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 .
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.
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.
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.
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.
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.
Christiana Mavroyiakoumou is a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences, New York University. She specializes in fluid dynamics and fluid-structure interactions, with a focus on vortex dynamics, membrane flutter, and bio-inspired systems. Her research integrates modeling, numerical simulations, and experimental insights to study phenomena such as bird flock formations and fish swimming hydrodynamics. Mavroyiakoumou holds a Ph.D. from the University of Michigan (2022), an M.Sc. from the University of Oxford (2017), and a B.Sc. from Imperial College London (2016). Education: PhD in Applied & Interdisciplinary Mathematics, University of Michigan (2017–2022) MSc in Mathematical Modeling and Scientific Computing, University of Oxford (2016–2017) BSc in Mathematics, Imperial College London (2013–2016) Her research interests span fluid-structure interactions, vortex dynamics, and collective locomotion. She investigates how fluid flows mediate interactions between bodies, such as the aerodynamics of bird formations and the hydrodynamics of flapping foils. Her work bridges theoretical models with experimental observations, contributing to both fundamental science and bio-inspired engineering. Mavroyiakoumou has received prestigious awards including the Joseph B. Keller Fellowship (NYU), Peter Smereka Award (U-M), and ProQuest Distinguished Dissertation (U-M). She actively engages in academic service, organizing conferences and mentoring students. Her teaching experience includes courses on mathematical modeling, differential equations, and algebra at NYU and the University of Michigan. Key Research Themes: Flow-mediated collective behavior and instability mechanisms Vortex wake interactions and their role in locomotion Membrane dynamics in inviscid and viscous flows She collaborates with experimentalists like Leif Ristroph and Jun Zhang at NYU's Applied Math Lab, focusing on experimental validation of theoretical models. Her recent work explores self-amplifying waves in bird formations and the aerodynamic origins of flight coordination.
Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.
Jeffrey Guasto , Associate Professor at Tufts University, holds joint appointments in the School of Engineering (Mechanical Engineering) and School of Arts and Sciences (Physics & Astronomy). His work bridges engineering, physics, and biology to study transport properties in complex systems. Ph.D., Engineering (2009), Brown University Sc.M., Engineering (2004), Brown University Dual B.S. in Physics and Mechanical Engineering (2003), Lehigh University Research Interests focus on: Biophysics : Flagellar mechanics, chemotaxis, cell-fluid interactions Soft Matter : Active suspensions, colloids, viscoelastic materials Microfluidics : Device design for cell motility studies and gradient generation Environmental Transport : Microbial ecology in porous systems Scientific Trends from his 77+ publications show emphasis on microscale fluid dynamics, bacterial transport mechanisms, and viscoelastic flow instabilities. His 2024 Nature Microbiology work reveals phage-infected bacteria driving marine chemotaxis, while 2023 PNAS research explores stress topology in viscoelastic flows. Scientific Awards : NSF CAREER Award (2016) for cell dispersal mechanisms Collaborative NSF grants (2015-2023) Advising includes mentoring 15+ students and postdocs. His grants portfolio features 9+ awards, notably NSF grants for viral-microbe interactions (2018) and flagellar mechanics (2020). Labs & Teams : Leads the Guasto Laboratory at Tufts, integrating microfluidics and high-speed imaging for studying microbial transport, while collaborating with MIT, Harvard, and international institutions.
Adrián Lozano-Durán is an Associate Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Guggenheim Laboratory for Aeronautics (GALCIT). He holds a B.S., M.S., and Ph.D. from the Polytechnic University of Madrid (2010–2015) and joined Caltech as a Visiting Associate in 2024 before becoming a faculty member in the same year. His research focuses on fluid dynamics, turbulence, and machine learning applications in computational fluid dynamics (CFD), particularly for aerospace systems. He leads the Aerofluids, Learning & Discovery (ALD) Lab, collaborating with MIT’s AeroAstro department. Key research areas include causal inference in fluid systems, reduced-order modeling, and machine-learning-based closure models for large-eddy simulation (LES). His work addresses challenges in low-speed aerodynamics, supersonic, and hypersonic flows. Notable recent contributions include advancements in LES wall models and information-theoretic approaches to turbulence control. He frequently presents at international conferences and has co-authored high-impact papers in Nature Communications , Journal of Fluid Mechanics , and Physical Review Research . Education: B.S., Polytechnic University of Madrid (2010) M.S., Polytechnic University of Madrid (2012) Ph.D., Polytechnic University of Madrid (2015) Affiliations: GALCIT, Caltech AeroAstro, MIT (collaboration) Advising focuses on students like Álvaro Martínez-Sánchez and Tristan, whose work spans causality in turbulence and flow control. He actively engages in interdisciplinary research, bridging fluid mechanics with machine learning and information theory to advance aerospace engineering solutions.
Rishidev Chaudhuri is an Associate Professor at the University of California, Davis in the Department of Neurobiology, Physiology and Behavior within the College of Biological Sciences. His research focuses on computational neuroscience and neural dynamics, employing mathematical models to investigate how neural circuits generate cognitive processes such as memory, perception, and decision-making. His work explores neural dynamics through models of memory systems, attentional mechanisms, and probabilistic inference. Recent publications highlight advances in understanding hippocampal memory scaffolds, parietal-frontal interactions, and neuromorphic computing inspired by brain architecture. Education: BA in Physics (Amherst College), PhD in Applied Mathematics (Yale University) Centers: Center for Neuroscience; affiliated with Applied Mathematics and Neuroscience Graduate Groups Scientific awards and honors are not explicitly mentioned in the provided materials.
