Izak Moerdijk is a Distinguished University Professor at Utrecht University's Mathematical Institute, part of the Faculty of Science. He holds a part-time professorship in Pure Mathematics at the University of Sheffield. His academic career includes roles as Chair of Algebra and Topology at Radboud University Nijmegen (2011–2015) and Professor of Topology at Utrecht University (2002–2011). Moerdijk earned his PhD in Mathematics from the University of Amsterdam in 1985 (cum laude), followed by postdoctoral positions at the University of Chicago and Cambridge. His research focuses on category theory, algebraic topology, topos theory, and applications to mathematical logic. Notable contributions include foundational work on dendroidal sets, operads, and Lie groupoids. He co-authored influential texts like Sheaves in Geometry and Logic (with S. Mac Lane) and Introduction to Foliations and Lie Groupoids (with J. Mrcun). Awarded the prestigious Spinoza Prize (2011) and Descartes-Huygens Prize (2011), Moerdijk is a member of the Dutch Royal Academy of Sciences (KNAW) and Academia Europaea. His recent work emphasizes developing dendroidal set theory and advancing homotopy-theoretic frameworks.
Jayce Robert Getz is an Associate Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. His research focuses on Langlands functoriality, nonabelian Fourier transforms, and relative trace formulae, with applications to automorphic representations and arithmetic geometry. He holds a Ph.D. from the University of Wisconsin (2007), an M.A. from the same institution (2006), and an A.B. from Harvard University (2004). Getz has held visiting positions at Pohang University of Science and Technology (2021, 2024), and collaborated internationally at institutions like the Hebrew University and the Institute for Advanced Study. His work bridges number theory, representation theory, and algebraic geometry. Getz has been awarded prestigious fellowships, including the NSF Postdoctoral Research Fellowship (2007–2010) and the NDSEG Graduate Fellowship (2004–2007). He is a Principal Investigator on multiple NSF grants, including projects on summation formulae and L-functions. His research interests include trace formulae, L-functions, algebraic cycles, and nonabelian harmonic analysis. Recent publications highlight advancements in summation formulae for quadrics, nonabelian circle methods, and automorphic kernel functions. Teaching includes courses on number theory, algebraic structures, and advanced topics in mathematics. Getz collaborates globally, with affiliations at the Postech Mathematics Institute and contributions to international workshops. His work emphasizes foundational questions in arithmetic geometry and the Langlands program.
Alexander V. Marynych is a Professor at Taras Shevchenko National University of Kyiv. He holds a Doctorate with habilitation in Physics and Mathematics and has been recognized with numerous awards including the Gold Medal of the Ukraine Mathematics Competition (2020) and the Alexander von Humboldt Foundation fellowship (2015-2017). His research focuses on stochastic geometry, regenerative random structures, and probabilistic number theory. He has authored over 30 publications in top-tier journals such as Probability Theory and Related Fields and Annals of Probability . Key academic roles include: Guest Professorship at Leopold-Franzens-Universität Innsbruck (2018) Recipient of the President of Ukraine Prize for Young Scientists (2018, 2017) Lead researcher in projects funded by UC Berkeley, Humboldt Foundation, and Polish National Agency Teaching responsibilities include courses on: Algebra and Geometry for undergraduates Probabilistic Analysis of Algorithms Cryptography and Data Security His research portfolio demonstrates significant contributions to limit theorems, random analytic functions, and convex hull analysis through high-dimensional stochastic models.
Eliza O'Reilly is an Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University. Her research focuses on the intersections of stochastic geometry , convex geometry , high-dimensional probability , and statistical learning theory . Her work explores: Nonconvex and convex regularizers in inverse problems Random tessellations and their machine learning applications Spectrahedral regression for convex function approximation Determinantal point processes for modeling repulsive interactions High-dimensional random convex sets and their asymptotic geometry Her research is supported by the National Science Foundation . Recent publications investigate gradient-based dimension reduction, oblique decision trees, and geometric properties of regularizers. She has received her PhD from the University of Texas at Austin and was a postdoctoral scholar at Caltech.
Yihong Wu is the James A. Attwood Professor of Statistics and Data Science at Yale University, where he also serves as Chair of the Department of Statistics and Data Science. His academic career spans prestigious institutions with a focus on theoretical and applied statistical methods. His research bridges information theory and statistics, with applications across multiple domains of data science. Professor Wu's research focuses on the theoretical foundations of high-dimensional statistics, information theory, and optimization. His work explores dimensionality reduction through both intrinsic low-dimensionality (sparsity, smoothness) and extrinsic low-dimensionality (functional estimation). He has made significant contributions to understanding statistical-computational tradeoffs in problems involving random graphs and combinatorial structures. His research has important applications in machine learning, network analysis, and signal processing. His recent publications reveal a strong focus on information-theoretic approaches to statistical problems, with particular emphasis on graph matching, empirical Bayes methods, and high-dimensional inference. Wu's work consistently addresses fundamental questions about the limits of statistical estimation and the computational feasibility of achieving those limits. His research spans theoretical foundations while maintaining relevance to practical data analysis challenges. Professor Wu actively contributes to academic education through multiple graduate-level courses including Information Theory, Statistical Inference on Graphs, and Topics in High-Dimensional Statistics and Information Theory. His teaching reflects his research interests, emphasizing mathematical rigor and theoretical foundations.
Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).
Travis Schedler is a Professor of Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on algebraic and geometric structures in mathematical physics, including symplectic resolutions, Poisson varieties, and representation theory. He has developed novel homology theories such as Poisson-de Rham homology and introduced nondegeneracy conditions like 'holonomicity.' Education history is not explicitly detailed in the text, but his academic career includes a five-year fellowship (2008–2013) from the American Institute of Mathematics and NSF funding. He teaches advanced modules such as Representation Theory and Algebraic Geometry, offering both handwritten lecture notes and typed materials. In Spring 2019, he delivered a TCC course on Symplectic Resolutions and Singularities, with comprehensive lecture notes and problem sheets. His research spans Hochschild (co)homology, log symplectic manifolds, preprojective algebras, and A-infinity/L-infinity algebras. He actively contributes to academic discourse through lectures like his talk on Toric Poisson Structures at the 2020 Fields Institute event. Awards include his AIM fellowship and NSF grants. Teaching materials include problem sheets and Slack-based course forums. His work bridges algebraic geometry with mathematical physics, emphasizing structural analysis of geometric and algebraic objects.
Leslie Greengard is a Silver Professor of Mathematics and Computer Science at New York University's Courant Institute, part of the Faculty of Arts and Science. His research focuses on integral equation methods for electromagnetics, acoustics, plasma physics, and fluid dynamics, with recent work extending to kernel-based methods in statistical inference. He leads the Courant Mathematics and Computing Laboratory and contributes to novel electromagnetic simulation techniques. Education: Ph.D. and M.D. in Computer Science and Medicine from Yale University (1987) B.A. in Mathematics from Wesleyan University (1979) Research Interests: Greengard's group develops fast solvers for complex geometries in electromagnetics and fluid dynamics. Notable contributions include the Fast Multipole Method (FMM) for particle simulations and Quadrature by Expansion (QBX) for layer potential evaluations. His work bridges computational mathematics and applications in physics, engineering, and biomedicine. Recent Article Trends: Recent publications emphasize fast algorithms (e.g., FMM, adaptive Gauss transforms), biomedical applications (e.g., deep brain stimulation modeling), and cosmological simulations. His methods address challenges in high-dimensional data, multiscale problems, and real-time computation. Awards & Recognition: While no specific prizes are listed, his foundational work on FMM has had a transformative impact on computational science. Advising & Collaborations: Collaborations span academia and industry, with co-authors including V. Rokhlin, M. O’Neil, and others. No formal advisees are listed here, though his research involves postdoctoral fellows and graduate students. Labs & Teams: Active in the Courant Mathematics and Computing Laboratory, focusing on electromagnetic simulation and design methodologies.
Marwa Chafii is an Associate Professor of Electrical Engineering at New York University (NYU) Abu Dhabi and an associated faculty member at the NYU Tandon School of Engineering. She joined NYU Abu Dhabi in 2021 after holding roles as a research group leader at TU Dresden (Germany) and an associate professor at ENSEA (France) with a Chair of Excellence in AI. Her research focuses on advanced waveform design, integrated sensing and communication (ISAC), machine learning for wireless communications, and localization . Education: PhD and Master’s in Electrical Engineering from CentraleSupélec, France. Visiting researcher stints at institutions including Poznan University of Technology, University of York, and University of Oxford. Awards & Recognition: IEEE ComSoc Best Young Researcher Award (EMEA region) Best Editor Award for IEEE Communications Letters (2020) Nominated Top 10 Rising Stars in Computer Networking & Communications (2020) French National Council of Universities (CNU 61) AI Excellence Chair (2018–2022) Best PhD in France (Signal, Image & Vision) Professional Roles: Vice-chair of IEEE ComSoc ETI on Machine Learning for Communications Education Lead for IEEE ComSoc ETI on Integrated Sensing & Communications Associate Editor for IEEE Transactions on Communications and IEEE Communications Letters Research Contributions: Over 50+ publications in top-tier journals/conferences (IEEE Transactions, Globecom, ICC) Active in ISAC, 6G waveform design, and AI-driven communication systems Leading the NYU Wireless Research Lab focused on ISAC and future wireless systems
Rasmus Waagepetersen is a Professor in the Department of Mathematical Sciences at Aalborg University, affiliated with The Faculty of Engineering and Science. His research focuses on spatial statistics, quantitative genetics, and statistical methodology for spatial point processes. He leads and participates in interdisciplinary projects such as urbanLab (spatial data analysis for urban planning) and studies on microbiome interactions in agricultural systems. Key research areas include spatial point processes, Markov chain Monte Carlo methods, and statistical inference for complex ecological and biomedical data. His work frequently involves collaborations with environmental and biological scientists, as seen in projects analyzing root microbiota assemblies in legumes and climate data for building simulations. Waagepetersen has contributed to methodological advancements in spatial statistics, including goodness-of-fit tests, likelihood-based inference for log Gaussian Cox processes, and quasi-likelihood approaches for case-control point pattern data. His research has been supported by grants from institutions like the Villum Foundation. Key Projects: urbanLab, Klimadata til fugtsimuleringer, Nod factor signaling in plant microbiota. Grants: Multiple projects funded by the Villum Foundation and Danish research councils. His recent publications address topics such as space-time point processes, microbiome data analysis, and statistical modeling in education. Waagepetersen maintains an active research group and collaborates internationally on both theoretical and applied statistical problems.
Gui-Qiang G. Chen is a Professor at the Mathematical Institute , University of Oxford, and a Professorial Fellow of Keble College. He serves as Director of the Oxford Centre for Nonlinear Partial Differential Equations (OxPDE) , focusing on nonlinear PDEs, hyperbolic conservation laws, and their applications to fluid dynamics, geometry, and mathematical physics. His research spans Partial Differential Equations , Nonlinear Analysis , Shock Wave Theory , and Free Boundary Problems , with recent work on stochastic PDEs , geometric PDEs , and numerical analysis . His publications cover topics like transonic shocks , hypersonic flow , and compressible fluid dynamics . Gui-Qiang Chen has co-authored 15+ major publications since 2007, including research monographs on shock reflection-diffraction and Prandtl-Meyer reflection configurations , and his work appears in leading journals like Annals of Mathematics and Communications on Pure and Applied Mathematics . Awards and fellowships include: 2024 Polya Prize (London Mathematical Society) Doctor of Science (Oxford, 2024) Member of Academia Europaea (2022) Member of the European Academy of Sciences (2020) Fellow of the American Mathematical Society (2017) SIAM Fellow (2013) Chinese National Prize of Sciences (1990) He supervises DPhil/PhD students in nonlinear PDEs and related fields, and his research is supported by Oxford's mathematical infrastructure and international collaborations.
Holger Dullin is a Professor of Applied Mathematics at the School of Mathematics and Statistics, University of Sydney. His research focuses on Hamiltonian dynamical systems, integrable systems, and their applications in mechanics, fluid dynamics, and biomechanics. He serves as a member of the Sydney Southeast Asia Centre and the Applied Mathematics Research Group. Member of Sydney Southeast Asia Centre Member of Applied Mathematics Research Group Research Interests include: Hamiltonian systems and integrability Quantum and classical monodromy Geometric phase phenomena N-body and rigid body dynamics Volume-preserving mappings His work spans from fundamental mathematical physics to interdisciplinary applications in biomechanics and space engineering , with recent studies on light sail stabilization and geometric phase optimization in diving. Publications cover 2024 to 2003, showing sustained contributions to nonlinear dynamics , stability analysis , and symplectic geometry . Teaching includes advanced courses in Hamiltonian dynamics and nonlinear ODEs. Current research students include Babak Attarha and Gleb Palshin , with past supervision of William Tong (PhD 2016) on geometric phases in diving.
Arthemy Kiselev is an Assistant Professor at the University of Groningen's Faculty of Science and Engineering, specifically within the Department of Mathematics at the Johann Bernoulli Institute. He has been working at the Chair of Algebra since January 2011, contributing significantly to mathematical physics research. His educational background includes: (Under)graduate studies at Lomonosov Moscow State University (summa cum laude, 2001) and Independent University of Moscow PhD in mathematical physics (2004) Professor Kiselev's research focuses on the interface of (super)geometry and quantisation, particularly examining the (non)commutative geometry of Kontsevich's deformation and Batalin-Vilkovisky's approaches to quantisation of gauge field models. His work centers on deformation quantisation, BV quantisation, geometry of differential equations, Poisson geometry, and brackets. He has developed algebraic and geometric tools for the mathematical language of fundamental physics, with particular emphasis on the geometry of variations in Batalin-Vilkovisky formalism and Kontsevich's deformation quantization. His fingerprint in research shows strong connections to Cocycle Mathematics (100%), Poisson Bracket Mathematics (95%), Vector Field Mathematics (77%), Manifold Mathematics (65%), Poisson Structure Mathematics (51%), and Partial Differential Equation Mathematics (41%). His recent publications (2023-2024) demonstrate continued exploration of Kontsevich graphs acting on Nambu-Poisson brackets, star-products for affine Poisson brackets, and associativity properties in deformation quantization. These works show a consistent focus on understanding the mathematical structures underlying quantization procedures, with particular attention to graph complexes, cocycles, and their applications to Poisson geometry. His research reveals deep connections between algebraic structures, differential geometry, and theoretical physics. Scientific recognition includes: NWO VENI post-doctoral grant at Mathematical Institute Utrecht (2008-2010) Throughout his career, Kiselev has given 107 international talks at mathematics and theoretical physics research seminars. His collaborative work with PhD and master's students, such as M.S. Jagoe Brown and F. Schipper, has produced significant results in Poisson geometry and deformation quantization. His research has been supported by various institutions including visits to prestigious centers like IHES (France), MPIM (Germany), CRM (Montreal, Canada), and SISSA (Trieste, Italy). He has held positions at institutions including ISPU in Ivanovo, Russia (as docent since 2009). Kiselev is an active member of the Geometry and Quantum Theory (GQT) research group, contributing to the vibrant mathematical physics community at Groningen. His work continues to bridge abstract mathematical structures with fundamental physical theories, particularly through the lens of deformation quantization and Poisson geometry.
Petros Dellaportas holds dual appointments as a Professor of Statistical Science at University College London (UCL) and a Professor of Statistics at the Athens University of Economics and Business (AUEB). His research focuses on Bayesian statistics, machine learning, financial econometrics, and dynamic pricing. He leads projects on topics such as Poisson processes for cybersecurity, reservoir computing for macroeconomic forecasting, and probabilistic fault detection in wind parks. His recent publications emphasize advancements in Bayesian methods, variational autoencoders, and spatio-temporal point processes. Dellaportas has supervised over 20 PhD students, contributing to areas like stochastic volatility models and inverse reinforcement learning. He co-founded Thales and Friends, an organization bridging mathematics and cultural activities, and organizes the Greek Stochastics workshop series on topics ranging from causal learning to computational statistics. Key projects include anomaly detection in VAT networks and scalable Gaussian process models. His work often integrates statistical theory with applications in finance, sports analytics, and environmental science. Dellaportas maintains active collaborations with institutions globally, advancing interdisciplinary research and methodological innovations in statistical science.
Ron Reid-Edwards is an Associate Professor at the University of Cambridge, affiliated with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Faculty of Mathematics. He is also a Korner Fellow in Mathematics at Trinity Hall, where he has held a College Associate Professor position since 2017. Prior to his current role, he held academic positions at the University of Hull, University of Oxford, City University, London, and the University of Hamburg. Education: PhD in Mathematics from Queen Mary, University of London (2002-2006) His research lies at the intersection of String Theory , Quantum Field Theory , and Twistor Theory , motivated by the goal to understand Quantum Gravity . His work explores non-geometric backgrounds, doubled geometry, and generalized T-duality, with applications to compactification scenarios and gauge-gravity dualities. The 15 most recent publications highlight his focus on foundational questions in String Theory , including ambitwistor strings, lattice gerbes, and flux compactifications. These works often intersect with Supergravity , Conformal Field Theory , and D-brane physics, emphasizing geometric and algebraic structures. Scientific awards include: Korner Fellowship in Mathematics Ron contributes to postgraduate education as the Associate Director of Taught Postgraduate Education in the Faculty of Mathematics, teaching advanced courses such as Part III Advanced Quantum Field Theory and Part III String Theory.
