California Institute of Technology (Caltech)United States
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 .
California Institute of Technology (Caltech)United States
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.
California Institute of Technology (Caltech)United States
Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science
Massachusetts Institute of TechnologyUnited States
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.
Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.
Massachusetts Institute of TechnologyUnited States
Professor Michael S. Triantafyllou serves as the Henry L. and Grace Doherty Professor in Ocean Science and Engineering and Professor of Mechanical and Ocean Engineering at the Massachusetts Institute of Technology. He also directs MIT Sea Grant, a significant research center focused on ocean-related science and engineering. His work bridges multiple disciplines within mechanical and ocean engineering, with a strong emphasis on biomimetic approaches to underwater systems. Triantafyllou's research focuses on biomimetic ocean robots and sensors, flow-structure interaction, and the dynamics and control of ocean vehicles. His work in experimental fluid mechanics has led to groundbreaking developments in understanding how marine animals move and sense their environment, which he applies to create innovative underwater technologies. His research spans from fundamental fluid dynamics to practical applications in underwater robotics and sensing systems. His publications reveal a strong emphasis on vortex-induced vibrations, biomimetic sensing inspired by marine animals (particularly harbor seals and fish), and the development of novel underwater propulsion systems. His research shows consistent innovation in applying biological principles to engineering solutions for underwater vehicles and sensing systems. Fellow, American Physical Society, 2014 Aurel Stodola Medal and Lecture 'Biomimetic survival hydrodynamics and sensing', ETH Zurich, May 2014 William I. Koch Professor of Marine Technology (2008 - 2017) Discover Magazine Awards for Technological Innovation (1998) Work on Robotic Tuna featured on cover of Scientific American (March 1995) As Director of MIT Sea Grant and previously as Director of the Center for Ocean Engineering (2005-2017), Triantafyllou has led significant research initiatives and secured substantial funding for ocean-related research. His work with the Towing Tank Facility (which he has directed since 1988) and Propeller Tunnel Facility has supported numerous research projects in marine hydrodynamics. Triantafyllou's laboratory work focuses on biomimetic ocean technologies, particularly robotic systems inspired by marine animals. His famous RoboTuna project has been exhibited at major museums worldwide and represents a landmark achievement in biomimetic underwater robotics.
California Institute of Technology (Caltech)United States
Alexei Kitaev is the Ronald and Maxine Linde Professor of Theoretical Physics and Mathematics at the California Institute of Technology (Caltech). His research focuses on quantum computation, topological quantum phases, anyons, topological insulators and superconductors, and the black hole information paradox. He has pioneered the concept of topological quantum computation, where quantum information is protected through topological properties of many-body systems. His recent publications explore quantum error correction, scrambling dynamics, and holographic principles in SYK-like models, reflecting his interdisciplinary impact on quantum physics, computer science, and condensed matter. His work on the Sachdev-Ye-Kitaev model has advanced understanding of quantum chaos and gravitational phenomena. Kitaev has received numerous accolades, including the MacArthur Award (2008), Breakthrough Prize in Fundamental Physics (2012), Dirac Medal (2015), and Oliver Buckley Condensed Matter Prize (2017). He has taught advanced courses such as 'Quantum Computation' and 'Advanced Condensed-Matter Physics' at Caltech. Scientific Awards: MacArthur Award (2008) Breakthrough Prize in Fundamental Physics (2012) Dirac Medal (2015) Oliver Buckley Condensed Matter Prize (2017)
Holger Fröning is a full professor at Heidelberg University’s Institute of Computer Engineering (ZITI), where he leads the Hardware and Artificial Intelligence (HAWAII) Lab. His research focuses on embedded machine learning , high-performance computing , and hardware-software co-design , with emphasis on resource efficiency, power optimization, and emerging architectures like analog , photonic , and resistive memory systems. He has held leadership roles including Managing Director of ZITI (2023–present) and Dean of Studies for Computer Science (2019–2022) , and has collaborated with institutions such as NVIDIA Research, Chinese Academy of Sciences, and Graz University of Technology. Research Trends : His recent publications explore Bayesian neural networks , green machine learning , analog computing noise mitigation , and GPU/FPGA optimization . Articles highlight photonic computing for AI , memory-efficient training , and hardware-aware DNN compression . Scientific Awards : 2025 HiPEAC Paper Award (Nature Computational Science) 2014 Google Faculty Research Award Multiple Best Paper Awards (IPDPS, ICPP, ECML-PKDD workshops) Leadership & Service : Organized workshops (WEML, ITEM, F4HD), chaired tracks at EuroPar and ISC, and served on program committees for ICPR, ECAI, and FPL. Education & Affiliations : PhD and MSc from University of Mannheim (2007/2001). Sponsors include DFG, FWF, FFG, NVIDIA, SAP, and XILINX.
Prof. Igors Gorbovickis is an Associate Professor of Mathematics at the Department of Mathematics, School of Computer Science and Engineering, Constructor University (formerly Jacobs University Bremen). His research focuses on complex dynamical systems, including topics such as renormalization theory, bifurcation analysis, Julia sets, and applications to mathematical physics. He also contributes to discrete geometry, particularly exploring conjectures like the Kneser-Poulsen problem. His work bridges pure mathematics with interdisciplinary applications, emphasizing rigorous analysis of nonlinear systems and geometric configurations. Key areas of investigation include critical point accumulations, Hausdorff dimension estimates, and equidistribution phenomena in parameter spaces. Recent publications highlight advancements in understanding chaotic systems, circle maps, and the interplay between algebraic structures and dynamical behavior. Prof. Gorbovickis collaborates internationally, with co-authored papers appearing in journals like Advances in Mathematics , Ergodic Theory and Dynamical Systems , and Nonlinearity . His office is located at Research I, Room 128 on the Constructor University campus in Bremen, Germany.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Scientia Professor Gary Froyland is a Professor at the University of New South Wales (UNSW), affiliated with the School of Mathematics & Statistics. He leads the ARC Laureate Centre for Dynamical Systems and Data and holds an Einstein Visiting Fellowship from the Einstein Foundation Berlin. His academic credentials include a BSc (Hons 1, Medal) in Pure and Applied Mathematics from the University of Queensland and a PhD in Mathematics from the University of Western Australia. Professor Froyland's research spans two primary domains: dynamical systems and optimization. In dynamical systems, he investigates the interplay of probability and geometry in nonlinear and chaotic systems, employing tools from ergodic theory, functional analysis, and differential geometry. His work extends to applications in oceanography, atmospheric science, and granular flows. In optimization, he focuses on decision-making in complex systems with uncertain information, developing novel approaches in mathematical programming that have been applied to mining, logistics, and medical treatment planning. His recent publications demonstrate a strong focus on coherent structures in dynamical systems, linear response theory, and applications to geophysical phenomena. The research shows increasing interdisciplinary collaboration, particularly with climate scientists and data analysts, reflecting a trend toward applying advanced mathematical techniques to real-world problems in environmental science and engineering. J.D. Crawford Prize (2025) Elected Member of the Academy of Europe / Academia Europaea (2024) ARC Laureate Fellow (2024-2029) Fellow of the Society for Industrial and Applied Mathematics (SIAM) (2021) Fellow of the Australian Academy of Science (2020) Vice-Chancellor's Award for Teaching Excellence - Postgraduate Research Supervision (2015) Professor Froyland actively supervises PhD and honors students, with current advisees including Kevin Felipe Kühl Oliveira, Nicholas Peters, and Kathrin Völkner. His research is supported by multiple grants, including an ARC Laureate Fellowship (2024-2029) for "Breakthrough mathematics for dynamical systems and data," an Einstein Visiting Fellowship (2022-2026), and several ARC Discovery Projects. His work has practical applications in climate science, mining optimization, and medical treatment planning, particularly in radiotherapy. He leads the ARC Laureate Centre for Dynamical Systems and Data, which brings together researchers to develop new mathematical approaches for analyzing complex dynamical systems. The center focuses on creating methods to identify coherent structures in spatiotemporal data, with applications spanning environmental science, social science, health science, and engineering.
Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.
Alex Blumenthal is an Assistant Professor in the School of Mathematics at the Georgia Institute of Technology since Fall 2020. His academic background includes a Ph.D. from New York University (2016) with a dissertation titled 'Nonuniformly hyperbolic theory for Banach space mappings.' Prior to joining Georgia Tech, he held positions as an instructor at the University of Maryland, teaching courses in probability theory, linear algebra, and precalculus, and served as a recitation leader at New York University for courses in chaos theory, differential equations, and analysis. Blumenthal's research focuses on dynamical systems and ergodic theory, with specialization in: Chaotic behavior in deterministic and stochastic systems Smooth ergodic theory and SRB measures Lyapunov exponents in random dynamical systems Stochastic fluid mechanics and turbulence modeling Infinite-dimensional dynamical systems on Banach spaces Statistical properties of complex systems His work bridges abstract mathematical theory with physical applications like fluid dynamics and statistical mechanics. Analysis of his recent publications shows strong emphasis on stochastic dynamics, Lyapunov exponents, and fluid mechanical systems, with mathematical techniques drawn from ergodic theory, functional analysis, and probability theory. His publications frequently appear in top mathematical physics and dynamics journals. No scientific awards or honors are mentioned in the source materials. Similarly, no information is available regarding research grants, student advising, or laboratory affiliations.
Scott Kerlin is a Senior Lecturer in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds an M.S. and B.S. in Computer Science from the University of North Dakota. Prior to academia, he worked at the Mayo Clinic on medical software systems and IBM as a build master for enterprise products. His career spans roles including network administrator, lab manager, and Undergraduate Director at UND. Dr. Kerlin's research focuses on bridging industry experience with academic curriculum, particularly in computer science education, project-based learning, and cybersecurity. He emphasizes practical applications of theoretical concepts, such as integrating 3D printing and scanning technologies with security systems for small satellites. His work also explores student efficacy in AI courses and scalable software project management methodologies. He has taught at multiple institutions, including the University of Minnesota and Augsburg University, and held roles at Michigan Tech as Senior Security Engineer. His 2024 Engineering+ Outstanding Teaching Award highlights his commitment to pedagogical innovation. Current projects include in-space 3D printing, solar energy systems, and cryptographic solutions for satellite communications. Key areas of contribution include: 3D printing/Scanning: Material characterization, key replication, and aerospace applications Cybersecurity: Intrusion detection, satellite communications security, and chaotic cryptosystems Educational Innovation: Active learning frameworks, PBL implementation, and student performance modeling His interdisciplinary approach connects computer science fundamentals with real-world engineering challenges, emphasizing sustainability and industry relevance in curricula.
