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Lucas C. Wilcox is a Professor in the Department of Applied Mathematics at the Naval Postgraduate School. His research focuses on scientific computation, particularly in the numerical solution of partial differential equations with emphasis on wave propagation and uncertainty quantification using high-order methods. He is active in developing scalable algorithms for adaptive mesh refinement and parallel computing.
استاد پژوهش
Rohan Sarkar is an Assistant Research Professor (postdoc) in the Mathematics Department at the University of Connecticut. His research focuses on Probability Theory, Functional Analysis, and Geometry, with emphasis on spectral theory of non-self-adjoint Markov semigroups and functional inequalities. He completed his Ph.D. in 2022 from Cornell University's Department of Operations Research and Information Engineering, under the supervision of Pierre Patie. He holds a Master's and Bachelor's in Statistics from the Indian Statistical Institute, Kolkata. His research interests bridge spectral theory and stochastic processes, exploring connections between functional inequalities and geometric properties of operators. Recent work includes studies on Lévy-Ornstein-Uhlenbeck semigroups, dimension-independent functional inequalities, and solutions to the van Dantzig problem linked to the Riemann hypothesis. His articles address topics ranging from spectral analysis of Markov processes to applications in PDEs and bioinformatics. Notable contributions include isospectral schemes for stochastic processes and clustering methods for large biological datasets.
پژوهشگر
Nicola Anselmi is a Researcher (RTD-A) at the University of Trento, affiliated with the Department of Civil, Environmental and Mechanical Engineering. He also contributes to the Department of Physics through teaching and research. His work focuses on advanced antenna array design, quantum computing applications in electromagnetics, and modular phased array architectures. Key research areas include tolerance analysis of reconfigurable systems, compressive sensing techniques for antenna characterization, and optimization strategies for next-generation wireless communication systems. He teaches courses such as Antenna Theory and Synthesis Methods (Civil Engineering) and Quantum Electromagnetics (Physics), emphasizing theoretical foundations and practical software applications. His research leverages quantum computing for solving complex electromagnetic problems and explores novel array configurations for low Earth orbit (LEO) satellite communications and urban wireless networks. Anselmi’s recent work highlights advancements in interval arithmetic for robust array tolerance analysis, Bayesian compressive sensing for microwave imaging, and self-replicating tiling techniques for modular array design. His publications span IEEE journals and conferences, addressing topics like electromagnetic environment optimization, sparse array synthesis, and AI-driven antenna optimization. Though no scientific awards are explicitly mentioned, his contributions reflect cutting-edge innovations in electromagnetics and quantum engineering. His research also involves collaborations with the ELEDIA Research Center, focusing on task-oriented reflectarrays and system-by-design methodologies for multi-scale applications. Current interests include overcoming electromagnetic challenges in smart cities and next-generation radar systems.
Benoit Merlet is a Professor at Université de Lille, specializing in advanced mathematical analysis with a focus on Calculus of Variations, Geometric Measure Theory, and Partial Differential Equations. His research explores non-convex functionals, optimal transport problems, and pattern formation in materials science. He collaborates extensively with researchers such as M. Goldman, M. Pegon, and A. Chambolle on topics ranging from Aviles-Giga functionals to liquid drop models. Merlet's work bridges theoretical mathematics with applications in physics and engineering, particularly in energy minimization problems and phase transitions. He organizes international conferences, including the Calculus of Variations in Lille series, and contributes to seminar programs on geometric measure theory and variational methods. His publications span high-impact journals like Arch. Rat. Mech. Anal. , Journal de l’École polytechnique , and SIAM J. Math. Analysis , reflecting his expertise in rigorous mathematical analysis and interdisciplinary problem-solving.
Edoardo Mainini is an Associate Professor at the University of Genova, specializing in mathematical analysis and applied mathematics. His research focuses on calculus of variations, partial differential equations, elasticity theory, optimal transport, and nonlinear dynamics. He has contributed to studies on fractional equations, material science, and stochastic processes. His work often bridges pure and applied mathematics, addressing problems in mechanics, probability, and geometric analysis. Mainini has collaborated extensively with researchers such as M. Kružík, D. Percivale, and U. Stefanelli, producing influential papers in journals like Calc. Var. Partial Differential Equations and Arch. Ration. Mech. Anal. . His recent articles (2020–2025) address topics ranging from fractional linear equations to Bayesian nonparametric models. Education includes a PhD in Mathematical Analysis from the University of Genova (2010) and a thesis on Infinite-dimensional porous media equations and optimal transportation . He has organized international conferences and seminars on variational methods and geometric structures. His research emphasizes rigorous mathematical frameworks for physical phenomena, including the linearization of elasticity models and the study of ground states in diffusion-dominated systems. Mainini’s contributions to carbon nanotube geometries and optimal transport theory have been recognized through invited talks at major events, such as the International School of Mathematics “Guido Stampacchia” . His work often explores the interplay between discrete and continuous models, with applications to materials science and geometric optimization.
Dmitriy Bilyk is a Professor in the School of Mathematics at the University of Minnesota, based in Vincent Hall. His research explores harmonic analysis, functional analysis, and discrepancy theory, with applications to geometric inequalities and optimization problems on spheres. Research focuses on: Energy minimization and measure discreteness on spheres Discrepancy theory in arbitrary dimensions Geometric inequalities and spherical optimization Publications address mathematical structures in discrepancy theory, energy optimization, and geometric inequalities, with recent emphasis on sphere packing and measure theory applications. No awards are documented in the provided text. Research is supported by NSF grants and the Simons Foundation Collaboration Grant.
پژوهشگر
Jack Thomas is a postdoctoral researcher at the Laboratoire de Mathématiques d'Orsay, Université Paris-Saclay, under the supervision of Antoine Levitt. He holds a PhD in Mathematics and Statistics from the University of Warwick (2018–2021), supervised by Christoph Ortner, for which he received the Faculty Thesis Prize 2022 (joint winner). His research focuses on the mathematical analysis of electronic structure models in materials science and quantum chemistry, with a particular emphasis on tight binding models and their applications in predicting material properties. Education: PhD in Mathematics and Statistics, University of Warwick (2018–2021) MSc in Mathematics and Statistics, University of Warwick (2017–2018) MMath in Mathematics, University of Warwick (2013–2017) Research Interests: Jack’s work bridges mathematical rigor and computational methods, addressing challenges in electronic structure analysis, including locality properties of interatomic interactions, body-ordered approximations, polynomial approximation of symmetric functions, and nearsightedness in materials. His contributions advance the theoretical foundations for multi-scale models and machine learning interatomic potentials. Awards & Contributions: Recipient of the Faculty Thesis Prize 2022 (Warwick) Organized the SPAAM student seminar series (2019/20) Active member of SIAM and IMA Teaching: Jack has extensive supervision experience at the University of Warwick, covering modules such as Mathematical Analysis, Linear Algebra, and Multivariable Calculus. He has also contributed to marking and course design for first-year mathematics students. Labs & Collaborations: He is affiliated with the Laboratoire de Mathématiques d'Orsay and collaborates with researchers such as Christoph Ortner (Warwick), Huajie Chen (Beijing Normal University), and Gábor Csányi (University of Cambridge).
استادیار
Alex Wein is an Assistant Professor of Mathematics at the University of California, Davis. His research bridges theoretical computer science, statistics, and probability, with a focus on the mathematical foundations of data science. Key areas include understanding optimal algorithms for signal detection in noise, computational complexity of statistical inference (especially via the low-degree polynomial framework), tensor analysis, and applications of group actions in computational problems. Research Interests: Mathematics of data science: optimal algorithms for hidden structure detection Computational-statistical gaps via low-degree polynomials Tensors: computational challenges and applications Bayesian inference and connections to statistical physics Group actions in molecular structure determination and representation theory Recent Talks: Banff International Research Station (2024): 'Optimality of AMP Among Low-Degree Polynomials' Bernoulli-IMS Symposium (2020): 'Low-Degree Framework for Statistical Inference' Professional Service: Program committee member for COLT, STOC, FOCS Organizer of workshops on computational complexity and statistical inference
استادیار مدعو
Hong Ye Tan is currently a Hedrick Assistant Adjunct Professor in Computational and Applied Mathematics at the University of California, Los Angeles (UCLA), hosted by Professor Stanley Osher. Previously, he completed his PhD at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics as a member of the Cambridge Image Analysis group and the Cantab Capital Institute for the Mathematics of Information, supervised by Professors Carola-Bibiane Schönlieb, Subhadip Mukherjee, and Junqi Tang with funding from GSK.ai. His educational trajectory is exceptional: admitted to the University of Hong Kong at age 11 in 2015 (youngest in recent history) and to Cambridge at age 13 for doctoral studies. He passed his PhD thesis with no corrections, focusing on provably convergent algorithms leveraging geometric structures in data. Tan's research centers on machine learning theory, specifically investigating why learning succeeds through interactions between problem structure, data distributions, optimizers, and network architectures. His work bridges differential geometry (manifold hypothesis, intrinsic complexity), optimization (convex learning-to-optimize, Plug-and-Play inverse problems), and sampling theory (noise-free MCMC methods). He develops theoretically grounded algorithms with practical applications in imaging and unsupervised learning, emphasizing provable convergence guarantees derived from classical mathematics. Analysis of his 13 recent publications reveals a cohesive research program connecting optimal transport theory, manifold learning, and regularization techniques. His work demonstrates how geometric insights enable efficient solutions for high-dimensional problems, particularly in image analysis where dimensionality effects transform from curse to blessing. Key themes include Wasserstein proximal methods, dataset distillation via quantization, and accelerating mirror descent through equivariance. His scientific recognition includes: Masason Foundation Fellowship GSK.ai PhD Fellowship Tan has secured research funding through the GSK.ai PhD studentship and operates within Professor Stanley Osher's group at UCLA. He maintains active collaborations from his Cambridge tenure, particularly with the Cambridge Image Analysis group. Notably, he handles 100% of coding and 98% of writing for first-author publications, actively encouraging code reuse by the community. His work continues to explore foundational questions in learning theory while developing practical tools for inverse problems and imaging science.
پژوهشگر
Dr. Jackson Fliss is a Researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP), part of the Faculty of Mathematics at the University of Cambridge. His research focuses on quantum gravity, topological field theories, and entanglement entropy, with a particular emphasis on Chern-Simons theory, BF theory, and their applications to gravitational systems. He explores topics such as Wilson spools, edge modes, and quantum fluctuations in de Sitter and three-dimensional spacetimes. Fliss’s work bridges theoretical physics and mathematical rigor, addressing foundational questions in quantum field theory and cosmology. His recent publications investigate null energy conditions, non-minimal couplings, and the interplay between matter and quantum gravitational effects. His research often involves advanced techniques like topological entanglement analysis and loop quantum gravity formalisms. While no formal awards or grants are explicitly mentioned, his contributions to understanding entanglement in topological systems and quantum gravity are notable within the field. Fliss collaborates closely with DAMTP’s High Energy Physics research group and maintains an active publication record in leading physics journals.
Antoine Deza is a Professor in the Department of Computing and Software at McMaster University, part of the Faculty of Engineering. His research focuses on applied computing, theory of computation, and digital & smart systems, with a strong emphasis on discrete geometry, optimization algorithms, and combinatorial problems. He is actively involved in optimizing systems for transportation, energy, and infrastructure, leveraging geometric and computational methods. His work explores geometric structures such as polytopes and zonotopes, with applications to network design, stochastic modeling, and resource allocation. Key themes include sparsity-inducing norms, congestion management in charging networks, and robust optimization for industrial processes. He also contributes to foundational topics in discrete mathematics, including packing problems and lattice geometry. Deza collaborates on interdisciplinary projects, particularly in digital systems and smart infrastructure. He is currently accepting graduate students and maintains a lab focused on advancing optimization techniques and their practical applications. His research bridges theoretical insights with real-world challenges in engineering and computational science.
Wade Naylor is a Lecturer in the School of Education at the Faculty of Education and Arts, focusing on physics education research and theoretical physics. His work spans student misconceptions in STEM education, pedagogical innovations, and the impact of online learning transitions. He has contributed to studies on cultural diversity in physics education and psychometric testing methodologies. Naylor's earlier research explored cosmology, black hole physics, and quantum field theory, including studies on inflationary power spectra and gravitino dynamics in black hole spacetimes. Research Interests: His dual focus bridges physics education and theoretical physics. In education, he examines conceptual understanding development, assessment design, and the effects of global pandemics on teaching methodologies. In theoretical physics, he investigates cosmological inflation models, quantum vacuum phenomena, and gravitational dynamics in higher-dimensional spacetimes. Key Contributions: Recent work includes analyzing dominant misconceptions among South African physics students and designing evidence-based scientific reasoning tasks for teacher education. Earlier contributions address logarithmic divergences in inflationary models and gravitino field behavior in black hole geometries. Awards/Grants: No specific awards or grants listed in the provided materials. His research has been published in journals like Physics Education , European Journal of Physics , and Journal of Cosmology and Astroparticle Physics . Labs/Teams: Affiliated with interdisciplinary education research groups and theoretical physics collaborations, though specific lab affiliations are not detailed in the text.
Ákos Horváth is a Professor at the Department of Geometry, Budapest University of Technology and Economics. He holds a Doctorate from the Hungarian Academy of Sciences and specializes in advanced geometric studies. His teaching includes courses such as Geometry 2, Non-Euclidean Geometry, and Descriptive Geometry for engineering students. His research focuses on non-Euclidean geometries, convex geometries, Minkowski spaces, and discrete geometry, with significant contributions to hyperbolic plane analysis and geometric optimization problems. Recent research highlights include studies on seashell geometry, affine constructions of conic sections, and extremal problems involving simplices. His work frequently explores metric properties in normed spaces and the interplay between geometric structures and algebraic methods. Collaborations extend to international journals and platforms like MTMT, ResearchGate, and Google Scholar. No specific awards are listed, but his extensive publication record reflects sustained academic engagement. His advising and grants remain unspecified in available texts, though his professional resume may provide further details. He actively contributes to the department's seminar programs and maintains a personal website with research resources.
Friedrich Martin Schneider is a mathematician affiliated with the Institute of Discrete Mathematics and Algebra at the Faculty of Mathematics and Computer Science , Technische Universität Freiberg. His research focuses on topological groups, functional analysis, ergodic theory, and dynamical systems, often intersecting with algebra and geometry. His work explores exotic groups without non-trivial unitary representations, measure concentration phenomena, and the escape property in topological groups. He has contributed to the MacWilliams extension theorem for infinite rings, the Liouville property in random walks, and the classification of profinite algebras. Collaborations with Sławomir Solecki, Andreas Thom, and Vladimir Pestov highlight his interdisciplinary impact. Recent publications emphasize geometric group theory, invariant means, and applications to computer science, including constraint satisfaction problems and intrinsic dimensionality in data analysis. Funded by the German Research Foundation (DFG), he leads a research group with Paula Kahl, Josefin Bernard, and Yannik Höll.
استاد
Wei Wu is a Professor in the Department of Statistics and an Associate Graduate Faculty member in the Program in Neuroscience at Florida State University (FSU). He holds a Ph.D. in Applied Mathematics from Brown University (2004). His research focuses on interdisciplinary statistical methods for neuroscience, functional data analysis, and computational statistics. Key contributions include frameworks for spike train analysis, statistical depth in point processes, and registration of functional data using Fisher-Rao metrics. Affiliations: Department of Statistics, Program in Neuroscience Education: Ph.D. in Applied Mathematics, Brown University (2004) Research Interests: Functional data registration, nonparametric methods for point processes, neural spike train modeling, statistical depth analysis, and applications in birdsong production and computational neuroscience. He develops tools for analyzing complex data structures in neuroscience and biostatistics, emphasizing geometric and Bayesian approaches. His work spans statistical modeling of neural coding, protein structure comparison, and signal processing under compositional noise. Recent publications include advancements in stochastic models for time warping and depth-based statistical inference in point processes. Collaborations: Active with researchers in neuroscience, mathematics, and bioinformatics at FSU and other institutions.
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