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 .
Johnny Guzmán is a Professor of Applied Mathematics at Brown University, specializing in numerical analysis of partial differential equations and scientific computing. He holds a Ph.D. in Applied Mathematics from Cornell University (2005) and a B.S. in Mathematics from California State University, Long Beach (1999). His research focuses on numerical methods for PDEs, including discontinuous Galerkin methods, mixed finite element methods, and fluid-structure interaction problems. Key contributions include work on hybridizable and mixed finite element methods, discontinuous Galerkin discretizations, and stability analysis of numerical schemes. He has been funded by multiple NSF grants, including a Postdoctoral Fellowship (2005–2008) and awards totaling over $1M in research support. Notable recognitions include the Comfort and Urry Family Fund Prize (2013). Guzmán collaborates with institutions globally and serves on editorial boards for journals like Journal of Numerical Mathematics and Calcolo . His teaching spans computational linear algebra, numerical methods for differential equations, and finite element analysis.
Jonathan Winghong Luk is a Professor in the Department of Mathematics at Stanford University. His research focuses on nonlinear partial differential equations, general relativity, and mathematical physics, with a particular emphasis on gravitational wave dynamics, shock formation, and high-frequency spacetime solutions. Contact: Email: jluk@stanford.edu Office: 382-Z, Building 380, Stanford, CA 94305 Research Trends: His recent publications examine nonlinear wave equations on dynamic spacetimes, gravitational phase mixing, impulsive gravitational wave interactions, and stability of black hole interiors. He frequently collaborates with experts like C. Huneau, S.-J. Oh, and J. Speck. Academic Activities: Luk organizes the Analysis and PDE seminar at Stanford with Eugenia Malinnikova and Ryan Unger. He has developed lecture notes on nonlinear wave equations and Fourier analysis, complemented by example sheets.
PD Dr. Christian Zillinger is a researcher at the Karlsruhe Institute of Technology (KIT), specifically within the Department of Mathematics. He leads the Junior Research Group "Stability and Instability in Fluids and Materials" (AP6) as part of the CRC 1173. His office is located at Kollegiengebäude Mathematik (20.30), room 2.024 in Karlsruhe, Germany. Dr. Zillinger obtained his PhD under the supervision of Herbert Koch at the University of Bonn. Following his doctorate, he served as an assistant professor (NTT) at the University of Southern California and was a postdoctoral fellow at BCAM (Basque Center for Applied Mathematics). He recently completed his habilitation thesis titled "On Mixing and Resonances in Fluid Systems" at KIT in 2023. Dr. Zillinger's research focuses on partial differential equations motivated by physical problems, particularly in fluid dynamics and material sciences. His work encompasses several key areas: Mixing as a (de)stabilizing mechanism in fluids and inviscid damping Cascades of resonances and instabilities in fluids and plasmas Convex integration and microstructures in materials, including rigidity and flexibility phenomena Magnetic fluids and magnetohydrodynamics Partial dissipation in the Boussinesq equations His recent publications demonstrate a strong focus on stability and instability phenomena in fluid systems, with particular attention to mathematical analysis of PDEs governing fluid behavior. He has made significant contributions to understanding echo chains, resonance phenomena, and damping mechanisms in various fluid models. His work bridges theoretical mathematics with applications in physics and materials science, often employing advanced analytical techniques to address challenging problems in nonlinear PDEs. Dr. Zillinger actively teaches courses at KIT, including "Klassische Methoden für partielle Differentialgleichungen" (Classical Methods for Partial Differential Equations), "Introduction to convex integration," "Introduction to Kinetic Equations," and seminars on microstructure in materials and fluid dynamics. He leads the Junior Research Group "Stability and Instability in Fluids and Materials" which is part of the Collaborative Research Centre (CRC) 1173 at KIT, focusing on wave phenomena. This research group investigates mathematical aspects of stability and instability in physical systems, with applications to fluid dynamics and material science.
Kai Xu is a Morrey Visiting Assistant Professor in the Mathematics department at the University of California, Berkeley, mentored by Richard Bamler. Appointed in 2025, he holds a PhD from Duke University supervised by Hubert Bray. His research addresses foundational problems at the intersection of differential geometry and analysis. His educational background includes: PhD in Mathematics, Duke University (2025), supervised by Hubert Bray Xu's research spans geometric analysis, calculus of variations, and metric geometry with concentrated focus on 3D scalar curvature geometry, weak inverse mean curvature flow, nonlinear potential theory (p-harmonic functions for $1 \leq p \leq \infty$), and spectral Ricci curvature bounds. His work systematically explores connections between curvature constraints, topological properties, and geometric flows through rigorous analytical methods. His publication record (2022-2025) reveals consistent advancement in scalar curvature theory, inverse mean curvature flow, and spectral Ricci geometry. Key contributions include spectral splitting theorems, drawstring constructions for scalar curvature constraints, and topological gap theorems for positive scalar curvature 3-manifolds. His collaborative work with leading mathematicians appears in journals including Duke Mathematical Journal and Calculus of Variations and Partial Differential Equations. No scientific awards are mentioned in the provided text. Teaching responsibilities include Math 104 in Fall 2025. Information regarding student advising and grant funding is not specified in available materials. No dedicated laboratory or research team structure is described in the source text.
Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
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.
University of Illinois Urbana-ChampaignUnited States
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.
Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.
John Evans is an Associate Professor and Jack Rominger Faculty Fellow in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Applied Mathematics program. He serves as Associate Chair for Undergraduate Curriculum and is part of the Aerospace Mechanics Research Center (AMREC). His research focuses on computational mechanics, particularly fluid dynamics, fluid-structure interaction, and turbulence modeling using high-order and structure-preserving methods. Evans holds a PhD (2011) and MS (2008) in Computational and Applied Mathematics from the University of Texas at Austin, and dual BS/MS degrees in Mathematics and Applied Mathematics from Rensselaer Polytechnic Institute (2006). Before joining CU Boulder, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences (ICES). His research interests include isogeometric analysis, immersed methods, and data-driven turbulence modeling. Notable contributions include development of divergence-conforming discretizations for incompressible flows, stabilized collocation methods, and invariant subgrid stress models. He leads the AMREC lab and collaborates on plasma-fueled propulsion systems and geometrically sensitive simulations. Key Awards: 2021: Rocky Mountain AIAA Educator of the Year 2021: Gallagher Young Investigator Medal 2019-2021: Clarivate Highly Cited Researcher Professional Activities: Editor of Engineering Computations, Senior AIAA Member, Simons Visiting Professor (2019) Evans' work bridges advanced numerical methods with real-world engineering challenges. His lab develops open-source tools like XIGA for multi-material problems and focuses on immersive simulation environments. Current projects explore turbulence closure models, plasma propulsion, and topology optimization with B-spline-based approaches.
Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling
Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University, specializing in applied mathematics, numerical analysis, and computational methods. His research focuses on numerical methods for partial differential equations, fluid mechanics, interface problems, and computer graphics. He holds a PhD from UCSB (2004) and has held academic positions at MIT and McGill since 2005. Currently, he serves on committees such as the Steering Committee of the Institut des Sciences Mathematiques and the CRM Applied Mathematics Lab. His educational background includes a PhD under Professors Xu-Dong Liu and Sanjoy Banerjee. Key research areas include level set methods, fluid-structure interaction, and invariant numerical methods. Notable works include the Correction Function Method for interface problems and the Characteristic Mapping Method for advection problems. Nave’s publications span topics like Poisson equations with discontinuous coefficients, fluid dynamics simulations, and high-order numerical schemes. He has advised numerous graduate and undergraduate students, contributing to their research in applied mathematics and computational science. His work bridges theoretical rigor and practical applications in engineering and physics. He teaches advanced courses such as Numerical Analysis I/II and Computational Methods in Applied Mathematics. His research group collaborates on projects involving fluid dynamics, elasticity, and geometric algorithms, with a focus on developing robust numerical tools for complex systems.
Mohamed Amara is a full-time Professor at the University of Pau and the Pays de l'Adour (UPPA) since 1996, affiliated with the Laboratory of Mathematics and their Applications (CNRS-UMR 5142). He served as its director (1999-2007), Director of the Doctoral School of Exact Sciences (ED211, 2007-2008), and UPPA's Scientific Council Vice-President (2008-2012). He has been UPPA's President since 2012 (re-elected until 2020). Education: Mathematics from University of Algiers (1973), Pierre and Marie Curie University (DEA 1974, Doctorate 1978, State Doctorate 1983) Academic Roles: Research Associate at Ecole Polytechnique (1978-1982), Algerian Electricity and Gas Company (1983-1992), Professor in Algiers (1988-1994), Tunis (1994-1995), and Associate Professor at Paris 6 (1995-1996) His research focuses on numerical simulation of partial differential equations for environmental/energy applications, including mechanics in porous media (petroleum engineering, geoscience), fluid mechanics (aerodynamics, estuarine hydrodynamics), non-Newtonian flows, and wave propagation. Articles highlight expertise in discontinuous Galerkin methods, Helmholtz problems, finite element discretization, and multiphysics systems. He managed 20 doctoral theses and led national mathematics programs at ANR (2007-2011). He chairs the Cocktail association for higher education IT systems and collaborates with INRIA's Magique 3D team (since 2006).
Manuel Del Pino is Professor at the University of Bath's Department of Mathematical Sciences and Royal Society Professor specializing in nonlinear partial differential equations. His research focuses on singularity formation, geometric evolution equations, and asymptotic analysis in fluid dynamics and mathematical physics. His investigations encompass blow-up phenomena in heat equations, vortex dynamics in Euler flows, and minimal surface theory. Current projects examine infinite-time singularity formation in parabolic equations and asymptotic properties of vortex configurations. Del Pino has received the Royal Society Professorship and leads multiple grants including 'Asymptotic patterns in nonlinear evolution problems' (EPSRC). He maintains collaborations with researchers globally through projects on singularity formation in PDEs.
John Baillieul is Distinguished Professor at Boston University with joint appointments in Mechanical Engineering, Systems Engineering and Electrical & Computer Engineering. He directs experimental laboratories for real-time control of lightweight robotic systems and applies nonlinear control theory to complex multi-body, networked, and bio-inspired systems. Education: Ph.D., Harvard University Research Interests: Baillieul’s work spans robotics, nonlinear control, and networked systems. Early contributions resolved motion-planning for kinematically redundant manipulators; current themes include neuromimetic learning, vision-based navigation, and resilience of infrastructure networks such as power grids. His group couples rigorous geometric control with real-time hardware to create lightweight, high-performance robots and to uncover fundamental information limits in feedback systems. Recent Publication Trends (2021-2025): Over the past five years his output has concentrated on three synergistic directions: (i) neuromimetic and Koopman-based data-driven methods for estimating and controlling nonlinear systems, (ii) vision-based guidance and sparse optical-flow primitives for agile autonomous flight, and (iii) network-theoretic decomposition and information-rate studies for resilient operation of power grids and collective dynamics. Honors & Awards: IEEE Fellow 2025 Roger W. Brockett Control Systems Award Former Editor-in-Chief, IEEE Transactions on Automatic Control Affiliations & Service: He is a member of Boston University’s Center for Information and Systems Engineering (CISE), has served as Editor-in-Chief of IEEE Transactions on Automatic Control, and remains active in editorial and organizational roles across the IEEE control systems community.
