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William EricksonView profile

Research Fellow

• Representation Theory
• Lie Groups
• Invariant Theory
• +3 more

Dr. William Erickson is a Postdoctoral Research Fellow in the Department of Mathematics at Baylor University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Wisconsin-Milwaukee (2022) and a B.A. from the University of Notre Dame (2010). Before pursuing his doctorate, he taught middle/high-school mathematics and Latin for eight years, while also managing a local pool as a lifeguard and swim instructor. His research focuses on the representation theory of Lie groups from a combinatorial perspective, invariant theory, and algebraic statistics, with notable contributions to combinatorial algebra and geometric probability. Educations: Ph.D., Mathematics, University of Wisconsin-Milwaukee, 2022 (Advisor: Jeb Willenbring) B.A., Mathematics, University of Notre Dame, 2010 Research Interests: Dr. Erickson explores the intersection of algebra, combinatorics, and geometry, with a focus on Lie groups and their representations. His work often employs combinatorial tools to study invariant rings, Young tableaux, and algebraic statistics. Recent projects include analyzing the earth mover’s distance in probabilistic contexts and developing new methods for reconstructing Young tableaux via minors. Publications Trends: His articles span combinatorial algebra, representation theory, and applied statistics. Notable topics include Demazure products, palindromicity in generating functions, and geometric interpretations of statistical metrics. His work frequently bridges pure and applied mathematics, with applications ranging from optimization to geometric analysis. Scientific Awards & Grants: No awards or grants explicitly listed in the provided text. Advising & Teams: No formal advisees or lab teams explicitly mentioned. His collaborative work includes co-authors like Rebecca Bourn, Jan Kretschmann, and Markus Hunziker.

Evan GawlikView profile

Associate Professor

• Numerical Analysis
• Finite Element Methods
• Computational Fluid Dynamics
• +3 more

Evan Gawlik is an Associate Professor in the Department of Mathematics and Computer Science at Santa Clara University (2024–present), previously serving as Associate Professor (2023–2024) and Assistant Professor (2018–2023) at the University of Hawaii. He holds a Ph.D. in Computational and Mathematical Engineering from Stanford University (2015) and a B.S. in Applied and Computational Mathematics from the California Institute of Technology (2010). Education : Ph.D., Stanford University, 2010–2015 B.S., California Institute of Technology, 2006–2010 Research Interests : Focused on numerical analysis, finite element methods, and structure-preserving algorithms for partial differential equations. His work emphasizes geometric numerical integration, computational fluid dynamics, and applications in magnetohydrodynamics (MHD) and general relativity. Key areas include finite element discretization of differential forms, error analysis on manifolds, and thermodynamically consistent numerical schemes. Grants & Funding : NSF Grant DMS-1703719 (2017–2020) ANR Grant GEOMFLUID (ANR-14-CE23-0002-01) Labs & Collaborations : Active in computational mathematics research groups, including contributions to open-source finite element software and collaborations on geometric numerical methods. His GitHub repositories (e.g., egawlik ) showcase code implementations of his research, such as variable density Euler equations solvers.

Arumugam NallanathanView profile

Professor

• Wireless Communications
• Networking
• 6G Technology
• +6 more

Arumugam Nallanathan is a Professor of Wireless Communications at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. He leads the Communication Systems Research (CSR) Group and is a core member of the Centre for Networks, Communications and Systems. His research focuses on 6G technologies, AI-driven wireless systems, reconfigurable intelligent surfaces (RIS), UAV-enabled communications, ultra-reliable low-latency communications (URLLC), and IoT applications. He teaches the advanced 5G Mobile and Beyond module, integrating theoretical frameworks with practical implementations. Nallanathan's work bridges cutting-edge research with real-world challenges, emphasizing secure and energy-efficient communication systems. His recent publications explore topics such as IRS-based DOA estimation, covert communication techniques, and federated learning in edge networks. He actively contributes to the design of integrated sensing and communication systems, with applications in smart cities and beyond. His research group collaborates on grants addressing future network architectures and AI-enabled solutions. Research interests span: 6G systems, AI for communications, RIS optimization, UAV networks, URLLC protocols, and IoT security. His teaching emphasizes 5G fundamentals and emerging technologies. Notable contributions include advancements in STAR-RIS aided communication, multi-modal learning for satellite-ground networks, and secure resource allocation in NOMA systems. He leads projects on edge intelligence, digital twins for URLLC, and generative AI for channel estimation. The CSR Group actively engages in industry partnerships to translate research into practical solutions.

• Geometry and Topology
• Geometric Analysis
• Differential Geometry
• +7 more

Cedric Martinez Campos is an Assistant Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia. He is a member of the Geometric Analysis Group U.V. (GAGUV) and specializes in Geometry and Topology. His academic work is deeply rooted in geometric mechanics and mathematical physics. Research Interests: Geometric methods in classical and field theories Differential geometry and topology Variational calculus and optimal control Geometric numerical integration (variational integrators) Hamilton-Jacobi theory Constrained and dissipative systems His recent publications reveal a strong focus on applying advanced geometric structures—such as contact, multisymplectic, and k-contact geometries—to formulate and analyze mechanical and field theories, especially those involving dissipation and constraints. He develops high-order numerical methods grounded in variational principles, contributing to both theoretical and computational mechanics. Scientific Contributions: Over 16 publications in major mathematical physics journals Significant work on higher-order field theories and Lagrangian submanifolds Development of geometric integrators for optimal control Contributions to the Hamilton-Jacobi formalism in field theory He has collaborated extensively with leading figures such as Manuel de León, David Martín de Diego, and J. M. Sanz-Serna. His research has been cited over 90 times, reflecting its relevance in geometric mechanics. He advises no listed students yet but is actively involved in high-level research. He is part of ongoing efforts to extend geometric frameworks to non-conservative and singular systems, indicating a forward-looking research agenda in mathematical physics.

Markus KiderlenView profile

Associate Professor

• Integral Geometry
• Convex Geometry
• Stochastic Geometry
• +2 more

Markus Kiderlen is an Associate Professor at the Department of Mathematics, Aarhus University, specializing in the intersection of geometry, probability theory, and statistics. His research spans integral geometry, convex geometry, stochastic geometry, geometric tomography, and spatial statistics. Primary affiliation: Department of Mathematics, Aarhus University Academic rank: Associate Professor His work in stochastic geometry involves integral geometric relations, Minkowski tensors for quantifying random particles, and uniqueness/inference problems in geometric tomography (e.g., solving Hammer's X-ray problem). Another key focus is spatial sampling methods, including Wicksell's corpuscle problem and Cavalieri volume estimation improvements. Recent research trends include geometric reconstruction algorithms, stereological techniques for nonconvex particles, and rotational integral formulae for convex bodies. He has also explored polarizations in rearrangement processes and their applications. Markus Kiderlen has presented at international conferences such as MCQMC (2022), Stochastic Geometry Workshops (2019-2022), and the WIASABISS workshop (2018). His publications reflect collaborations with researchers like M. Stehr, R. Eriksen, and D. Hug.

Tim SeynnaeveView profile

Research Fellow

• Algebraic Geometry
• Applied Algebra
• Combinatorics
• +4 more

Tim Seynnaeve is a postdoctoral researcher at KU Leuven, holding a dual affiliation with the Department of Computer Science and Mathematics. He serves as an FWO special research associate within the Numerical Analysis and Applied Mathematics (NUMA) research unit at the Arenberg campus. Previously, he completed his PhD at the Max Planck Institute for Mathematics in the Sciences in Leipzig under the supervision of Mateusz Michałek and held a postdoctoral position at the Mathematical Institute of the University of Bern. His research focuses on the intersection of algebraic geometry with computational mathematics, particularly applied and combinatorial algebraic geometry. Seynnaeve's work bridges theoretical mathematics with practical applications in quantum information theory, tensor networks, and computational complexity. His approach combines deep theoretical insights with computational techniques to solve problems in matrix analysis, representation theory, and combinatorial structures. Seynnaeve's recent publications reveal a strong trend toward interdisciplinary work connecting algebraic geometry with quantum information science, statistical models, and computational complexity. His research on matrix product states, tensor spaces, and algebraic structures demonstrates how geometric methods can advance our understanding of quantum systems and computational algorithms. The recurring themes in his work include the application of Schubert calculus to statistical models, the geometry of tensor networks, and the algebraic foundations of matrix multiplication complexity. Seynnaeve actively contributes to the academic community through organizing seminars, including the NUMA seminar at KU Leuven since Fall 2022. He has mentored students such as Jonas Adams on projects related to Group-Invariant Tree Tensor Networks. His teaching experience spans multiple institutions, including courses on Algebra II, Representation Theory, and specialized workshops on nonlinear algebra and tensor applications. His academic work is organized around collaborative research projects with institutions including the Max Planck Institute for Mathematics in the Sciences and participation in numerous international conferences on algebraic geometry and its applications. Seynnaeve maintains active collaborations with researchers across Europe, particularly in the Netherlands, Germany, and Switzerland, contributing to a vibrant network focused on the applications of algebraic geometry to real-world problems.

François RoueffView profile

Professor

• Statistics
• Signal Processing
• Time Series Analysis
• +3 more

François Roueff is a Professor at TELECOM Paris, part of the Institut Polytechnique de Paris. He holds a position within the Laboratoire Traitement et Communication de l'Information (LTCI). His research focuses on statistical signal processing, time series analysis, stochastic processes, and mathematical statistics. He has been actively involved in supervising 14 PhD students, contributing to advancements in statistical methodologies and their applications in various domains. Key research interests include wavelet analysis, long-memory processes, and nonparametric estimation. His work spans theoretical contributions to applied problems such as energy spectrum reconstruction and network traffic analysis. Recent publications emphasize methodological advancements in machine learning, variational inference, and tensor factorization for multi-site energy management. Roueff’s articles demonstrate expertise in time series modeling, stochastic processes, and statistical theory. Notably, his work on fractional calculus and operator theory has expanded the understanding of Hilbert space-valued processes. He has collaborated on projects involving signal processing for nuclear spectroscopy and infrasound detection, showcasing interdisciplinary applications. His advising contributions include co-supervising prominent students like Tepmony Sim (2016) and Amaury Durand (2022). Research grants and collaborations have enabled advancements in statistical methodologies for modern data analysis challenges.

Petros ValettasView profile

Associate Professor

• Convex Geometry
• Functional Analysis
• Probability Theory
• +4 more

Petros Valettas is an Associate Professor at the University of Missouri, holding dual appointments in the Department of Mathematics and the Department of Electrical Engineering and Computer Science. His research focuses on Asymptotic Convex Geometry, Geometric Functional Analysis, High-Dimensional Probability, and Probabilistic Methods in Analysis and Geometry. He has taught advanced courses such as High-Dimensional Probability, Analysis of Boolean Functions, and Advanced Calculus I, alongside foundational courses like Discrete Mathematical Structures and Calculus I/II. Dr. Valettas’ work explores the interplay between probability, geometry, and functional analysis, with contributions to topics like concentration inequalities, convex body properties, and stochastic processes. His recent publications address areas such as Gaussian convex bodies, hypercontractivity in normed spaces, and decomposition methods for high-dimensional random arrays. He has authored a monograph, Geometry of Isotropic Convex Bodies , and maintains an active role in academic seminars and curriculum development. His teaching spans undergraduate and graduate levels, emphasizing rigorous mathematical foundations and modern applications. Collaborations with co-authors like Paouris and Giannopoulos highlight his engagement with leading figures in geometric analysis. No scientific awards are explicitly mentioned, though his prolific publication record underscores sustained academic excellence.

Brian WoodView profile

Professor

• Transport Phenomena
• Multiscale Analysis
• Environmental Engineering
• +3 more

Brian Wood is a Professor in the Department of Chemical, Biological, and Environmental Engineering at Oregon State University (OSU), part of the College of Engineering. His research focuses on multiscale systems analysis, particularly in transport phenomena, tissue modeling, and environmental engineering applications. He holds a Ph.D. in Civil and Environmental Engineering from the University of California, Davis (1999), an M.S. in Environmental Engineering from Washington State University (1990), and a B.S. in Civil Engineering from Washington State University (1988). Wood’s research interests span transport of mass, momentum, and energy in natural and engineered systems, including multiscale tissue analysis and cancer biology modeling. His work integrates computational methods with experimental data to address challenges in porous media dynamics, biofilm growth, and environmental remediation. Notable contributions include studies on non-equilibrium transport, chemotaxis in granular media, and biofilm architecture visualization using synchrotron-based X-ray tomography. His recent publications (2020–2025) emphasize advancements in upscaling techniques for porous media flow, machine learning applications in biomedical modeling, and regulatory frameworks for end-user control systems. He leads the Computation and Simulation research group at OSU, fostering interdisciplinary approaches to multiscale hydrogeologic modeling. Wood’s research has been supported by collaborative grants focusing on bacterial transport in heterogeneous environments and hybrid multiscale methods. His work bridges theoretical developments in continuum mechanics with practical applications in environmental engineering and biomedical systems.

Michael GoldmanView profile

Researcher

• Partial Differential Equations
• Geometric Measure Theory
• Image Processing
• +5 more

Michael Goldman is a junior CNRS researcher at the Centre de Mathématiques Appliquées (CMAP) of École Polytechnique. His research focuses on Partial Differential Equations (PDEs), Geometric Measure Theory, Optimal Transportation, and Calculus of Variations. He explores applications in material sciences, homogenization, and mathematical physics. Notable contributions include studies on Gamma convergence in superconductors, optimal transport dynamics, and branched transport models. His work bridges theoretical analysis with applied problems, addressing topics like liquid drop models, particle systems, and phase transitions. Goldman actively participates in academic networks, organizing conferences and contributing to seminars such as the Parisian Calculus of Variations group. His research also touches on image processing and geometric flows, reflecting interdisciplinary interests in mathematical modeling and analysis.

Pierre Yves Gaudreau LamarreView profile

Assistant Professor

• Probability
• Functional Analysis
• Mathematical Physics
• +3 more

Pierre Yves Gaudreau Lamarre is an Assistant Professor in the Department of Mathematics at Syracuse University , within the College of Arts and Sciences. Previously, he held a William H. Kruskal Instructorship at the University of Chicago (2020–2023) and earned his Ph.D. in Operations Research and Financial Engineering from Princeton University in 2020, under Mykhaylo Shkolnikov. His research focuses on probability, functional analysis, and mathematical physics, with specialties in random Schrödinger operators, stochastic partial differential equations (SPDEs), random matrices, and free probability. Education: Ph.D., Operations Research and Financial Engineering, Princeton University (2020) M.Sc., Mathematics and Statistics, University of Ottawa (2015) B.Sc., Mathematics, University of Ottawa (2013) Research Interests: His work addresses foundational questions in probability theory and mathematical physics, including spectral properties of random operators, intermittency in stochastic models, and asymptotic behaviors in high-dimensional systems. Recent projects involve analyzing multi-point correlations in the stochastic heat equation, rigidity phenomena in stochastic Airy operators, and phase transitions in singular Anderson models. Awards: NSERC Postdoctoral Fellowship (2020) Teaching & Service: He teaches advanced courses like MAT 526 (Stochastic Processes) and MAT 721 (Probability I). He actively reviews for journals such as the Annals of Applied Probability, Annales Henri Poincaré, and Advances in Mathematics. He also organizes the Finger Lakes Probability Seminar and participates in conference-related service. Labs/Teams: Collaborates with researchers in stochastic analysis, mathematical physics, and random matrix theory, including frequent engagements with institutions like the Isaac Newton Institute and MIT.

Andrea MentrelliView profile

Associate Professor

• Extended Thermodynamics
• Mathematical Physics
• Hyperbolic Equations
• +4 more

Andrea Mentrelli is an Associate Professor of Mathematical Physics at the Department of Mathematics, University of Bologna, and Scientific Coordinator of the Alma Mater Research Center on Applied Mathematics (AM²). He holds a MS in Nuclear Engineering and a PhD in Mechanics of Materials from the University of Bologna. His research focuses on Extended Thermodynamics, High-Performance Computing (HPC), and computational modeling of plasma and fluid dynamics. Key roles include leadership in projects such as PRIN2022 (Mathematics of Nonlinear Wave Propagation) and Combustion-Wave Interactions. He is affiliated with INFN Bologna Section and AM². Notable contributions include shock wave analysis, hyperbolic models for incompressible fluids, and fractional calculus applications in viscoelasticity. His work combines theoretical rigor with numerical methods, addressing challenges in plasma physics, astrophysics, and materials science. **Education:** MS in Nuclear Engineering, PhD in Mechanics of Materials (University of Bologna). **Affiliations:** AM², INFN Section Bologna. **Research Projects:** Over 10+ grants including PRIN2022 and EU-funded initiatives. Extensive publications in journals like *Phys. Rev. D*, *Annals of Physics*, and *Journal of Computational Physics*.

Petr PelechView profile

Researcher

• Calculus of Variations
• Partial Differential Equations
• Continuum Thermodynamics
• +5 more

Dr. Petr Pelech is a researcher at the Faculty of Mathematics and Physics, Charles University (since 2023), and previously at the Weierstrass Institute for Applied Analysis and Stochastics (2020-2022) in the Partial Differential Equations research group. His work spans calculus of variations, continuum thermodynamics, and nonlocal mechanics, with a focus on variational methods for inelastic material modeling. Doctoral degree in Variational Methods in Thermomechanics of Solids (Charles University, 2020) M.Sc. in Mathematical Modelling (Charles University, 2016) B.Sc. in Mathematics (Charles University, 2014) Research interests primarily involve: Rate-independent processes in large-strain inelasticity Gradient flows in Banach spaces via minimizing movement schemes GENERIC framework for visco-elasto-plastic fluid-solid modeling Peridynamics (nonlocal solid mechanics) Thermodynamic consistency in generalized standard solids Recent publications demonstrate interdisciplinary applications in: Biological systems (bacterial division site morphogenesis) Shape memory alloys modeling Non-equilibrium thermodynamics Nonlocal-to-local transitions in mechanics His methodological focus combines variational principles with thermodynamic frameworks, emphasizing both mathematical rigor and physical consistency in modeling complex material behaviors.

Paul SkoufranisView profile

Associate Professor

• Operator Algebras
• Free Probability
• Operator Theory
• +3 more

Paul Skoufranis is an Associate Professor in the Department of Mathematics and Statistics at York University. He specializes in operator algebras, free probability, and functional analysis, with research focusing on bi-free probability extensions and operator theory applications. Skoufranis earned his PhD from UCLA under Dimitri Shlyakhtenko and completed postdoctoral work at Texas A&M University. His research explores connections between free probability, operator algebras, and matrix theory, developing new frameworks for non-commutative probability. Recent publications investigate entropy analogues in bi-free contexts, joint majorization in matrix algebras, and stochastic process applications. Skoufranis maintains active collaborations advancing mathematical foundations of operator spaces. Teaching responsibilities include advanced courses in operator theory and functional analysis. He contributes to the mathematical community through conference presentations and scholarly publications in peer-reviewed journals.

Rolf SulankeView profile

Professor

• Differential Geometry
• Möbius Geometry
• Projective Geometry
• +5 more

Prof. Rolf Sulanke is a renowned mathematician affiliated with Humboldt University of Berlin, where he held the C4 Professorship for Differential Geometry from 1992 until his retirement in 1995. His academic journey began with a Diploma in Mathematics (1955) and habilitation (1964) at Humboldt University, followed by roles as a Scientific Assistant, Senior Assistant, Lecturer, and eventually Professor of Geometry (1975). His research focuses on differential geometry, particularly Möbius geometry, projective geometry, and Cayley-Klein geometries. Key contributions include foundational work on submanifolds in Möbius space, conformal invariants, and the application of E. Cartan’s methods. He authored influential textbooks like Projective and Cayley-Klein Geometries and Introduction to Euclidean Differential Geometry . Publications span over 30 works, including studies on Crofton’s formulas, random convex polyhedra, and Lie algebra applications. His work integrates geometric theory with computational tools, notably Mathematica notebooks for tensor algebra and Möbius geometry visualization. Despite no explicitly listed awards, his academic longevity and textbook contributions highlight his impactful career.