Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.
Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.
Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.
David Croydon is an Associate Professor at the Research Institute for Mathematical Sciences (RIMS), Kyoto University. His research focuses on probability theory, particularly diffusions on random fractals and scaling limits of random walks on random graphs. He also investigates discrete integrable systems with random initial conditions. Dr. Croydon's primary research interests span several areas of probability theory and mathematical physics. His work centers on diffusions on random fractals and how these processes can be constructed as scaling limits of related random walks on random graphs. He has made significant contributions to understanding random walks on critical structures including Galton-Watson trees, uniform spanning trees, and percolation clusters. More recently, he has developed a growing interest in the behavior of discrete integrable systems such as the box-ball system, particularly when started from random initial conditions. His research often bridges theoretical probability with applications in statistical physics and mathematical physics. Dr. Croydon's recent publications demonstrate a dual focus on theoretical probability and mathematical physics. His work on random walks spans various structures including binary trees, critical percolation clusters, and uniform spanning trees. He has made significant contributions to understanding aging phenomena, heat kernel fluctuations, and scaling limits in random media. Simultaneously, his research on discrete integrable systems explores the connections between probability theory and soliton theory, particularly through the lens of the box-ball system and related models. These two research strands converge in his investigations of scaling limits and invariant measures for complex stochastic systems. Dr. Croydon's scientific contributions have been recognized through publications in top-tier journals across probability theory and mathematical physics, though specific awards are not mentioned in the available information. Dr. Croydon has supervised several doctoral students to completion, including Adam Bowditch (2017), George Andriopoulos (2019), Eleanor Archer (2020), and Takumu Ooi (2024). He has also served in advisory roles for other students including John Sylvester (2017). His collaborative research spans multiple international institutions, suggesting involvement in various research grants supporting his work in probability theory and mathematical physics. While specific lab names aren't mentioned, Dr. Croydon is part of the vibrant probability theory research group at the Research Institute for Mathematical Sciences (RIMS) at Kyoto University. His extensive collaborations with researchers worldwide, particularly in the UK, France, and Japan, indicate active participation in international research networks focused on stochastic processes, random media, and discrete integrable systems.
Olof Runborg is a Professor in Numerical Analysis at the Royal Institute of Technology (KTH) in Stockholm, Sweden. He works in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH. His research focuses on developing and analyzing numerical methods for partial differential equations, particularly for wave propagation problems. His educational background includes: MSc in Electrical Engineering from KTH (1992) BSc in Economics & Business Administration from SSE (1994) PhD in Numerical Analysis/Applied Mathematics from KTH (1998) Postdoc at Paris VI University (1999) Postdoc at Princeton University's Program in Applied and Computational Mathematics (2000-2001) Became Docent at NADA in 2003 Appointed Professor at KTH in 2010 Professor Runborg's research centers on the numerical treatment of partial differential equations, with special emphasis on wave propagation problems. His work spans multiple areas including high-frequency waves, multiscale phenomena, numerical homogenization, uncertainty quantification, multiresolution analysis, Gaussian beams, and mesh generation. A recurring theme in his research is developing methods to solve computationally expensive problems more efficiently while maintaining accuracy. His approach often involves coupling different numerical methods or reformulating equations to create more efficient computational approaches. His research has applications across physics and engineering domains where wave phenomena are important. An analysis of his recent publications (2015-2025) shows continued focus on high-frequency wave propagation with expanding applications to areas like the Landau-Lifshitz equation for magnetic materials and elastic wave propagation. His work bridges theoretical numerical analysis with practical computational techniques, often developing novel methods like the WaveHoltz iteration for solving the Helmholtz equation. The publications demonstrate increasing attention to uncertainty quantification and multiscale methods while maintaining strong theoretical foundations in error analysis. Professor Runborg teaches several courses at KTH including Numerical Methods (basic course), Applied Numerical Methods, Numerical Algorithms for Data-Intensive Science, and Selected Topics in Numerical Analysis II. He serves as examiner for degree projects in Scientific Computing. His teaching reflects his research expertise in numerical methods and computational mathematics, providing students with both theoretical foundations and practical implementation skills. Beyond his core research, Professor Runborg has engaged in interesting side projects, such as his analysis of 'Numbers on the Web' where he investigated the frequency of numbers 11-1000 in web content, revealing patterns related to dates, time, computer systems, and cultural phenomena. This demonstrates his broader interest in data analysis and computational approaches to understanding patterns in information.
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.
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.
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.
Dr. Edouard Boujo is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering (STI) and working in the Institute of Mechanical Engineering (IGM) and Laboratory of Fluid Mechanics and Instabilities (LFMI) . He also teaches in the SGM-ENS department of the School of Engineering. Scientist at EPFL STI IGM LFMI Lecturer at EPFL STI-SGM SGM-ENS His research focuses on Fluid Dynamics with expertise in Flow Stability , Flow Control , Aeroacoustics , Thermoacoustics , Fluid-Structure Interaction , and Coating Flow Dynamics . He employs advanced mathematical modeling and computational methods to study complex fluid behaviors. Recent publications highlight his work on stochastic modeling of fluid instabilities, adjoint-based optimization of flow systems, and nonlinear dynamics of coating flows. His 15 most recent papers cover topics ranging from symmetry-breaking bifurcations to spin coating optimization and noise-induced transitions in fluid systems. Dr. Boujo actively collaborates with institutions across Europe and New Zealand, mentoring PhD student Atharva Lagwankar . He has received research funding from the Swiss National Science Foundation for two PhD theses and contributes to major fluid dynamics conferences like the European Fluid Dynamics Conference and APS Division of Fluid Dynamics meetings. His laboratory work at LFMI involves experimental and computational studies of fluid instabilities, with applications in aerospace, mechanical engineering, and industrial coating processes. He develops adjoint-based control methods for optimizing flow systems and reducing drag in various fluid configurations.
Grégoire Allaire is a Professor of Applied Mathematics at École Polytechnique, where he leads research in shape optimization , homogenization , and multi-scale modeling . His work bridges theoretical and applied domains, focusing on partial differential equations (PDEs), composite materials, and computational methods.
Joe Pitt-Francis is Associate Professor of Computer Science and Tutorial Fellow in Computer Science at St Edmund Hall, University of Oxford . Since 1999 he has tutored Oxford computer-science students and formally became a Tutorial Fellow of St Edmund Hall in 2024. His research lies at the intersection of computational biology and mathematical biology . Using sophisticated numerical techniques he constructs and analyses models of the heart , cancer and blood flow . A central strand of his work is software development for biological simulation; he is an active contributor to Chaste ( Cancer, Heart and Soft-Tissue Environment ), a large-scale C++ library that supports multiscale computational models in physiology and medicine. Across more than 60 peer-reviewed publications since 1998, his work has progressively advanced from foundational software-engineering papers describing Chaste’s architecture to highly-cited studies on cardiac electrophysiology , tumour-induced angiogenesis , microvascular haemodynamics and cell-cycle dynamics under hypoxia . The 2024-2025 corpus shows strong emphasis on multiscale frameworks , open benchmarking , and radiotherapy-induced vascular remodelling , positioning his group at the forefront of translational in-silico oncology. Contact: Email: Joe.Pitt-Francis@seh.ox.ac.uk
Amine MEHEL is a Research Professor at ESTACA's Mechanics and Environment Center (MSCE) since 2010, specializing in air quality and pollution control in transport systems. His work bridges experimental and numerical studies of turbulent flow interactions with pollutants and nanoparticles. Research Axes : CQA (Characterization of Air Quality) and EDP (Spatiotemporal Dynamics of Pollutants) Key Projects : CEPARER (2022-2025), AmCoAir (2020-2023), CAPNAV (2019-2022), CAPTIHV (2015-2018) His expertise includes experimental facilities like wind tunnels, PIV/LDV measurements, and CFD simulations using Eulerian-Lagrangian approaches. He supervises PhD students and coordinates teaching projects like PIRATE and PRI. Recent publications focus on ultrafine particle dispersion in vehicle wakes, brake emissions in underground stations, and cabin air quality characterization.
Gianni Dal Maso is a Professor of Mathematical Analysis at the International School for Advanced Studies (SISSA) in Trieste, Italy. He has been a faculty member at SISSA since 1985, first as Associate Professor and then as Full Professor since 1987. He has held several leadership positions at SISSA including Head of the Sector of Functional Analysis and Applications (1993-1998, 2001-2010), Deputy Director (2010-2015), and Coordinator of the Mathematics Area (2016-2020). His educational background includes: 1973-1977: Undergraduate student in Mathematics at the University of Pisa and Scuola Normale Superiore 1977: Degree in Mathematics with honors at the University of Pisa (thesis: "Gamma-limits of set functions," advised by Ennio De Giorgi) 1977: "Diploma" in Mathematics from the Scuola Normale Superiore 1977-1981: Post-graduate Research Fellowship in Mathematics ("Perfezionamento") at the Scuola Normale Superiore Dal Maso's research focuses on the Calculus of Variations, with particular emphasis on semicontinuity and relaxation problems, Gamma-convergence, and more recently, free discontinuity problems and their applications to mechanics. His work bridges pure mathematical analysis with practical applications in material science, particularly in plasticity and fracture mechanics. He has developed mathematical frameworks for understanding crack propagation, material failure, and the behavior of solids under stress, contributing significantly to both theoretical foundations and practical modeling approaches in these areas. His extensive publication record shows a clear evolution from foundational work in Gamma-convergence (culminating in his influential book "An Introduction to Gamma-Convergence" in 1993) toward increasingly sophisticated models of material behavior, particularly in fracture mechanics and plasticity. Recent work demonstrates continued innovation in handling complex discontinuities, non-local effects, and multi-scale phenomena in material science applications. Among his notable scientific recognitions: 1982: Stampacchia Prize, awarded by the Scuola Normale Superiore 1990: Caccioppoli Prize, awarded by the Italian Mathematical Union 1996: Medaglia dei XL per la Matematica, awarded by the Accademia Nazionale delle Scienze detta dei XL 2003: Prize of the Minister for the Cultural Heritage for Mathematics and Mechanics, awarded by the Accademia Nazionale dei Lincei 2005: Prize Luigi and Wanda Amerio, awarded by the Istituto Lombardo Accademia di Scienze e Lettere Dal Maso has supervised 42 PhD students at SISSA, demonstrating a strong commitment to academic mentorship. His research has been significantly supported by multiple National Research Projects (PRIN) in Italy, and notably by an ERC Advanced Grant "Quasistatic and Dynamic Evolution Problems in Plasticity and Fracture" (QuaDynEvoPro) from 2012-2017, where he served as Principal Investigator. This major project focused on nonlinear evolution problems in plasticity and fracture, with three main research directions: plasticity with hardening and softening, quasistatic crack growth, and dynamic fracture mechanics. His scholarly activities extend to editorial service, with membership on the boards of numerous prestigious journals including Archive for Rational Mechanics and Analysis, SIAM Journal on Mathematical Analysis, and Journal of Convex Analysis. He has also been active in the mathematical community through membership in scientific committees and academies, including the Accademia Nazionale dei Lincei since 2014.
Stefan Vandewalle is a full professor at the Department of Computer Science, Faculty of Engineering Sciences, KU Leuven. His research focuses on numerical analysis, applied mathematics, and computational methods for stochastic differential equations, wind energy modeling, and uncertainty quantification. Department Chair, KU Leuven Member, Subdivision Numerical Analysis and Applied Mathematics Member, iSi Health Institute Observer, Faculty Council of Sciences Chair, Department Council for Computer Science His recent work explores multiscale modeling, Monte Carlo methods, and data assimilation techniques. Projects include micro-macro Parareal algorithms, wind turbine aeroelasticity, and turbulent flow reconstruction for wind farms. He supervises PhD candidates and collaborates on interdisciplinary studies involving structural mechanics and renewable energy systems. Publications highlight advancements in parallel-in-time methods, stochastic optimization for tokamak reactors, and DNS-based control of turbulent flows. Key keywords: Multiscale numerical methods Uncertainty quantification Wind energy simulation Monte Carlo algorithms PDE-constrained optimization Stochastic differential equations He contributes to academic governance as a member of extended faculty boards and evaluation committees.
Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants
