Dond Asha Kisan is an Assistant Professor at the School of Mathematics , Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM). His research focuses on numerical analysis and computational mathematics , particularly in finite element methods for partial differential equations. He can be contacted at ashadond@iisertvm.ac.in or via phone at +91 (0)471-2778247. PhD : Mathematics, Indian Institute of Technology Bombay M.Sc. : Mathematics, K.T.H.M. College, Nashik Kisan's research spans adaptive finite element methods , stabilized formulations for convection-diffusion problems , and optimal control governed by Stokes equations . His work includes convergence analysis, nonconforming discretizations, and hybrid numerical schemes. Recent publications (2023-2025) address stochastic modeling in liquid crystal physics, advanced WENO schemes, and adaptive algorithms for control problems. Scientific Awards : No explicit awards mentioned in the data, though he held prestigious postdoctoral fellowships including National Post-Doctoral Fellowship and NBHM Post-Doctoral Fellowship. Kisan has extensive teaching experience , including MATLAB workshops and undergraduate course assistantships. He has presented at major international conferences like ICIAM and Hyperbolic Problems, demonstrating global engagement in computational mathematics.
Thomas Yizhao Hou is the Charles Lee Powell Professor of Applied and Computational Mathematics at the California Institute of Technology, where he has served as a faculty member since 1998 and as Executive Officer of Applied and Computational Mathematics from 2000-2006. His research spans fundamental mathematical problems with significant implications for fluid dynamics and computational science. Hou received his B.S. in Mathematics from South China University of Technology in 1982, followed by an M.S. in 1985 and Ph.D. in 1987 from UCLA under the supervision of Prof. Bjorn Engquist. His academic journey includes positions at the Courant Institute and the Institute for Advanced Study before joining Caltech. Hou's research focuses on multiscale analysis and computation, interfacial problems, stochastic PDEs and uncertainty quantification, and the Millennium Problem concerning global regularity of 3D incompressible Euler and Navier-Stokes equations. His work on adaptive data analysis has led to significant methodological innovations. His research is characterized by the integration of rigorous mathematical analysis with computational approaches to tackle problems that have resisted traditional methods. His recent publications reveal a consistent focus on singularity formation in fluid equations, particularly the Euler and Navier-Stokes equations, with increasing sophistication in analyzing potential blowup scenarios. His work spans theoretical analysis, numerical verification, and the development of innovative mathematical frameworks for multiscale problems. Member of the National Academy of Sciences (2024) William Benter Prize in Applied Mathematics (2024) SIAM Ralph E. Kleinman Prize (2023) SIAM Outstanding Paper Prize (2018) Fellow of the American Mathematical Society (2012) Fellow of the American Academy of Arts and Sciences (2011) Hou has served in significant editorial roles including Founding Editor-in-Chief of the SIAM Journal on Multiscale Modeling and Simulation and Co-Editor-in-Chief of Research in Mathematical Sciences. His professional service includes membership on the SIAM Council and leadership roles at the Institute of Mathematics and its Applications. His research has been supported by numerous grants focusing on multiscale modeling, fluid dynamics, and computational mathematics.
Peter Schroeder is the Shaler Arthur Hanisch Professor of Computer Science and Applied and Computational Mathematics at the California Institute of Technology (Caltech). He holds a B.S. from the Technical University of Berlin (1987), M.S. from MIT (1990), M.A. and Ph.D. from Princeton University (1992–1994). His academic roles at Caltech include Assistant Professor (1995–1998), Associate Professor (1998–2001), Professor (2001–2013), and Hanisch Professor since 2013. He served as Division Deputy Chair (2012–2015) and Acting Director of the Center for Advanced Computing Research (2013–2014). Schroeder’s research focuses on numerical algorithms for computer graphics, geometric modeling, and physical simulation. His work emphasizes Discrete Differential Geometry, rebuilding classical differential geometry for computational applications. Key areas include cloth deformation, fluid dynamics, and vortex simulations. Notable contributions include 'Schrödinger’s smoke' and fluid visualization techniques using Clebsch maps. His publications span ACM Transactions on Graphics and address topics like constrained Willmore surfaces, filament-based plasma models, and shape reconstruction from metrics. He has received the ACM Fellowship and Best Paper in Geometry Processing Award. His research often bridges computational mathematics with artistic and engineering challenges, such as simulating ink chandeliers and solar flares. Schroeder’s academic leadership includes co-founding the ACM SIGGRAPH Academy and mentoring students like James R. McLaughlin and Yanke Song, both recipients of the Henry Ford II Scholar Award.
Roles & Affiliations: Professor of Mathematics at Cornell University, Department of Mathematics. Member of graduate programs in Mathematics, Applied Mathematics, Operations Research and Information Engineering, Theoretical & Applied Mechanics, and Computational Science & Engineering. Co-organizer of the Scientific Computing and Numerics (SCAN) Seminar and founder of the Cornell Mathematical Contest in Modeling. Education: Ph.D. in Applied Mathematics from University of California, Berkeley (2001); B.A. in Applied Mathematics (with high honors) from University of California, Berkeley (1995). Research Interests: Focuses on numerical analysis, nonlinear PDEs, control theory, and dynamical systems. Explores applications in optimal control, front propagation, anisotropy, bifurcation theory, and mathematical biology. Develops methods for invariant manifold approximation, Eikonal equations, and stochastic systems. Recent work includes studies on cancer therapy optimization, surveillance evasion, and pedestrian flow modeling. Teaching: Teaches courses like Introduction to Partial Differential Equations, Differential Games, Numerical Analysis, and Mathematical Modeling. Recent courses include Math 4280 (Spring 2025) and Math 3610 (Fall 2024). Labs/Teams: Active in interdisciplinary collaborations, including work on computational biology, robotics path planning, and mathematical contest problem-solving initiatives.
Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.
Endre Süli FRS is Professor of Numerical Analysis at the University of Oxford and Fellow of Worcester College. He also holds the position of Professor Hospitus Universitatis Carolinae Pragensis at Charles University, Prague. Süli leads research within the Oxford Centre for Nonlinear Partial Differential Equations (OxPDE) and the Numerical Analysis Group at Oxford. His research focuses on mathematical and numerical analysis of nonlinear partial differential equations and finite element methods , with particular expertise in kinetic models for polymers, Navier-Stokes-Fokker-Planck systems, non-Newtonian fluid flow, implicitly constituted material models, free-discontinuity problems, computational fracture modeling, adaptive algorithms, and multiscale finite element methods. Süli has received numerous prestigious honors including Fellowship in the Royal Society (2021), the London Mathematical Society Naylor Prize (2021), SIAM Fellowship (2016), and membership in the Academia Europaea (2020). He has delivered distinguished lectures worldwide including the John von Neumann Lecture (2016) and the Charlemagne Distinguished Lecture (2011). Foreign Member of Serbian National Academy of Sciences and Arts (2009) Fellow of European Academy of Sciences (2010) London Mathematical Society Forder Lecturer (2015) Oxford University Teaching Excellence Award (2009) Professor Süli has supervised over 40 doctoral students throughout his career and currently advises Stefano Fronzoni and Olav Haaland. He serves on multiple editorial boards including as Editor-in-Chief of Springer's Universitext Series (2023-) and previously co-edited Oxford University Press's Monograph Series in Numerical Mathematics (1994-2023). Süli has held significant leadership roles including Chair of the Society for Foundations of Computational Mathematics (2002-2005) and President of SIAM UKIE Section (2013-2015). He is actively involved in the international mathematics community, organizing workshops at the Mathematisches Forschungsinstitut Oberwolfach and participating in major conferences through 2025, including the upcoming EFEF XXII in Trieste.
Roy Dong is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges Control Theory Economics Statistics Optimization to address challenges in cyber-physical systems and the Internet of Things, focusing on data manipulation, privacy, and strategic behavior in interconnected systems. His academic journey includes a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2017) and dual B.S. degrees in Economics and Computer Engineering from Michigan State University (2010). At Illinois, he teaches courses ranging from Control Systems to Convex Optimization , with multiple teaching excellence awards. Roy's research explores Closed-loop effects of machine learning Causality in decision systems Incentive design for strategic agents Privacy-utility tradeoff optimization Human behavior modeling with applications in smart grids, transportation networks, and semi-autonomous vehicles. His work formulates privacy-preserving mechanisms as optimization problems, balancing data utility against user privacy in dynamic systems. Article trends show expertise in Game theory for strategic data sources Energy disaggregation techniques Nonlinear basis pursuit algorithms Privacy-aware control systems with a focus on cyber-physical systems and human-in-the-loop applications. Scientific recognition includes 'Teacher Ranked as Excellent' awards (ECE 120, ECE 486, ECE 515) Contributions to smartSDH building control and CPRL compressive sensing Roy leads the Privacy-aware Control Systems research group, collaborating with institutions like UC Berkeley and Michigan State University , and directs projects funded by grants including the New USDA NIFA grant for agricultural robot autonomy .
Rohit Kannan is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. and M.S. in Chemical Engineering from MIT and a B.Tech. from IIT Madras. His research focuses on integrating machine learning with global optimization and optimization under uncertainty, emphasizing energy systems applications. Previous roles include postdoc positions at Los Alamos National Laboratory and the Wisconsin Institute for Discovery. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018 M.S., Chemical Engineering Practice, MIT, 2014 B.Tech., Chemical Engineering, IIT Madras, 2012 Research Interests: Global optimization, optimization under uncertainty, computational optimization, energy systems, and machine learning integration. Recent Highlights: Recipient of the Excellence in Teaching Spotlight Award (2024) Lead researcher in stochastic optimization and energy systems (e.g., hybrid polygeneration systems) Developed algorithms for chance-constrained nonlinear programs and distributionally robust optimization Service & Leadership: Elected Vice-Chair of Global Optimization, INFORMS Optimization Society (2025–2026) Reviewer for top journals like Operations Research and Mathematical Programming Advisor to ISE InclusiveVT and Graduate Admissions Committee Labs & Collaborations: Directs a research group advancing optimization and machine learning for energy and engineering systems. Active in interdisciplinary projects with LANL and UW-Madison.
Justin Wan is a Professor in the Department of Computer Science at the University of Waterloo. His research focuses on scientific computing, medical image processing, computational finance, and machine learning. He holds a Ph.D. from UCLA (1998), an M.A. from UCLA (1995), and a B.Sc. from the Chinese University of Hong Kong (1992). Wan’s work bridges numerical methods, optimization, and deep learning, with applications in financial modeling, medical imaging, and fluid dynamics. His research interests include advanced techniques in scientific computing (e.g., multigrid methods), computer graphics simulation, and medical image enhancement (e.g., CT scan artifact reduction). He has pioneered applications of machine learning to computational finance, including option pricing and hedging using deep neural networks and GANs. His recent work explores denoising diffusion models and multi-agent systems for optimal execution in finance. Publications span topics like volatility surface computation, optimal mass transport for image registration, and parallel solvers for fluid dynamics. His methods address challenges in high-dimensional problems, robust numerical valuation, and scalable algorithms for large datasets. Wan collaborates across disciplines, integrating mathematical rigor with practical engineering solutions.
Pierre Raphaël is the Herchel Smith Professor of Pure Mathematics at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, where he joined in 2019. His academic career spans prestigious institutions including École Polytechnique, University of Cergy-Pontoise, Princeton University, and various research positions in France before his appointment at Cambridge. Professor Raphaël's research lies at the border between physics and pure mathematics, focusing on understanding energy concentration mechanisms and singularity formation during the propagation of non-linear waves. His work is deeply connected to fundamental nonlinear structures occurring in electromagnetism, astrophysics and turbulent fluid flows. He has made significant contributions to the mathematical analysis of nonlinear partial differential equations, particularly in understanding blow-up phenomena and singularity formation. His publications reveal a consistent research trajectory focused on nonlinear wave phenomena, with particular emphasis on energy concentration mechanisms and singularity formation across various mathematical models. His work spans multiple areas including nonlinear Schrödinger equations, heat equations, Korteweg-de Vries equations, and harmonic heat flows, demonstrating his expertise in analyzing critical and supercritical regimes where singularities may form. Grand Prix Alexandre Joannides 2014 from the French Academy of Sciences Royal Society Wolfson Fellowship 2019 Invited Speaker at International Congress of Mathematicians 2014 ERC Advance Grant recipient Professor Raphaël has secured significant research funding including European Research Council grants, demonstrating his leadership in the field. His research group at Cambridge focuses on singularity formation for nonlinear PDEs, as evidenced by the conference he organized at St Catharine's College in September 2024. His work bridges pure mathematics with physical applications, particularly in understanding extreme regimes of nonlinear wave propagation.
Chun Liu is Chair and Professor of Applied Mathematics at the Department of Applied Mathematics, Illinois Institute of Technology (IIT), within the College of Computing. His research focuses on Nonlinear Partial Differential Equations , Complex Fluids , and Multiscale Modeling , with applications in electrophysiology and materials science. He earned a Ph.D. from New York University’s Courant Institute, an M.S. from Duke University, and a B.S. from Fudan University. Prof. Liu leads projects on General Diffusion Systems , Ion Channel Dynamics , and Viscoelastic Fluids . He has secured grants from NSF, BSF, and DAAD for research in energetic variational approaches, multiscale materials modeling, and biomolecular systems. Key contributions include the development of Poisson-Boltzmann models , coarse-grained dynamics , and energetically stable numerical methods . He serves on editorial boards for Communications in Mathematical Sciences , SIAM Journal on Mathematical Analysis , and others. His work bridges applied mathematics with engineering and biophysics, addressing challenges in fluid mechanics, ion transport, and nonlinear systems.
Lenya Ryzhik is a Professor in the Department of Mathematics at Stanford University, specializing in analysis and partial differential equations with applications in various physical contexts. His research spans stochastic processes, wave propagation, and front dynamics in random media, with significant contributions to understanding reaction-diffusion systems and their applications in mathematical biology and physics. Professor Ryzhik's research interests focus on the mathematical analysis of partial differential equations arising in physical systems. His work particularly emphasizes stochastic PDEs, wave propagation in random media, front propagation in reaction-diffusion systems, and homogenization theory. He investigates how randomness and complex structures affect wave propagation, front speeds, and transport phenomena, with applications ranging from combustion theory to population dynamics and quantum mechanics. The publication record demonstrates a consistent focus on understanding propagation phenomena in complex environments. Ryzhik's research shows a progression from classical PDE analysis toward increasingly sophisticated stochastic frameworks, particularly examining high-dimensional systems and random media. His recent work has focused on KPZ fluctuations, random heat equations, and non-local reaction-diffusion models, revealing deep connections between probability theory and partial differential equations. Alfred P. Sloan Research Fellowship (2002-2004) AFOSR NSSEFF Fellowship (2010-2015) Ryzhik has advised graduate students including Alexandra Stavrianidi, and has secured substantial research funding throughout his career. His grant history includes multiple NSF awards (DMS-9971742, DMS-0203537, DMS-0604687, DMS-0908507, DMS-1311903), ONR funding (N00014-02-1-0089, N00014-04-1-0224), and FRG support for collaborative research on nonlinear evolution problems. He co-organized a Summer School and Workshop on 'Recent Advances in PDEs and Fluids' at Stanford in 2013. Ryzhik maintains an active research group collaborating with leading mathematicians worldwide, particularly with researchers at institutions like NYU, Chicago, and various European universities. His work frequently involves interdisciplinary collaborations bridging mathematics with physics and biology.
Lakshmi N Sankar serves as Regents Professor and Sikorsky Professor in the Guggenheim School of Aerospace Engineering at Georgia Institute of Technology, where he directs the Computational Fluid Dynamics Laboratory and teaches aerodynamics, helicopter theory, and wind energy courses. His research program spans unsteady viscous flow modeling for aircraft, helicopters, and wind turbines since joining the faculty in 1982 after industry experience at Lockheed Martin. Education: Ph.D., Aerospace Engineering, Georgia Institute of Technology, 1977 MSAE, Aerospace Engineering, Georgia Institute of Technology, 1975 B. Tech., Aeronautical Engineering, Indian Institute of Technology, Madras, India, 1973 Research Focus: Professor Sankar's work centers on Computational Fluid Dynamics for rotorcraft aerodynamics and wind energy systems , with significant contributions to icing phenomena and unsteady flow modeling . His recent publications reveal intensifying focus on adverse weather effects (rain/icing), eVTOL conversion challenges, and high-fidelity hybrid modeling techniques for rotorcraft performance prediction. Publication Trends: Analysis of his 2022-2025 publications shows dominant themes in rotorcraft icing (35%), weather impact studies (25%), and advanced CFD methodologies (20%), with growing interest in drone applications and mathematical aspects of fluid dynamics. His work consistently bridges theoretical mathematics with practical aerospace engineering challenges. Scientific Recognition: AIAA Fellow and AHS Technical Fellow NASA Group Achievement Award (2007) and Space Act Software Release Award (2003) Multiple Sigma Gamma Tau Teaching Awards (2005-2015) Dean George C. Griffin Faculty of the Year (2014-2015) Sikorsky Professorship (2018-Present) Mentorship and Collaboration: As recipient of Georgia Tech's Graduate Research Assistant Development Award, he has cultivated extensive student mentorship. His research integrates with the Vertical Lift Research Center of Excellence and Center for 21st Century Universities, securing major industry and NASA funding for rotorcraft innovation. Current projects include physics-based modeling of ice accretion and eVTOL retrofit feasibility studies. Research Infrastructure: The Computational Fluid Dynamics Laboratory serves as his primary research hub, complemented by collaborations through the Vertical Lift Research Center of Excellence where his team develops next-generation modeling tools for military and civilian rotorcraft applications under federal funding programs.
Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.
Alex Dunlap is an Assistant Professor in the Department of Mathematics at Duke University. His research focuses on probability theory, partial differential equations (PDEs), and applied mathematics, particularly the asymptotic behavior of stochastic PDEs. Before joining Duke in 2023, he was an NSF postdoctoral fellow at NYU Courant, sponsored by Jean-Christophe Mourrat and Yuri Bakhtin. He earned his Ph.D. from Stanford University in 2020 under the supervision of Lenya Ryzhik. His work involves studying nonlinear stochastic PDEs such as the KPZ equation, stochastic Burgers equation, and stochastic heat equations. He is particularly interested in universality phenomena, fluctuation scaling, and invariant measures. Dunlap co-organizes the Duke Probability Seminar and has published extensively in top journals including Annals of Probability , Communications on Pure and Applied Mathematics , and Archive for Rational Mechanics and Analysis . His research is supported by NSF grant DMS-2346915. Notable contributions include work on viscous shock fluctuations, Edwards-Wilkinson universality in 2D systems, and stationary solutions of stochastic Burgers equations. He has collaborated with leading researchers such as Cole Graham, Yu Gu, and Lenya Ryzhik.
