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Demeter KrupkaView profile

Professor

• Calculus of Variations on Smooth Manifolds
• Natural Bundles, Jets, and Differential Invariants
• Tensor Algebra
• +1 more

Demeter Krupka is a Professor of Geometry and Topology at the Lepage Research Institute in Slovakia, serving as founding editor of Differential Geometry and Its Applications . His work bridges pure mathematics and theoretical physics through geometric frameworks for physical theories. His research focuses on calculus of variations on smooth manifolds , natural bundles and jets , differential invariants , and tensor algebra . Recent publications (2015–2025) reveal consistent exploration of principal bundle structures, Schwarzschild spacetime topology, Grassmann fibrations, and higher-order homogeneous functions, demonstrating deep integration of geometric methods in variational problems. Krupka’s scholarly impact includes editorial roles for Journal of Geometry and Physics and International Journal of Geometric Methods in Modern Physics , alongside Czech-language textbooks on quantum mechanics, general relativity, and differential invariants. He maintains active collaborations with the University of Prešov, Eötvös Loránd University, and the Italian Society for General Relativity and Gravitation.

Jackson KulikView profile

Assistant Professor

• Astrodynamics
• Guidance, Navigation, and Control
• State Estimation
• +6 more

Jackson Kulik is an Assistant Professor in the Mechanical and Aerospace Engineering Department at Utah State University's College of Engineering. He leads the Dynamics, Control, and Estimation (DyCE) Lab and is part of the Autonomy, Robotics, Control, and Optimization (ARCO) Group, focusing on cislunar astrodynamics, rendezvous operations, and space situational awareness. Education: PhD in Applied Mathematics, Cornell University, 2024. Dissertation: "Linear and Multilinear Methods for Guidance, Navigation, and Control of Satellite Formation Flight". BS in Mathematics, Texas Tech University, 2020. Dr. Kulik's research centers on advanced guidance, navigation, and control systems for space applications, leveraging higher-order tensor methods for partial derivatives and statistical moments. His work spans astrodynamics, state estimation, and trajectory optimization with emphasis on cislunar navigation, spacecraft proximity operations, and orbital uncertainty modeling. Recent publications (2024-2025) reveal a strong focus on linear covariance analysis fidelity, non-Gaussian uncertainty propagation, and reachable set theory for spacecraft dynamics. These works integrate tensor mathematics with practical space mission challenges, particularly in satellite formation flight and space domain awareness. Scientific Awards: DOD NDSEG Fellowship (2021) Dr. Kulik mentors five graduate students including PhD candidates Alex Nelson (RPO/Cislunar GN&C), Braden Hastings (Situational Awareness), and Allan Lyon (stochastic navigation), plus Master's student Zack Fraser (GN&C engineering). His research is supported by Department of Defense funding, building on his NDSEG Fellowship background. The DyCE Lab operates within USU's ARCO Group, developing novel mathematical frameworks for space system dynamics and control. Current projects address cislunar navigation challenges, spacecraft rendezvous algorithms, and space object tracking using tensor-based statistical methods.

• Financial Mathematics
• Systemic Risk
• Energy Markets
• +4 more

Prof. Nils Detering is a Professor at the Chair of Financial and Actuarial Mathematics at Heinrich Heine University Düsseldorf. He previously held a tenure-track Associate Professor position at the University of California, Santa Barbara. His research focuses on systemic risk in financial systems, energy market modeling, and machine learning applications in finance. He has contributed over 20 peer-reviewed articles in top journals like Finance & Stochastics and SIAM Journal on Financial Mathematics . Education: PhD in Finance from Frankfurt School of Finance & Management and a Mathematics undergraduate degree from Georg-August University Göttingen. His work bridges stochastic analysis, random graph theory, and financial engineering. Key Research Areas: Systemic Risk, Energy Markets, Machine Learning in Finance, Random Graphs Teaching: Probability Theory, Stochastic Processes, Financial Mathematics courses at both undergraduate and graduate levels. Publications highlight methodological innovations in operator learning, reinforcement learning for financial systems, and quantitative risk management frameworks. His 2022 paper on reinforcement learning for banking networks earned a best paper award at the ACM International Conference on AI in Finance. Current affiliations include leading academic initiatives at HHU and collaborating on interdisciplinary projects combining mathematical finance with computational methods.

Yann StrozeckiView profile

Associate Professor

• Enumeration Complexity
• Algorithmic Game Theory
• Simple Stochastic Games
• +7 more

Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.

Yibo XuView profile

Visiting Assistant Professor

• Continuous optimization
• Mixed-discrete programming
• Nonconvex programming
• +7 more

Yibo Xu is a Visiting Assistant Professor in the Department of Mathematics & Statistics at the University at Albany, State University of New York , holding this position since August 2024. PhD in Mathematical Sciences (2018) from Clemson University Postdoctoral Fellow at Clemson University (2021-2024) Postdoctoral Research Associate at Rensselaer Polytechnic Institute (2018-2021) His research focuses on continuous optimization , mixed-discrete programming , and large-scale optimization methods for machine learning . He has also explored convex analysis, game theory, computational algebraic geometry, and cryptography. Recent publications highlight trends in stochastic gradient methods , distributed optimization , accelerated algorithms , and polyhedral analysis for nonconvex problems. Teaching : Courses include Optimization Methods, Machine Learning, and Mathematics for Data Science Advising : Mentored PhD students Yuheng Jiang and Tina Yidan Guo Professional Activities : Session chair roles at INFORMS and Continuous Optimization conferences

Filip StrobinView profile

Associate Professor

• Fractal Geometry
• Functional Analysis
• Iterated Function Systems
• +3 more

Dr. Filip Strobin is an Associate Professor in the Division of Applications of Contemporary Mathematical Analysis at Lodz University of Technology. His research centers on theoretical aspects of fractal geometry and functional analysis. Key research themes include: Topological and geometric properties of fractal attractors Generalizations of iterated function systems (IFS) Embedding theorems for metric spaces Fixed point theory applications to fractals Lineability and spaceability in function spaces Recent work characterizes conditions for connected attractors in IFS, establishes embedding capabilities of fractals in various spaces, and develops algorithms for fractal visualization. His publications frequently appear in analysis and topology journals.

Esmaeil Babaei KhezerlooView profile

Lecturer

• Financial Mathematics
• Stochastic Dynamical Systems
• Stochastic Optimization
• +1 more

Dr Esmaeil Babaei Khezerloo serves as a Lecturer in Statistics at Manchester Metropolitan University (MMU), teaching core courses in Statistics and Financial Mathematics (6G5Z3018) and Mathematics and Statistics for Data Science (6G4Z0025). His academic trajectory includes prior roles as a Teaching Fellow in Financial Mathematics at the University of Leeds, Lecturer in Applied Mathematics at the University of Glasgow, and Teaching Associate in Economics at the University of Manchester. Education PhD in Economics, University of Manchester, 2019 PhD in Mathematics, Sahand University of Technology, Iran, 2014 Research Profile His expertise centers on Financial Mathematics with advanced applications in Stochastic Dynamical Systems and Stochastic Optimization, leveraging Multilinear Algebra frameworks. This interdisciplinary work bridges quantitative finance and complex systems modeling, focusing on uncertainty quantification and nonlinear optimization techniques for real-world financial scenarios. Scientific Recognition No specific awards or fellowships documented in source materials Academic Mentorship Actively supervises Master's and undergraduate research projects in Financial Mathematics and Data Science, emphasizing practical applications of stochastic modeling. No research grants explicitly referenced in available documentation. Research Infrastructure Operates from Dalton Building (Room 3.32) at MMU with dedicated Thursday office hours (14:00-16:00), though no specialized laboratories or formal research teams are specified in current profiles.

Loukas GrafakosView profile

Professor

• Harmonic Analysis
• Fourier Analysis
• Functional Analysis
• +4 more

Loukas Grafakos is a Curators Distinguished Professor and Mahala and Rose Houchins Distinguished Professor in the Department of Mathematics at the University of Missouri. His expertise centers on harmonic analysis, Fourier theory, and multilinear operators. He received his PhD from UCLA in 1989 under Michael Christ, followed by positions at Yale University and Washington University before joining Missouri in 1994. Research Focus: Grafakos develops theoretical frameworks in harmonic analysis, including multilinear singular integrals, maximal functions, and Fourier multiplier theorems. His work has applications in partial differential equations and functional inequalities. Publications: Author of foundational texts like Classical Fourier Analysis (3rd ed.) and Modern Fourier Analysis (3rd ed.), used globally in graduate mathematics. His 150+ articles advance operator theory and mathematical analysis. Honors: Recipient of the Kemper Teaching Award (2002), Chancellor's Research Award (2008), and Simons Fellowship (2021). Funded by NSF and Simons Foundation grants since 1996. Editorial Service: Sits on boards of Mathematische Annalen , Journal of Geometric Analysis , and others.

Kevin Charles LeonardView profile

Professor

• Electrocatalysis
• Sustainable Energy
• Data Science
• +3 more

Kevin C. Leonard is Professor of Chemical and Petroleum Engineering at the University of Kansas, with joint appointment at the Center for Environmentally Beneficial Catalysis. He leads research on sustainable electrochemical technologies for fuel and chemical production, with focus areas in electrocatalysis, scanning electrochemical microscopy, CO₂ utilization, and machine learning for catalyst discovery. His research develops novel electrochemical systems for renewable hydrogen production, CO₂ conversion, and sustainable chemical synthesis. Current projects include federated learning approaches for catalyst discovery, CO₂-expanded electrolytes for chemical production, and scanning electrochemical microscopy for catalyst characterization. Professor Leonard has received significant research funding from NSF, Department of Energy, and Cisco Systems. Awards include the ACS Sustainable Chemistry & Engineering Lectureship Award and Army Research Office Young Investigator Award. He co-founded Avium, LLC to commercialize hydrogen production technology. He teaches undergraduate and graduate courses in chemical engineering fundamentals, kinetics, catalysis, and data science. Professor Leonard currently advises 7 graduate students working on electrocatalysis, machine learning applications, and electrochemical characterization techniques.

Kyle A CaudleView profile

Professor

• Forecasting
• Time Series Analysis
• Multilinear Algebra
• +3 more

Kyle A. Caudle is a Professor of Mathematics at the South Dakota School of Mines and Technology, holding a Ph.D. from George Mason University, an M.S. from Salve Regina University, and a B.A. from Western State College. His research spans forecasting, tensor analysis, and anomaly detection, with applications in engineering and data science. Caudle develops computational tools for time series forecasting, tensor decomposition, and graph representation learning. He created software packages like Flow Field, rTensor2, and LTAR, published on CRAN. His interdisciplinary projects include collaborations with NIST and Naval Surface Warfare Centers on surface ship maintenance and anomaly detection. Publications emphasize multilinear algebra, temporal forecasting, and machine learning. Recent work advances tensor factorization for high-dimensional data, hierarchical graph networks, and deep generative models. Awards include the 2015 Peter Holmes Prize for innovative statistics teaching and accreditation as a Professional Statistician (ASA, 2013). He mentors graduate students and co-developed the Ph.D. in Data Science.

Jiawang NieView profile

Professor

• Polynomial Optimization
• Semidefinite Optimization
• Data Science Optimization
• +2 more

Jiawang Nie is a Professor of Mathematics at the University of California, San Diego, where he conducts research at the intersection of optimization, algebraic geometry, and computational mathematics. His work focuses on developing theoretical foundations and practical algorithms for solving complex polynomial optimization problems with applications across various scientific domains. His primary research interests include Polynomial and Semidefinite Optimization , Data Science Optimization , and Matrix and Tensor Computation . These areas form the backbone of his theoretical contributions to optimization theory, with particular emphasis on moment problems, sum of squares methods, and applications to tensor computations and machine learning. Nie's recent publications demonstrate a consistent focus on advancing the theory and applications of polynomial optimization. His work spans from fundamental theoretical developments in moment problems and semidefinite programming to practical applications in multi-view learning, tensor decomposition, and game theory. A notable trend is his increasing focus on connecting optimization theory with machine learning applications, particularly in handling high-dimensional data structures through tensor methods. Professor Nie is actively involved in the academic community through teaching and seminar organization. He teaches graduate courses including Math 271C (Nonlinear Nonconvex Optimization) and runs the UCSD Optimization and Data Science Seminar (Math 278C), which has been ongoing since at least Winter 2017 and continues into Spring 2025. He authored the book Moment and Polynomial Optimization (SIAM, 2023), which serves as a comprehensive resource in the field. His academic contributions extend beyond publications to educational resources, with numerous graduate-level courses taught at UCSD covering optimization theory and applications. His research demonstrates a sustained commitment to advancing both the theoretical foundations and practical applications of optimization methods in mathematics and computational science.

Jan DraismaView profile

Professor

• Algebraic Geometry
• Tensor Analysis
• Combinatorics
• +3 more

Prof. Jan Draisma is a Professor of Mathematics at the University of Bern, serving as Program Director for the MSc in Mathematics and a Member of the Institute Management at the Mathematisches Institut (MAI). His research focuses on algebraic geometry, tensor analysis, and their applications, with particular emphasis on polynomial representations, tensor rank, and geometric invariant theory. He has made significant contributions to understanding asymptotic tensor ranks, topological Noetherianity, and combinatorial algebraic structures. His work bridges pure mathematics with computational methods, often addressing foundational problems in algebra and geometry. Recent research trends include exploring singular loci in tensor varieties, the interplay between algebraic density properties and affine manifolds, and advancing techniques in polynomial functors. His articles highlight interdisciplinary approaches, combining geometric, combinatorial, and computational perspectives. Prof. Draisma’s interests span theoretical advancements and practical applications, such as genome reconstruction from biological data and the development of efficient algorithms for tensor analysis. His contributions reflect a commitment to advancing both fundamental mathematical theory and its real-world relevance.

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +3 more

Charles Smart is a Professor of Mathematics at Yale University, located at Kline Tower - 219 Prospect Street, New Haven, CT 06511. His research focuses on partial differential equations, probability theory, and stochastic homogenization, with notable contributions to elliptic equations, statistical physics, and discrete models such as the Abelian sandpile. He holds a prominent academic position within the Department of Mathematics at Yale's Faculty of Arts and Sciences. Smart's work bridges theoretical mathematics with applications in physics and engineering, addressing topics like unique continuation properties, localization phenomena, and scaling limits. His recent publications explore advanced concepts such as large-scale analyticity in elliptic systems, free boundary problems, and the interplay between singular integrals and mixing flows. He has contributed to foundational studies in stochastic homogenization, including quantitative error estimates and convex integral functionals. His research also extends to geometric analysis, such as convex hull peeling and facet formation in free boundary problems. No specific grants or awards are listed in the provided information. His advising record remains unspecified, though his academic standing suggests significant mentorship potential.

Bethany LuschView profile

Assistant Professor

• Scientific Machine Learning
• Dynamical Systems
• Supercomputing
• +7 more

Bethany Lusch is an Assistant Computer Scientist in the Data Science Group at the Argonne Leadership Computing Facility, Argonne National Laboratory. She holds advanced degrees in applied mathematics and focuses on integrating artificial intelligence with scientific computing, particularly for dynamical systems and PDE-based simulations. Education: PhD in Applied Mathematics, University of Washington, 2016 MS in Applied Mathematics, University of Washington, 2011 BS in Mathematics (Honors), University of Notre Dame, 2010 Her research centers on scientific machine learning, with emphasis on developing machine-learning emulators for expensive simulations such as climate models and computational fluid dynamics. She pioneers methods that embed domain knowledge into deep learning frameworks, particularly through Koopman operator theory to linearize nonlinear dynamics. Her work spans representation learning, reduced-order modeling, and analysis of supercomputing system logs. The 15 most recent publications reflect a strong trend toward using deep learning—especially recurrent and autoencoder architectures—to model complex physical systems. Key themes include surrogate modeling, latent-space dynamics, closure in reduced-order models, and interpretable AI in scientific contexts. Her work bridges theoretical rigor with practical applications in engineering and geophysics. Scientific Awards: NPSC Fellow, National Physical Science Consortium (now GFSD), Aug 2010 – May 2016 Six-Year Graduate Fellowship, NSF-SUMR Scholarship University of Notre Dame Mathematics Department Scholarship, Aug 2007 – Jun 2010 Bethany Lusch has collaborated extensively with researchers at Argonne and the University of Washington. Her projects often involve interdisciplinary teams working on high-performance computing applications. She has contributed to the development of tools like MELA for visual analytics of HPC logs and has secured competitive fellowships and funding. Her prior role as a Research Associate at the University of Washington (2016–2018) preceded her current position at Argonne (2018–present), indicating a continuous research trajectory. She is actively involved in advancing AI-driven scientific discovery, with ongoing work in universal embeddings for PDEs and multifidelity modeling. Her lab affiliations include the Argonne Leadership Computing Facility, where she leverages world-class supercomputing resources to develop and test her models.

• Mathematics
• Computer Science
• Applied Mathematics
• +6 more

Anthony Nouy is a Professor at the Department of Mathematics, Computer Science, and Biology (MIB) at École Centrale de Nantes. His research spans Mathematics, Computer Science, and Computational Biology, with a focus on approximation theory, tensor networks, and stochastic optimization. His work explores nonlinear manifold approximation dimension reduction in feature spaces stochastic gradient descent optimization moment methods for optimal transport and PDEs model reduction and tensor geometry Recent publications highlight trends in machine learning, numerical analysis, and computational mathematics, particularly through compositional polynomial networks, Poincaré inequality surrogates, and tree tensor formats. Collaborations include researchers like Alexandre Pasco, Philipp Trunschke, and Mazen Ali. He is affiliated with the Jean Leray Mathematics Laboratory and maintains a personal webpage for updates. Co-authors of recent publications include Antoine Bensalah, Joel Soffo, and Alexandre Pasco (2025) Robert Gruhlke, Philipp Trunschke (2024) Clément Cardoen, Swann Marx, Nicolas Seguin (2024) Mazen Ali, Antonio Falcó, Wolfgang Hackbusch (2023)