Andrew Stuart is the Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), joining in 2016. He previously held faculty positions at the University of Warwick (1999–2016), Stanford University (1992–1999), and Bath University (1989–1992). He earned his PhD from the University of Oxford's Computing Laboratory in 1986. Professor Stuart's research focuses on applied and computational mathematics , particularly Bayesian inverse problems , data assimilation for dynamical systems , and stochastic modeling . His work bridges mathematical theory, algorithm development, and applications in geophysics, materials science, and biological systems. His recent publications emphasize operator learning , machine learning for PDEs , and uncertainty quantification . Key areas include ensemble Kalman methods , Gaussian processes , and neural operators for solving and learning from complex systems. Scientific awards include the Vannevar Bush Faculty Fellowship and election to the Royal Society of Great Britain . He advises graduate students in applied mathematics, computational science, and geophysics, including Edoardo Calvello , Hojjat Kaveh , and Florian Wolf .
Ram Vasudevan is an Associate Professor and Associate Chair of Graduate Studies in the Department of Robotics at the University of Michigan. His research focuses on developing tools for safe and robust deployment of robotic systems, emphasizing optimization, nonlinear control, and real-world applications. Key areas include legged robot locomotion, shared control systems, and safety-critical autonomous systems. Research Interests: Optimization and control of nonlinear systems, locomotion of legged robots, shared control active safety systems, and automation of diagnostic/rehabilitative tasks. His ROAHM Lab prioritizes mathematical guarantees for robotic performance, with applications in medical robotics, autonomous vehicles, and soft robotics. Recent work emphasizes trajectory optimization, sensor fusion, and safety-aware control strategies. He has contributed to benchmarks for autonomous vehicle perception and novel methods in thermal image restoration using neural radiance fields. Awards: None explicitly listed in provided text. Labs/Teams: Directs the ROAHM Lab, collaborating on projects like robotic tail mechanics, real-time motion planning, and sensor data analysis. Active in academic conferences including RSS and ICRA.
Jon Simon is the Joan Reinhart Professor and Professor of Applied Physics at Stanford University . He leads the Simon Lab , which explores the convergence of condensed matter physics , quantum optics , and quantum information science , focusing on creating synthetic materials from light and investigating topological and strongly correlated quantum systems. His research spans constructing photonic materials in quantum circuits, studying small quantum systems with strong correlations, and applying Hamiltonian engineering to realize exotic states of matter. The lab has achieved milestones like the first Mott insulator of photons and topologically insulating circuits . Collaborative projects with the Schuster Lab leverage superconducting quantum circuits for synthetic matter studies. Jon's students include Adam Shaw (PhD, now at Stony Brook) Lavanya Taneja (PhD, now at Atom Computing) Ruichao Ma (Postdoc, now faculty at Purdue) among others. The lab's recent publications focus on cavity arrays, hybrid quantum systems, and topological photonics. Research is supported by grants and affiliations with Stanford's Department of Applied Physics and interdisciplinary institutes.
Matthew O'Toole is an Associate Professor at Carnegie Mellon University's School of Computer Science, holding joint appointments in the Robotics Institute and Computer Science Department. His research focuses on computational imaging, integrating optics, electronics, and computational processing to innovate visual information capture and display. Education: PhD (Computer Science, University of Toronto, 2016), MSc (2009), BSc (Honors Computer Science and Mathematics, University of British Columbia, 2007). Prior roles include Banting Postdoctoral Fellow at Stanford University and visiting scholar at MIT Media Lab's Camera Culture group. Research interests emphasize programmable imaging systems, transient imaging, non-line-of-sight sensing, and holographic displays. Key innovations include vibration sensing via dual-shutter optics and radar super-resolution for autonomous vehicles. Awards include runner-up best paper recognitions at ICCV 2007, CVPR 2014, and SIGGRAPH 2017 dissertation honors. Advisees include Dorian Chan and Arjun Teh. Grants supported by Canadian Banting Fellowships. Active in workshop organization (CVPR Computational Cameras 2016-2017) and course development on computational imaging at SIGGRAPH 2014. Labs/Teams: Leads research in computational imaging and robotics at CMU, collaborating with industry partners like NVIDIA and MDA. Current projects explore LiDAR-radar fusion, holographic projection systems, and dynamic scene reconstruction.
Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.
Mark Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with a primary appointment in the Whiting School of Engineering. He is also a Fellow of the Hopkins Extreme Materials Institute. His research focuses on developing ultrahigh-speed optical systems at the intersection of photonics and electronics, emphasizing photonic devices and information theory to advance imaging, sensing, and communications technologies. Applications include quantum-optical systems, ultrawide-bandwidth microwave photonics, and terahertz-rate imaging systems. Dr. Foster received his BS (2003), MS (2007), and PhD (2008) in Applied and Engineering Physics from Cornell University. Before joining Johns Hopkins in 2010, he served as a postdoctoral associate there. His work has been funded by the NSF, IARPA, DTRA, and NIH, resulting in over 200 publications and eight patents. He has held leadership roles, including chairing the IEEE Photonics Society’s Baltimore chapter (2011–2014). Research Highlights: World-leading imaging systems achieving terahertz frame rates Quantum-optical platforms and nonlinear photonic materials (e.g., NbTiOx) Secure authentication via physically unclonable functions (PUFs) Applications in fusion energy diagnostics and medical imaging His awards include the NSF CAREER Award (201?), DARPA Young Faculty Award, and ONR Young Investigator Award. Current projects explore machine learning-resistant PUFs, multi-modal imaging systems, and photonics for extreme environments.
Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.
Tengyu Ma is an Assistant Professor of Computer Science at Stanford University. His research focuses on machine learning, deep learning, optimization, and theoretical computer science. He is particularly known for work on neural networks, reinforcement learning, and algorithmic guarantees in AI systems. His email is tengyuma@stanford.edu . Ma's research interests span foundational aspects of machine learning, including generalization theory, optimization algorithms, and the theoretical underpinnings of deep learning. He has contributed to areas such as self-play theorem provers, learning rate schedules, and robustness in low-light vision tasks. His work often bridges theoretical insights with practical algorithm design. His recent publications emphasize advancements in large language models (LLMs), theorem proving via self-play, and understanding training dynamics in deep networks. Despite prolific output, no specific scientific awards are explicitly mentioned in the provided texts. Ongoing work includes exploring in-context learning mechanisms, formal verification of AI systems, and efficient pretraining techniques. His research has implications for both theoretical understanding and real-world applications of AI.
Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.
Jonathan Klamkin is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB). He also serves as the Director of the Nanofabrication Facility, overseeing advanced photonics fabrication resources. His research focuses on integrated photonics, silicon photonics, optical communications, and compound semiconductor integration. Klamkin holds a PhD in Materials from UCSB, an MS in Electrical and Computer Engineering from UCSB, and a BS in Electrical and Computer Engineering from Cornell University. His research interests span cutting-edge areas such as electronic-photonic integration, nanophotonics, and microwave photonics. He has pioneered techniques for heterogeneous integration of compound semiconductors on silicon, enabling scalable photonic systems for applications in LiDAR, high-speed communications, and quantum technologies. Notable awards include the DARPA Young Investigator Award, NASA Early Career Faculty Award, and the PIERS Young Scientist Award. His recent work emphasizes beam steering systems, high-power quantum dot lasers, and photonic integrated circuits for remote sensing and lidar. Klamkin’s lab develops both fundamental materials science and applied photonic devices, with a focus on bridging the gap between semiconductor growth and integrated system design. Key contributions include innovations in grating coupler design, antiphase boundary-free epitaxy for GaAs on silicon, and analog coherent detection for energy-efficient data centers. His research bridges photonics, electronics, and materials science to address challenges in high-performance integrated systems.
John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.
Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.
Di Zhu is a Presidential Young Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He holds a B.Eng. from Nanyang Technological University and M.Sc./Ph.D. from MIT, both in Electrical Engineering. His postdoctoral research at Harvard focused on lithium niobate integrated photonics and superconducting detectors. He previously worked as a research scientist and PI at A*STAR's Institute of Materials Research and Engineering (IMRE). Research Interests : Integrated quantum photonics, superconducting detectors, nonlinear optics, and nanofabrication. His group develops scalable quantum photonic devices using lithium niobate and superconducting materials, emphasizing applications in quantum computing, communication, and sensing. Awards : National Research Foundation (NRF) Fellowship Harvard Quantum Initiative (HQI) Postdoctoral Fellowship MIT Jin-Au Kong Thesis Award Advising & Recruitment : Actively recruiting postdocs, PhD students, and interns in areas like integrated photonics, quantum optics, and superconducting detectors. Positions include work on thin-film lithium niobate, quantum simulation, and microwave-optical transduction. Group website: dizhulab.org .
Aleksei Zheltikov is a University Distinguished Professor at Texas A&M University's Department of Physics and Astronomy. He holds dual affiliations with the International Laser Center and Physics Department of M.V. Lomonosov Moscow State University, and the Russian Quantum Center. His research focuses on ultrafast nonlinear optics and biophotonics, addressing applications in imaging, laser filamentation, and strong-field physics. Zheltikov earned his PhD (1990) and Doctor of Science (1999) degrees from Moscow State University, becoming a full professor there in 2000 before joining Texas A&M in 2010. He leads a research team including Xinghua Liu and Ajithamithra Dharmasiri. Recipient of prestigious awards including the Russian Federation State Prize (1997), Lamb Award (2010), and Kurchatov Prize (2014), his work bridges fundamental optics research with medical diagnostics and quantum technologies. Key contributions include developing laser filament-based imaging techniques and advancing Raman scattering-based frequency conversion methods in hollow-core fibers.
Professor Stefan Maier holds the position of Head of School in Physics and Astronomy at Monash University. Previously, he served as the Lee Lucas Chair in Experimental Physics at Imperial College London (2007–2018) and built a new chair at Ludwig-Maximilians-Universität München (2019–2022). His research focuses on nanophotonics, plasmonics, and metasurface engineering, with emphasis on optical trapping, nonlinear optics, and novel photonic devices. Education: Bachelor’s degree in Physics, Technical University of Munich M.Sc. and Ph.D. in Applied Physics, California Institute of Technology (Caltech) Research Interests: Development of metamaterials and metasurfaces for light manipulation Applications of nanophotonics in sensing, imaging, and quantum technologies Optical trapping and plasmonic catalysis Nonlinear optical phenomena in nanostructured materials Articles Trends: Recent work emphasizes bound states in the continuum (BICs), 3D nanoprinted optical platforms, and active metasurfaces with tunable properties. Key themes include hybrid nanophotonics, ultra-high-Q resonators, and plasmonic nanomaterials for energy applications. Awards: ISI Highly Cited Researcher (2017–present) Grants/Projects: Chief Investigator in the All-on-chip twisted light modulator project (2022–2025) Leadership in Monash’s nanophotonics research team Labs/Teams: Directs a multidisciplinary lab at Monash focused on integrating 3D nanofabrication with optical physics, including collaborations in metafiber development and plasmonic biosensing.
