Shimon Whiteson is Professor of Computer Science at the University of Oxford, leading the Whiteson Research Lab focused on reinforcement learning, multi-agent systems, and deep learning. His research develops algorithms for efficient learning in complex environments. Current work explores meta-reinforcement learning frameworks that enable agents to rapidly adapt to new tasks, with applications in autonomous driving simulation and robotics. Recent innovations include novel methods for offline reinforcement learning, multi-agent coordination, and morphology-aware control. Publications demonstrate advances in: Meta-RL algorithm design for few-shot adaptation Multi-agent reinforcement learning environments and benchmarks Imitation learning in autonomous driving Bayesian methods for sample-efficient learning Research outputs include widely used benchmarks and tools including JaxMARL for accelerated multi-agent RL research. Current doctoral supervision focuses on temporal abstraction in RL, multi-agent coordination, and reinforcement learning theory.
Kristen Grauman is a Full Professor in the Department of Computer Science at the University of Texas at Austin, where she leads the UT Computer Vision Group. Her research focuses on computer vision and machine learning, with applications in visual recognition, video analysis, and multi-modal perception. She received her B.A. from Boston College and her Ph.D. from MIT. Her research interests span visual recognition, image and video search, video analysis, first-person vision, embodied and multi-modal perception, and interactive machine learning. She has made significant contributions to the field, particularly in developing algorithms for understanding visual content and human activities from video, including foundational work on the Pyramid Match Kernel and relative attributes. Her recent publications reveal a strong emphasis on egocentric (first-person) vision, audio-visual learning, and view-invariant representations. There is a clear trajectory toward multi-modal integration (vision, audio, language) and real-world applications in instructional videos, human activity understanding, and embodied AI systems. She has received numerous awards including: AAAI Fellow (2019) J. K. Aggarwal Prize, International Association for Pattern Recognition (2018) Helmholtz Prize (2017) UT Austin Academy of Distinguished Teachers (2017) Best Paper Award, Asian Conference on Computer Vision (2016) Presidential Early Career Award for Scientists and Engineers (2014) Computers and Thought Award, International Joint Conferences on Artificial Intelligence (2013) Pattern Analysis and Machine Intelligence Young Researcher Award (2013) Alfred P. Sloan Research Fellow (2012) Marr Prize (2011) Prof. Grauman serves as Associate Editor-in-Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. She has secured substantial research funding including the Presidential Early Career Award, NSF grants, and industry partnerships. Her advising has produced numerous influential publications and students who are now leaders in computer vision. She leads the UT Computer Vision Group, which collaborates closely with the Electrical and Computer Engineering Department. The group is pioneering large-scale egocentric video research through projects like Ego4D and Ego-Exo4D, focusing on real-world applications in human activity understanding, audio-visual perception, and interactive systems.
Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines
Simon Birrer is an Assistant Professor in Physics and Astronomy at Stony Brook University, specializing in cosmology and gravitational lensing. He holds a PhD from ETH Zurich (2016) and previously served as Kavli Fellow at Stanford University. Birrer leads research probing dark matter and dark energy using gravitational lensing phenomena. His group develops computational tools for analyzing strong gravitational lensing data to study cosmic expansion and dark matter distribution. Research areas include time-delay cosmography, Hubble constant measurements, and machine learning applications in astrophysics. Recent publications focus on multi-messenger gravitational lensing (2025), LSST survey applications (2025), and AI-powered lens modeling pipelines (2025). His work consistently addresses fundamental cosmological tensions like the Hubble constant discrepancy. Awards: Kavli Postdoctoral Fellowship (2019-2022) Kugelpyramide Lifetime Achievement Award Experimental Innovation Award (ETH Zurich) Research Group: Leads the SBU Strong Lensing group with 9+ graduate students and postdocs. The group participates in major collaborations including LSST Strong Lensing Science Collaboration (co-chair), LSST Dark Energy Science Collaboration, and TDCOSMO.
Harvey Reall is a Professor of Theoretical Physics at the University of Cambridge , affiliated with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and a Fellow of Trinity College . His research focuses on General Relativity and Effective Field Theory , particularly in the context of black hole mechanics , higher-dimensional gravity , and cosmic censorship . He has held prestigious positions including a Royal Society University Research Fellowship from 2005 to 2013. Education: PhD from DAMTP, University of Cambridge. Previous Appointments: Lecturer at the University of Nottingham (2005-2007); Postdoctoral positions at the Kavli Institute (2003-2005), Queen Mary University of London (2000-2003), and University of California, Santa Barbara (2003-2005). Reall's work explores the uniqueness and stability of black holes , causality in gravitational theories , and effective field theory approaches to gravity . His recent publications address nonperturbative second law formulations , event horizon dynamics , and axisymmetry theorems in extended theories of gravity. He has supervised numerous researchers including Aidan McSharry (2025-) , Maxime Gadioux (2022-) , and Iain Davies (2020-24) , contributing to the training of the next generation of physicists. Scientific Awards: Royal Society University Research Fellow (2005-2013)
Laur Järv is an Associate Professor in Theoretical Physics at the University of Tartu, Faculty of Science and Technology, Institute of Physics. He has been serving as Associate Professor since 2021 and is currently the Head of the Laboratory of Theoretical Physics (since 2019). His academic career at the University of Tartu spans over 20 years, with progressive roles from Post-Doc to his current position. Dr. Järv received his education at the University of Tartu (B.Sc. in Fundamental Physics, 1996; M.Sc. in Theoretical Physics, 1998) and completed his Ph.D. in Mathematical Sciences at the University of Durham in 2002. His doctoral research focused on "The enhancon mechanism in string theory" under the supervision of Clifford V Johnson. Dr. Järv's primary research interests lie in gravitational physics and cosmology, with particular focus on modified theories of gravity including teleparallel gravity, scalar-tensor theories, and nonmetricity-based approaches. His work explores the cosmological implications of these theories, including inflationary models, black hole solutions, and gravitational wave propagation. His research bridges theoretical physics with observational cosmology, addressing fundamental questions about the nature of gravity and the evolution of the universe. His publication record demonstrates a strong focus on geometric foundations of gravity, with numerous high-impact papers in leading journals like Physical Review D and Classical and Quantum Gravity. Recent work shows increasing emphasis on alternative formulations of gravity (teleparallel, symmetric teleparallel) and their cosmological applications, often collaborating with international researchers in the field. Estonian National Research Award in exact sciences (2020) for the cycle of works "Extended geometric theories of gravity" with Manuel Hohmann and Margus Saal University of Tartu Badge of Distinction (2021) Best teaching staff in the UT Institute of Physics, recognized by students (2024) Letter of recognition for supervision of Joosep Lember's award-winning student work (2022) Dr. Järv has been actively involved in academic mentoring, serving as a supervisor for student research projects and as an opponent for PhD defenses internationally. He has organized multiple international conferences on gravitational physics in Tartu, establishing the university as a hub for research in modified gravity theories. As Head of the Laboratory of Theoretical Physics, he leads a research group focused on geometric foundations of gravity and cosmological applications. Dr. Järv's laboratory has become a recognized center for research on alternative gravity theories, particularly through the organization of the biennial "Geometric Foundations of Gravity" conference series since 2017, which has attracted leading researchers from around the world to Tartu.
Gerhard Huisken is a Professor at the University of Tübingen and Director of the Mathematisches Forschungsinstitut Oberwolfach . His work spans Differential Geometry , Geometric Flows , and Mathematical Relativity . Education : Diploma (1982), PhD (1983), and Habilitation (1986) in Mathematics from Heidelberg University. His research focuses on geometric evolution equations, particularly mean curvature flow and inverse mean curvature flow , with applications to mathematical relativity and geometric inequalities . He has contributed to the understanding of singularities in curvature flows and developed surgical techniques for their analysis. His work on the Riemannian Penrose inequality and center of mass in isolated systems bridges geometry and physics. Selected publications highlight trends in geometric flows (mean curvature flow, Ricci flow), mathematical relativity (Penrose inequality, center of mass), and singularities in geometric PDEs. His collaborations with leading mathematicians like Simon Brendle and Tom Ilmanen reflect interdisciplinary impact. Scientific Awards and Honors : Fellow of the American Mathematical Society (2013) Clay Foundation Senior Fellowship (2013, 2007) Leibniz Preis from German Research Foundation (2003) Medal of the Australian Mathematical Society (1991) Member of the German Academy of Sciences Leopoldina (2004) He has held leadership roles including Dean of the Faculty of Mathematics at Tübingen University and directed major institutes like the Max Planck Institute for Gravitational Physics (2002-2013).
Thomas Faulkner is an Associate Professor in the Department of Physics at the University of Illinois at Urbana-Champaign, where he has been a faculty member since 2014. His research bridges condensed matter physics, high energy physics, and quantum information science through the framework of holographic duality (AdS/CFT correspondence), exploring connections between quantum field theories and gravitational theories. Dr. Faulkner received his BSc in Physics from the University of Melbourne in 2003 and his PhD from MIT in 2009 under Hong Liu and Krishna Rajagopal. He held postdoctoral positions at the Kavli Institute for Theoretical Physics (2009-2012) and the Institute for Advanced Study in Princeton (2012-2013) before joining the Illinois faculty. His primary research focuses on three interconnected areas: entanglement entropy as a tool to study quantum phases and gravity; string-inspired models of strongly correlated phenomena including non-Fermi liquids and quantum criticality; and holographic approaches to QCD under extreme conditions. His work leverages theoretical tools from both condensed matter and string theory communities to address fundamental questions in quantum gravity and many-body physics. Dr. Faulkner's publication record shows an evolving research trajectory from early work on strange metal transport and QCD applications toward increasingly sophisticated investigations of entanglement structure, quantum information aspects of holography, and fundamental constraints on quantum field theories. His recent work demonstrates deep connections between quantum information theory, gravitational physics, and condensed matter phenomena. DOE Early Career Award (2018) DARPA Young Faculty Award (2015) Dr. Faulkner has taught a comprehensive range of physics courses from undergraduate College Physics to advanced graduate-level field theory courses. His research program receives significant external funding, supporting his investigations into the quantum structure of spacetime and its connections to condensed matter phenomena. He participates in a vibrant research ecosystem exploring the quantum information foundations of spacetime geometry, contributing to collaborative efforts that are reshaping our understanding of the relationship between quantum mechanics and gravity.
Elahe Soltanaghai is an Assistant Professor in the Department of Computer Science and a Faculty Affiliate in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. She is also a 2022 NCSA Fellow and received her PhD in Computer Science from the University of Virginia (2019), MS in Computer Engineering from Sharif University of Technology (2014), and dual BS degrees in Computer and Information Technology Engineering from Amirkabir University of Technology (2011, 2013). PhD: University of Virginia, Computer Science, 2019 MS: Sharif University of Technology, Computer Engineering, 2014 BS (Computer Engineering): Amirkabir University of Technology, 2011 BS (Information Technology Engineering): Amirkabir University of Technology, 2013 Her research spans wireless sensing and communication, focusing on Millimeter-wave Radar Sensing (for automotive, mixed reality, structural monitoring), Machine Learning for Wireless Systems (adaptive sensing/communication), Forest IoT (through-canopy biomass and soil sensing), Metaverse Technologies (gaze-based VR/AR), and Low-Power Backscatter Communication (WiFi/power-line tags). She directs the Wireless, Sensing & Embedded Networked Systems (iSENS) Lab and co-directs the Illinois Center for IoT. Her work bridges wireless networking with cyber-physical sensing , emphasizing environmental monitoring (e.g., wildfire fuel detection via radar tags) and human-computer interaction (e.g., gaze-tracking in VR). Recent articles include innovations in passive radar profiling , through-canopy biomass characterization , and integrated communication-sensing protocols . Scientific Awards: Google Research Scholar Award (2022) N2Women Rising Star (2021) ACM SIGMOBILE Dissertation Award (2020) EECS Rising Stars (2019) NCSA Faculty Fellowship (2023) Best Demo Runner-up, IPSN (2023) Teaching Excellence Award (2023) Grants: NASA FireTech Program Grant (2025) NSF Grant for Radar-based Perception (2024) Insper-Illinois Grant for VR Research (2024) Keysight Research Gifts (2022, 2023) T-Mobile Research Gift (2022)
Professor Jorge Eduardo Pinto Santos holds the position of Professor of Theoretical Physics at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. His career includes roles as Reader (2018–2022), Lecturer (2013–2018), and a Junior Visiting Professorship at the Institute for Advanced Study in Princeton (2019–2020). He completed his Ph.D. at DAMTP between 2006 and 2010, followed by postdoctoral fellowships at Stanford University (2013–2014) and the University of California, Santa Barbara (2010–2013). His research focuses on general relativity, quantum gravity, and gravitational aspects of string theory. Key areas include numerical relativity and applications of general relativity to condensed matter physics via holography. Notable contributions involve studies on black hole binaries, extremal black holes, and the AdS/CFT correspondence. His recent work explores topics such as spinning black binaries in de Sitter space, gravitational instabilities in anti-de Sitter space, and entanglement islands in higher dimensions. He is affiliated with the High Energy Physics and Relativity and Gravitation research groups at DAMTP. His advising record includes seven doctoral students, with ongoing supervision of William Boyce (2024–present) and John Crump (2021–present). Publications highlight interdisciplinary advances, including investigations into black hole thermodynamics, cosmic censorship violations, and holographic models of condensed matter systems. His research bridges fundamental physics and numerical methods, addressing both theoretical and applied questions in gravitational physics.
Andre Wibisono serves as Assistant Professor in Yale University's Department of Computer Science with a secondary appointment in Statistics & Data Science, joining the faculty in 2021 after postdoctoral research at University of Wisconsin-Madison and Georgia Institute of Technology. His educational background includes: Ph.D. in Computer Science, UC Berkeley M.A. in Statistics, UC Berkeley M.Eng. in Computer Science, MIT S.B. in Mathematics and Computer Science, MIT Wibisono's research focuses on algorithm design for machine learning through optimization, sampling, and game theory , leveraging dynamical systems and information theory to develop accelerated discrete-time algorithms from continuous dynamics. His work provides theoretical foundations for efficient machine learning systems with applications in generative modeling and constrained optimization. Recent publications (2023-2025) demonstrate consistent innovation in Hamiltonian-based optimization , constrained-space sampling , and min-max game convergence , characterized by rigorous mathematical analysis connecting continuous dynamics to discrete algorithms. Key trends include randomized integration for acceleration, phi-divergence convergence guarantees, and symplectic geometry applications to mirror descent. Scientific recognition includes: NSF CAREER Award for developing algorithmic frameworks bridging continuous and discrete dynamics He actively mentors current students (Siddharth Mitra, Kaylee Yang, Jane Lee, Qiang Fu, Peter Wang) and has guided two postdocs to faculty positions. Research is funded through the NSF CAREER award and collaborative CIF grants focused on Hamiltonian dynamics for sampling and optimization. His Yale research group develops theoretical foundations for next-generation machine learning algorithms, emphasizing mathematical rigor in optimization and sampling with applications to generative modeling and constrained inference problems.
Emil Bjerrum-Bohr is an Associate Professor at the Niels Bohr Institute, University of Copenhagen, where he holds a position in the Theoretical high energy, astroparticle and gravitational physics department within the Faculty of Science. He is also affiliated with the Niels Bohr International Academy and leads the Computations of Amplitudes Group as a Lundbeck Foundation Junior Group Leader. Dr. Bjerrum-Bohr's research focuses on theoretical particle physics with particular emphasis on amplitude analysis and computations. His primary fields of research include amplitude analysis and computations, amplitudes and string theory, and quantum gravity. His current research explores relations between amplitudes from string theory and computation of amplitudes in Quantum Chromodynamics (QCD) for use at the Large Hadron Collider (LHC) at CERN. He has made significant contributions to understanding scattering equations and effective field theory in the context of gravitational physics. His recent publication record demonstrates a strong focus on gravitational scattering amplitudes, quantum gravity, and connections between string theory and particle physics. A notable trend in his research is the application of amplitude techniques to gravitational physics, particularly in the post-Minkowskian expansion framework which has implications for gravitational wave astronomy and black hole physics. His work bridges theoretical concepts with practical applications for collider physics. Scientific awards: Lundbeck Foundation Junior Group Leader As a Lundbeck Foundation Junior Group Leader, Dr. Bjerrum-Bohr oversees the Computations of Amplitudes Group, where he mentors junior researchers and collaborates with international colleagues on cutting-edge theoretical physics problems. His research has involved organizing academic meetings including the "Current Themes in High-Energy Physics and Cosmology" series (2013-2015) and Nordic Winter Schools on Cosmology and Particle Physics (2013, 2015). His work is conducted within the vibrant theoretical physics environment of the Niels Bohr Institute, which provides access to computational resources and collaborative opportunities with both experimental and theoretical physicists across multiple disciplines.
Aviad Levis is an Assistant Professor at the University of Toronto's Department of Computer Science, starting July 2024. He is affiliated with the Dunlap Astronomical Data Science and Technology Group (DADDAA) and collaborates with the Toronto Computational Imaging Group alongside Kyros Kutulakos and David Lindell. Previously, he was a postdoctoral researcher at Caltech's Computing + Mathematical Sciences department under Katherine Bouman, working with the Event Horizon Telescope (EHT) collaboration. PhD in Electrical Engineering from the Technion (supervised by Yoav Schechner) Research focuses on computational imaging tools at the intersection of AI and physics Develops algorithms for 3D tomography in both cloud physics and black hole imaging Recipient of ERC Synergy grant for CloudCT space mission His research spans two major domains: Computational Climate Imaging through cloud tomography to improve climate models, and Black Hole Imaging with the EHT collaboration. He pioneered methodologies for 3D cloud structure recovery using scattered sunlight and contributes to dynamic 3D reconstructions of black hole environments. Current interests include non-linear inverse problems, equation discovery from data, and ML-accelerated scientific simulations. Recent publications highlight advancements in atmospheric tomography and black hole emission modeling. His work on CloudCT involves coordinated nano-satellites for 3D cloud imaging, while EHT contributions include first images of Sagittarius A* (2022) and ongoing development of algorithms for 3D structure recovery. The ERC Synergy grant underscores his impact on climate imaging technology. Personal Website Work Email
Carlo Rovelli is a Professeur de classe exceptionnelle in the Department of Physics at Aix-Marseille University, holding adjunct roles at Western University's Department of Philosophy and a Distinguished Visiting Research Chair at the Perimeter Institute. He founded the quantum gravity group at the Centre de Physique Théorique (CPT) and is an associate member of the Rotman Institute of Philosophy. His research focuses on loop quantum gravity, relational quantum mechanics, and the history/philosophy of science. He authored influential popular science books including Seven Brief Lessons on Physics (41 languages, 1M+ copies sold) and Helgoland: Making Sense of the Quantum Revolution . Rovelli's work bridges theoretical physics with philosophical inquiry, exploring foundational questions in quantum mechanics, spacetime structure, and the interpretation of physical theories. His recent publications address topics like quantum information theory, gauge symmetries, black hole evaporation, and cosmological implications of quantum gravity.
Francesco Locatello is a tenure-track Assistant Professor at the Institute of Science and Technology Austria (ISTA), leading the Causal Learning and Artificial Intelligence lab. He is also an AI Resident at the Chan Zuckerberg Initiative. He holds a PhD from ETH Zürich, co-advised by Gunnar Rätsch and Bernhard Schölkopf. His research focuses on causal representation learning, score matching, and object-centric learning, with applications in machine learning and AI. His work has been recognized with prestigious awards, including the ICML 2019 Best Paper Award and the Hector Foundation Award (2023). Education: PhD in Machine Learning, ETH Zürich (advisors: Gunnar Rätsch, Bernhard Schölkopf) Research Interests: Causal Learning, Causal Representation Discovery, Score Matching Algorithms, Object-Centric Learning, Robust Generalization in AI, and Applications in Vision and Reinforcement Learning. Recent Work Trends: His publications emphasize causal mechanisms in neural representations, scalable causal discovery methods, and improving model generalization through latent space analysis. Recent studies explore geometric representations, mechanistic neural networks, and OOD detection using relative angles. Awards: ICML 2019 Best Paper Award Hector Foundation Award for Outstanding Achievements in Machine Learning (2023) Google Research Scholar Award (2024) Advising & Teams: Supervises a dynamic lab with students and postdocs across ISTA, ELLIS, and partner institutions. Notable advisees include Dingling Yao (ISTA), Riccardo Cadei (co-advised with Cordelia Schmid), and Marco Fumero (now a postdoc at ISTA). Collaborates with leading researchers like Arthur Gretton, Max Welling, and Volkan Cevher. Labs & Initiatives: Leads the Causal Learning and AI lab at ISTA, contributing to ELLIS programs and fostering interdisciplinary collaborations in causal AI.
