, but Bai's lab focuses on projects like SmartSuit for extravehicular activities and synthetic afferent neural networks. Collaborations likely involve aerospace and biomedical sectors.
Amir Zamir is a tenure-track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology Lausanne (EPFL) in the School of Computer & Communication Sciences. Previously, he worked at UC Berkeley, Stanford, and UCF with prominent researchers including Silvio Savarese, Jitendra Malik, Mubarak Shah, Rahul Sukthankar, and Leonidas Guibas. He currently leads the Visual Intelligence & Learning Lab at EPFL and serves as chief scientist of Duranta, having previously been the CVML chief scientist of Aurora Solar (a Forbes AI 50 company valued at $4B in 2022) from 2015 to 2022. Dr. Zamir's research spans computer vision, machine learning, and artificial intelligence, with a focus on developing general multi-modal/multi-task vision systems that operate as active agents in the real world. His work emphasizes slow science principles, seeking fundamental understanding over quick publications. Key research areas include embodied vision, multimodal foundation models, computational imaging, and vision-language integration. His notable projects include 4M, Taskonomy, Gibson Environment, Omnidata, and MultiMAE, which have significantly influenced the field of computer vision and embodied AI. Zamir has made substantial contributions to the computer vision community through numerous high-impact publications and leadership roles. His work demonstrates a consistent focus on creating vision systems that go beyond narrow and passive methods toward more general, active, and embodied approaches. The trajectory of his research shows increasing sophistication in handling multiple modalities and tasks within unified frameworks, culminating in recent work on multimodal foundation models that can handle diverse vision tasks. Dr. Zamir has received numerous prestigious awards including the Young Researcher Award 2022 from ECCV, the PAMI Mark Everingham Prize 2022, SIGGRAPH 2022 Best Paper Award for CLIPasso, CVPR 2018 Best Paper Award for Taskonomy, and CVPR 2016 Best Student Paper Award. He is also an ELLIS Faculty Scholar and received the NVIDIA Pioneering Research Award in 2018 for the Gibson Environment. As an advisor, Dr. Zamir has mentored numerous PhD students including Roman Bachmann, Andrei Atanov, Rishubh Singh, Jason Toskov, Kunal Pratap Singh, Zhitong Gao, Mingqiao Ye, and Muhammad Uzair Khattak. His former PhD students include Oguzhan Kar (now at Apple), Alexander Sasha Sax (co-advised with Jitendra Malik, now at Meta FAIR), and Teresa Yeo (now at MIT-Singapore Alliance). Dr. Zamir teaches several advanced courses including CS-503 Visual Intelligence, CS-500 AI Product Management, COM-304 Intelligent Systems, and ENG-615 Topics in Autonomous Robotics. Dr. Zamir leads the Visual Intelligence & Learning Lab at EPFL, which focuses on developing fundamental methods for visual intelligence that can operate effectively in real-world environments. The lab takes an interdisciplinary approach combining computer vision, machine learning, robotics, and cognitive science to create systems that can perceive, understand, and interact with the world. Current research directions include multimodal foundation models, computational imaging, embodied vision, and personalization of generative models.
Massachusetts Institute of TechnologyUnited States
Polina Golland is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and a Principal Investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on developing novel techniques for biomedical image analysis and understanding, particularly in medical vision, AI/ML, and health care applications. She leads the Medical Vision Group and collaborates with the Vision Group at CSAIL. Her work emphasizes statistical modeling of medical images, shape modeling, and predictive analytics for biological processes. Current projects include fetal MRI analysis, cardiac MRI segmentation, and quantitative assessment of pulmonary edema in chest X-rays. She has secured grants from NIH, MIT-IBM Watson AI Lab, and other institutions to support her research. Dr. Golland teaches courses on inference, probability, and probabilistic systems. She advises graduate students in MIT's EECS program and has mentored numerous postdocs and researchers. Her lab focuses on translating advanced imaging techniques into clinical workflows, with applications in neuroimaging, fetal health monitoring, and cardiovascular disease analysis. Notable collaborations include work with Harvard Medical School affiliates, Brigham and Women's Hospital, and the MIT Jameel Clinic. Her research aims to bridge computational methods with clinical needs, improving diagnostic tools and treatment planning through machine learning and medical imaging innovation.
Jia Deng is a Professor of Computer Science at Princeton University and directs the Princeton Vision & Learning Lab. His research focuses on computer vision, machine learning, and robotics, with an emphasis on advancing 3D vision and synthetic data generation. Ph.D., Princeton University, 2012 B.Eng., Tsinghua University, Computer Science His work spans optical flow, depth estimation, and visual reasoning, leveraging procedural scene generation and robust neural architectures. Recent publications highlight advancements in multi-layer depth estimation, stereo matching, and simulation environments for embodied AI. Alfred P. Sloan Research Fellowship, 2018 NSF CAREER Award, 2020 ONR Young Investigator Award, 2020 Multiple Best Paper Awards (ECCV, ICCV, 3DV) Deng leads the Princeton Vision & Learning Lab, which develops foundational tools for computer vision and machine learning. His mentorship extends to advising students and collaborating on interdisciplinary projects.
Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.
Michael Kaess is an Associate Professor at the Robotics Institute, Carnegie Mellon University (CMU), within the School of Computer Science. He leads the Robot Perception Lab (RPL) and contributes to the Field Robotics Center (FRC) and Computer Vision Group (CV). His research focuses on efficient perception algorithms for mobile robots, particularly in 3D mapping, SLAM, and sensor fusion using vision, LiDAR, inertial, and sonar data. Kaess holds a PhD in Computer Science from Georgia Tech and was a postdoc at MIT's Marine Robotics Lab. Education: Georgia Institute of Technology, PhD in Computer Science (2008) MIT, Postdoctoral Associate (2008–2010) Research Interests: Kaess develops algorithms for robust and efficient inference in robotics, emphasizing factor graphs and linear algebra. His work spans underwater robotics, aerial systems, tactile SLAM, and multi-sensor integration. Key areas include SLAM with planes/lines, imaging sonar reconstruction, and neural field methods for LiDAR-visual fusion. Publications: Over 145 papers, including work on EDPLVO (visual odometry), HoloOcean (underwater simulation), and neural radiance fields with LiDAR. Recent trends focus on robust incremental smoothing, acoustic-optical fusion, and real-time volumetric mapping. Awards: Recognized with the RSS Test of Time Award (2020), Outstanding Associate Editor (2022), and paper awards at ICRA/ICRA. Active in conference organization (IROS/ICRA program committees). Advising & Grants: Supervises 10+ current PhD/MSc students, with past advisees contributing to CoRL/ICRA work. Manages grants in perception, autonomy, and marine robotics. Teaches courses like Robot Localization and Mapping (16-833). Labs/Teams: Directs RPL, collaborates with FRC on field robotics. Develops open-source tools like GTSAM (GNU Toolkit for Smoothing and Mapping).
Prof. Rob Timmermans is a Professor of Theoretical Physics and Vice-Dean for Education at the University of Groningen (UG). He is affiliated with the Faculty of Science and Engineering and the Precision Frontier — Van Swinderen Institute for Particle Physics and Gravity. His research focuses on theoretical particle physics, quantum mechanics, and precision measurements, particularly in electric dipole moment (EDM) searches using molecules like BaF. His work includes developing methods for molecular beam manipulation, phase-space analysis, and symmetry violation studies. He has contributed to collaborations such as NL-eEDM, advancing techniques for EDM detection and precision physics. Prof. Timmermans has received nominations for teaching awards, reflecting his commitment to education. Research highlights include studies on nucleon decay, antinucleon-nucleon interactions, and chiral effective field theory. His lab activities involve collaborations on laser-cooled molecules and trapping techniques. Prof. Timmermans’ articles often address fundamental physics questions, such as Lorentz violation in beta decay and parity violation in molecular systems. Awards: Nominated for Faculty Teaching Award 2014, Teacher of the Year 2014-15. Grants/Advising: Leads projects on EDM searches and particle physics, with active roles in international collaborations. Labs/Teams: Van Swinderen Institute, Precision Frontier group.
Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.
Avi Loeb , the Frank B. Baird Jr. Professor of Science at Harvard University, is a leading astrophysicist and former Chair of Harvard's Department of Astronomy (2011-2020). He directs the Institute for Theory and Computation (ITC) and the Galileo Project (2021-present), while holding the Sackler Senior Professorship at Tel Aviv University. His research spans cosmology, black holes, interstellar objects, and the search for extraterrestrial life. Education: PhD in Physics from Hebrew University of Jerusalem (1986) Former Positions: Member of the Institute for Advanced Study, Princeton (1988-1993) Loeb's research interests include: Gravitational wave detection and interstellar object analysis Simulation hypothesis and cosmic reality Technological signatures beyond the Standard Model His recent publications focus on 3I/ATLAS , gravitational SETI, and AI alignment in space exploration. Awards include being named among TIME's 25 Most Influential People in Space (2012), 14 Most Inspiring Israelis (2020), and ranking #3 in global astronomical impact (2024). He chairs the Breakthrough Starshot Initiative and advises the National Academies' Board on Physics and Astronomy.
William Chueh is a Professor in the Departments of Materials Science and Engineering and Energy Science & Engineering at Stanford University. He serves as Director of the Precourt Institute for Energy and Faculty Director of the Energy Innovation and Emerging Technologies Program. His research focuses on redox-active materials for energy storage, conversion, and carbon-neutral energy cycles. Education: PhD, Materials Science, Caltech (2010) BS, Applied Physics, Caltech (2005) Research Interests: Energy storage and conversion systems (batteries, fuel cells, electrolyzers) Multi-scale electrochemical and chemical reaction dynamics Materials design rules for redox-active solids Thermodynamic frameworks for sustainable energy Publication Trends: His work spans fundamental materials synthesis, electrochemical characterization, and modeling of redox reactions. Key themes include solar thermochemical cycles, ceria-based systems for CO2/H2O conversion, and advanced battery technologies. Scientific Honors: Outstanding Young Investigator Award (MRS, 2018) Camille Dreyfus Teacher-Scholar Award (2016) Sloan Research Fellowship (2016) CAREER Award (NSF, 2015) Advising: He advises students in energy technologies, materials science, and electrochemistry, including doctoral and master’s candidates. Contact: wchueh@stanford.edu
Maj Keith A. Wyman, PhD is a researcher affiliated with the Air Force Institute of Technology (AFIT), specializing in quantum optics and atmospheric photonics. His work focuses on photonic qubit propagation, atmospheric turbulence effects, and laser-based quantum technologies. He holds a PhD in Applied Physics from AFIT (2023), an M.S. in Applied Physics (2014), and dual B.S. degrees in Physics and Mathematics from the United States Air Force Academy (2012). Wyman’s research interests include developing atmospheric turbulence simulators, studying quantum communication protocols, and advancing laser technologies for free-space optical networks. His recent publications address photon pair indistinguishability, qubit degradation under turbulence, and sodium beacon systems for adaptive optics. He collaborates with institutions like Ohio State University’s Center for Quantum Information Science and Engineering. His academic contributions span conferences such as SPIE and FQMT, with a focus on experimental quantum systems and optical metrology. Wyman’s work bridges theoretical quantum mechanics with applied engineering solutions for military and civilian optical communication challenges.
Professor Thomas Bein is affiliated with the Department of Chemistry at Ludwig-Maximilians-Universität München (LMU) , where he leads the Functional Nanosystems research group. His work focuses on synthesizing and characterizing nanostructured materials with applications in energy, catalysis, and biomedical delivery. Mesoporous nanoparticles for drug delivery Semiconductor nano-morphologies for photovoltaics Photoelectrochemical water splitting Metal-organic frameworks (MOFs) Electroactive networks His research emphasizes atomic-scale control of material architectures using self-assembly, hydrogen bonding, and covalent interactions, enabling precise tuning of electronic, optical, and catalytic properties. A review of his recent publications reveals cutting-edge investigations into covalent organic frameworks (COFs), perovskite-inspired solar materials, and functional nanoparticle systems. Key trends include optimizing energy conversion efficiency, enhancing stability in optoelectronic devices, and exploring bio-compatible nanocarriers for targeted therapies. Professor Bein’s group actively contributes to interdisciplinary projects at the intersection of chemistry, physics, and biomedical engineering, with ongoing collaborations in solar energy, sustainable materials, and nanomedicine.
Robert Rohling is a Professor at the University of British Columbia's Faculty of Applied Science, affiliated with the Department of Mechanical Engineering and holding a joint appointment with the Department of Electrical and Computer Engineering. As Director of the Institute of Computing, Information and Cognitive Systems (ICICS), his research focuses on biomedical engineering, medical imaging, robotics, and computational methods. B.A.Sc. (UBC) M.Eng. (McGill) Ph.D. (Cambridge) Rohling's work spans three primary research areas: medical imaging (3D ultrasound, spatial compounding, elasticity reconstruction), medical information systems (radiologist navigation tools for large image datasets), and robotic calibration for surgical applications. His multidisciplinary approach integrates mechanical and electrical engineering principles with clinical needs. Rohling's publications (2020-2022) reveal trends in advanced ultrasound techniques (e.g., shear wave vibro-elastography), AI-driven image processing (cycleGAN translation), and computational optimization for diagnostic accuracy. Keywords across his work include Medical Imaging, Biomedical Engineering, Robotics, and Computational Modeling. As director of the Robotics and Control Laboratory , Rohling leads interdisciplinary collaborations with industry and clinical partners to address practical challenges in medical diagnostics and surgical robotics. His research emphasizes translating engineering innovations into clinical practice.
Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.
Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
