Alan Chang is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. His research explores analytical foundations of geometric structures, with particular focus on problems in geometric measure theory and harmonic analysis. His work examines the interplay between measure-theoretic concepts and geometric properties in high-dimensional spaces, developing new approaches to analyze singular sets and rectifiability problems. This research contributes to fundamental understanding of geometric analysis with applications to partial differential equations. Professor Chang received his PhD from the University of Chicago and completed postdoctoral training at Princeton University under the mentorship of Assaf Naor. He has received support from the National Science Foundation for his investigations into the geometric properties of Sobolev spaces and their metric embeddings.
László Kozma is an Assistant Professor at Freie Universität Berlin in the Theoretical Computer Science department. He obtained his PhD at Saarland University under Raimund Seidel, followed by postdocs at Tel Aviv University and TU Eindhoven. His work focuses on data structures , combinatorics , and algorithmic adaptivity , with significant contributions to self-adjusting heaps, binary search trees, and geometric optimization. Recent research includes pattern-avoiding sequences and saddlepoint algorithms . His publications span exponential algorithms , TSP variants , and heap structures , with a recurring theme of connecting combinatorial geometry to algorithm design. Co-authors include leading researchers like Robert Tarjan, Uri Zwick, and Haim Kaplan. Key software implementations (e.g., smooth heap ) are publicly available. He has developed tools like Cuckoo Hashing Visualization and the historical WikipediaVision project, demonstrating practical engagement with algorithmic concepts. His mathematical genealogy traces back to classical researchers.
Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.
Yiming Xu is an Assistant Professor in the Department of Mathematics at the University of Kentucky. He holds a Ph.D. from the University of Utah, completed a postdoc at the University of Waterloo, and worked as a Quantitative Analyst at Wells Fargo Bank. His research focuses on the mathematics of data science, including random sampling, statistical ranking, optimization-based uncertainty quantification, and multifidelity methods. Xu emphasizes problem-driven innovation and collaborates across disciplines. Education: Ph.D. in Mathematics, University of Utah, 2022 Postdoctoral Fellowship, University of Waterloo Quantitative Analyst, Wells Fargo Bank Research interests span computational statistics, numerical analysis, and machine learning applications. His work bridges theoretical foundations and practical data-driven problems, often integrating optimization and probabilistic techniques. Recent contributions include advancements in gradient descent for network analysis, multifidelity distribution estimation, and archetypal analysis using optimal transport. Publications reflect a trajectory in stochastic methods, statistical learning, and computational mathematics, with applications in PDE solvers and uncertainty quantification. Awards: None explicitly listed. Advising and grants: Actively seeks motivated students for collaborative projects. Teaching includes courses on matrix theory, data science, and numerical methods.
Islam Foniqi is a Senior Research Associate at the University of East Anglia (UEA), affiliated with the School of Engineering, Mathematics and Physics. He is a member of the Algebra, Number Theory, Logic, and Representations (ANTLR) research group. His work focuses on geometric group theory, particularly Artin groups, subgroup structures, and combinatorial algebraic problems. Foniqi has conducted research visits funded by the London Mathematical Society, collaborating with Prof. Bruno A. Cisneros De La Cruz at the Institute of Mathematics of UNAM, Oaxaca. His research explores topics such as twisted Artin groups, parabolic subgroups, and algorithmic aspects of group theory. He has published in high-impact journals like the Canadian Journal of Mathematics and the Journal of Group Theory, with a strong emphasis on subgroup separability and membership problems in algebraic structures. Key projects include 'Exploring twisted graph groups, algebras, and applications' (2023–2025) and a 2024 research collaboration with the London Mathematical Society. His work bridges pure mathematics with algorithmic challenges, contributing to foundational theories in algebraic combinatorics and geometric topology.
Håkon Andreas Hoel is an Associate Professor at the Department of Mathematics, University of Oslo. His research focuses on numerical methods for stochastic and partial differential equations, Monte Carlo methods, and data assimilation with an emphasis on developing efficient computational techniques for stochastic problems. Education: PhD in Numerical Analysis from KTH Royal Institute of Technology (2012), Master and Bachelor in Computational Science from University of Oslo (2006/2004). He has held postdoctoral positions at KAUST (2012–2014), University of Oslo (2014–2016), EPFL (2016–2017), and was a Junior Professor at RWTH Aachen (2019–2022). His research spans multilevel Monte Carlo methods, ensemble Kalman filtering, and stochastic numerical analysis with applications in spatiotemporal processes and uncertainty quantification. His work bridges theoretical analysis and practical computational implementation. Publications emphasize methodological advancements in stochastic numerical methods, with contributions to complexity reduction and error analysis in high-dimensional problems. Research groups include Computational Mathematics and Beregningsorientert Matematikk.
Dr. Ergun Simsek is an Assistant Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC) and serves as the Director of Graduate Data Science Programs. He is a member of the Computational Photonics Laboratory and leads the Computational Photonics for Multilayered Structures (CPMS) research group. Education: Ph.D., Electrical and Computer Engineering, Duke University (2006) M.S., Electrical and Computer Engineering, University of Massachusetts Dartmouth (2003) B.S., Electrical and Electronics Engineering, Bilkent University (2001) Research Focus: Dr. Simsek's expertise lies in computational photonics , with emphasis on: Wave propagation and scattering in complex, multilayered media Nano-photonic and optoelectronic device design Machine-learning-assisted electromagnetic inversion and inverse photonic design Photodetectors, plasmonic sensors, electro-optic modulators, and 2-D material devices He has published over 100 peer-reviewed journal and conference papers and is a Senior Member of both IEEE and Optica as well as a Licensed Professional Engineer. Scientific Recognition: Senior Member, IEEE Senior Member, Optica (formerly OSA) Licensed Professional Engineer (PE) Advising & Teaching: Dr. Simsek currently advises PhD students Ishraq Anjum and Raonaqul Islam , and has mentored undergraduate researchers. He teaches graduate and undergraduate courses including Machine Learning and Photonics , Electromagnetic Theory , Electromagnetic Waves and Transmission , and Introduction to Data Science . Laboratory & Facilities: He directs research within UMBC’s Computational Photonics Laboratory and the CPMS group , utilizing facilities in the TRC Building (Technology Research Center) and the ITE Building (Information Technology and Engineering).
Dr. Adina Svenja Wagner is a researcher at the Institute of Neuroscience and Medicine (INM-7) at the Research Center Jülich GmbH, Germany. Her work focuses on neuroinformatics , research software engineering , and research data management (RDM) . She is a core developer of the DataLad tool, which enables FAIR and reproducible data management in computational neuroscience and beyond. She teaches best practices for open science, reproducibility, and software tools to foster transparent scientific practices. Her roles include coordinating institute activities in AI, RSE, and RDM. Her research interests span open science infrastructure , reproducible workflows , and collaborative data science . Key contributions include advancing DataLad for large-scale datasets, developing frameworks for reproducibility in biomedical research, and promoting inclusive open science practices through initiatives like OHBM Brainhack. Her recent work emphasizes scalable solutions for big data challenges, FAIR principles in neuroscience, and interdisciplinary collaboration. She actively engages in conferences and community-driven projects to advance open science and computational reproducibility.
Riccardo Caniato is an Olga Taussky and John Todd Postdoctoral Scholar and Teaching Fellow in Mathematics at the California Institute of Technology (Caltech), affiliated with the Division of Physics, Mathematics, and Astronomy. His research focuses on geometric analysis, partial differential equations, and gauge theory, with particular emphasis on variational problems, singularities of maps, and regularity theory in supercritical dimensions. He investigates harmonic maps, Yang-Mills connections, and calibrated geometries, employing advanced analytical techniques such as log-epiperimetric inequalities and Coulomb gauges. His work bridges differential geometry, functional analysis, and algebraic geometry, addressing questions about uniqueness of tangent cones for holomorphic maps, non-concentration of energy, and closure properties of vector fields with singularities. Caniato’s contributions advance understanding of geometric flows, topological defects, and minimal surface theory in high-dimensional settings. He is based in Linde Hall at Caltech, where he contributes to teaching and research in the Mathematics Department. His research website provides further details on ongoing projects and collaborations.
Nicholas J. Zabaras is a Professor in the College of Engineering at the University of Notre Dame and serves as director of the Warwick Centre for Predictive Modelling at the University of Warwick. He holds a Hans Fischer Senior Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2014. His academic journey began with a diploma in Mechanical Engineering from the National Technical University of Athens (1982), followed by an M.Sc in Material Science and Engineering from the University of Rochester (1983), and a PhD in Theoretical and Applied Mechanics from Cornell University (1987). His research spans computational mathematics, computational statistics, and scientific computing with focus on predictive modeling of complex multiscale and multiphysics materials systems. Key research themes include Bayesian uncertainty quantification, high-dimensional problem modeling, information-theoretic coarse graining, stochastic model reduction, and optimization under uncertainty. His work has significant applications in materials science, particularly in uncertainty propagation from ab initio to continuum simulations and modeling of random microstructures. His recent publications demonstrate strong activity in Bayesian coarse-graining techniques, deep Gaussian processes, and uncertainty quantification for multiscale materials systems. The research shows consistent focus on developing computationally efficient methods for high-dimensional problems with applications across materials science and engineering disciplines. Major Awards and Recognitions: Royal Society Wolfson Research Merit Award (2014) Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge (2011) Michael Tien'72 College of Engineering Teaching Award, Cornell University (2009) Fellow, American Society of Mechanical Engineers (2006) Presidential Young Investigator Award (1991) Zabaras leads the Scientific Computing and Artificial Intelligence (SCAI) Laboratory and the Computational Science and Engineering (CSE) Laboratory at Notre Dame, where his team develops innovative mathematical and statistical approaches addressing unique challenges in predictive modeling. His research integrates computational mathematics, machine learning, and multiscale/multiphysics modeling to address problems in materials physics, geological sciences, and climate modeling.
Yimin Zhong is an Assistant Professor of Mathematics and Statistics at Auburn University. He holds a Ph.D. from the University of Texas at Austin (2017). His research focuses on applied mathematics, scientific computing, and machine learning, with expertise in inverse problems, radiative transfer, and nonlinear optics. He leads undergraduate research initiatives and collaborates on projects involving biomedical imaging and transport models. Key research areas include PDE learning, neural networks for high-frequency approximation, intrinsic complexity of datasets, and imaging with physical models. Zhong's work bridges theoretical analysis with computational methods, addressing challenges in data-driven modeling and inverse problem uniqueness/stability. He has contributed to fast algorithms for radiative transport and implicit boundary integration techniques for macromolecular electrostatics. His projects span collaborations with industry (e.g., Boeing) and academic networks, emphasizing interdisciplinary applications. Current interests also include graph theory, randomized algorithms, and geometric measure theory. Despite no explicit awards listed, his extensive publication record reflects recognition in computational and applied mathematics fields. Education: Ph.D. in Mathematics, University of Texas at Austin (2017) Advising: Mentored undergraduate research in inverse problems and numerical methods Labs/Teams: Leads Auburn's Undergraduate Research Network in Mathematics Open Problems: Active in transport equation analysis, nonlinear diffusion, and geometric measure theory challenges
Zhiyuan Geng is a Golomb Visiting Assistant Professor of Mathematics at Purdue University's Department of Mathematics, part of the College of Science. His research focuses on advanced mathematical analysis, particularly in liquid crystals, partial differential equations, geometric variational problems, and materials science. Contact details include his email: geng42@purdue.edu , and office location MATH 409. Research interests span the following key areas: Liquid crystal defect dynamics and stability Phase transitions modeled via Allen-Cahn systems Free boundary problems in materials systems Triple junction configurations and surface tension effects Asymptotic analysis of interfaces and self-similar solutions Mathematical modeling of colloidal interactions in nematic phases Recent publications (2021–2025) emphasize theoretical breakthroughs in understanding complex material systems through PDE analysis and geometric variational principles. His work addresses critical phenomena like eigenframe discontinuities in Q-tensor models, rigidity properties of triple junction solutions, and stability of free boundary flows. These studies highlight interdisciplinary approaches combining pure mathematics with applications in soft condensed matter physics. No scientific awards or grants are explicitly listed in the provided information. He does not currently have documented advisees or students. Laboratory affiliations or collaborative research teams are not indicated in the texts provided.
Onkar Jadhav is a Research Fellow at the University of Western Australia's School of Earth and Oceans, specializing in developing machine learning frameworks for oceanography and computational science. His work focuses on statistical downscaling techniques for ocean temperature predictions and reduced-order modeling for financial risk analysis. Previously, he was a postdoctoral researcher at the University of Luxembourg, where he applied machine learning to wind engineering and computational fluid dynamics. Education: He holds a Doctorate in Applied Mathematics from the Technical University of Berlin (2018–2022), with a thesis on physics-assisted machine learning for high-dimensional partial differential equations. His academic background bridges mathematics, engineering, and computational science. Research Interests: Jadhav’s expertise spans machine learning, computational science, and applied mathematics, with applications in oceanography, wind engineering, and financial risk analysis. He emphasizes interdisciplinary approaches to solve complex, high-dimensional problems. Publications: His research includes advancements in model order reduction for parametric systems in finance, wind load assessments using machine learning, and thermoelectric generator design for biomedical implants. Labs/Teams: Affiliated with the Oceans Graduate School and previously with the University of Luxembourg’s wind engineering team.
Matthew Hirn is a computational mathematician with expertise in harmonic analysis, data science, and machine learning. He was a tenured Associate Professor at Michigan State University (MSU), jointly appointed in the Department of Computational Mathematics, Science & Engineering and the Department of Mathematics. As the scientific leader of the ComplEx Data Analysis Research (CEDAR) Team at MSU, he developed innovative methods for analyzing complex data, including wavelet scattering transforms for atomistic systems and geometric deep learning techniques. He is currently a Senior Quantitative Researcher at Citadel LLC, contributing to algorithmic trading strategies. His research bridges theoretical mathematics and applied data science, with notable contributions to graph neural networks, scattering transforms, and privacy-preserving methods. His academic career included organizing conferences such as the SIAM Mathematics of Data Science mini-symposium on geometric learning and co-authoring influential software like the Kymatio library for wavelet scattering transforms. He advised PhD students Ryan LaRose, Xavier Brumwell, and Jieqian He, who have since pursued impactful roles in academia and industry. His work on 3D wavelet scattering networks for material science and geometric scattering on manifolds has advanced the intersection of mathematics and machine learning. Matthew’s research interests span signal processing, graph representation learning, and applications in bioinformatics and many-particle physics. He has published extensively in top journals and conferences, including the Journal of Chemical Physics and the Journal of Machine Learning Research.
Charles-Edouard Bréhier is a Professor at the University of Pau and the Pays de l'Adour, specializing in numerical methods for stochastic systems. His research spans stochastic PDEs, multiscale modeling, metastable processes, and geometric numerical integration. He leads investigations into error analysis and computational efficiency for complex stochastic systems. Bréhier's work develops advanced numerical schemes for high-dimensional problems, including splitting integrators, Galerkin approximations, and structure-preserving algorithms. His recent publications focus on error bounds for SPDE discretizations, turbulence modeling, and stochastic geometric mechanics. Articles consistently demonstrate rigorous mathematical foundations for computational methods, with applications spanning fluid dynamics, plasma physics, and optimization. The research provides tools for simulating complex systems with inherent randomness across multiple temporal and spatial scales.
