Paul Larson is a Professor of Mathematics at Miami University. His research focuses on set theory, topology, and model theory, with particular expertise in forcing axioms, descriptive set theory, and infinitary logic. He holds a Ph.D. in Mathematics from the University of California, Berkeley. His work bridges foundational mathematical logic with applications in topology and combinatorics. Key contributions include studies on canonical models under fragments of the Axiom of Choice, polar forcings, and cardinal characteristics. Larson has collaborated extensively with leading researchers such as Saharon Shelah and Jindřich Zapletal. His publications span prestigious journals like the Annals of Pure and Applied Logic and Transactions of the American Mathematical Society. Beyond research, he contributes to the academic community through editorial work and expository writings on historical developments in determinacy theory. Education: Ph.D., Mathematics, University of California, Berkeley Research interests emphasize foundational questions in set theory with applications to topology and model theory. His recent work explores advanced forcing techniques, square principles in Pmax extensions, and combinatorial properties of cardinal invariants. Publications reflect interdisciplinary engagement, including crystal structure prediction in high-pressure chemistry and operator theory in functional analysis. Despite an extensive publication record, no specific scientific awards are documented here. His advising and grant activities remain unspecified in the provided texts. Collaborations span international institutions, reflecting his role as a central figure in contemporary set theory research.
Professor Jasper van Wezel is a distinguished academic in the field of Condensed Matter Theory at the University of Amsterdam's Faculty of Science, where he serves as Professor in the Institute for Theoretical Physics (ITFA) within the Institute of Physics. With a career spanning over two decades, he has progressed from Assistant Professor (2014-2016) to Associate Professor (2016-2024) and currently holds the position of Professor since 2024. His academic journey began with a PhD in theoretical condensed matter physics from Leiden University in 2007, followed by prestigious fellowships at Argonne National Laboratory and Homerton College, Cambridge. PhD in theoretical condensed matter physics (cum laude), Leiden University, 2007 Master's diploma in theoretical condensed matter physics (cum laude), Leiden University, 2003 Dutch VWO Diploma (cum laude), Dalton Scholengemeenschap, Den Haag, 1997 US High School Diploma (cum laude), Sanford High School, Maine, USA, 1998 Professor van Wezel's research focuses on several interconnected areas within Condensed Matter Theory. His work explores competing instabilities in Charge Density Wave materials, including Superconductivity and Charge Order, Combined Charge and Orbital Order, and Transition-metal dichalcogenides. He has made significant contributions to Topology in Condensed Matter, particularly examining the Role of crystal symmetries and Topology in non-Hermitian systems. A major theme in his research involves investigating the Connections between Quantum and Classical behaviour, with special emphasis on Spontaneous Symmetry Breaking both in equilibrium (The role of the Thin Spectrum) and dynamically (Spontaneous loss of Unitarity). Analysis of Professor van Wezel's recent publications reveals a strong focus on quantum phenomena in condensed matter systems, with particular attention to topological aspects, symmetry breaking, and connections to fundamental physics concepts like black hole thermodynamics. His work often bridges theoretical concepts with potential experimental realizations, as evidenced by studies on electron patterns in materials like TaS2 and theoretical frameworks for understanding quantum phase transitions. Bristol Physics Teaching Award (2014) Students' Award for Outstanding Teaching (2014) Fellow of the Higher Education Academy (2014) Aneesur Rahman Fellowship at Argonne National Laboratory (2010-2012) Junior Research Fellowship at Homerton College, Cambridge (2007-2010) Physics 'Discovery of the year' by Leiden University Physics department (2005) 'Onderwijsprijs Natuurkunde' teaching award (2004/2005) Professor van Wezel has secured numerous research grants including an ENW-M grant (2023), an ENW-Groot project with Leiden University (2021), and a prestigious VIDI personal grant from NWO (2014). He has supervised over 50 students at various levels, including PhD candidates, MSc students, and BSc students, fostering the next generation of physicists. His leadership extends to organizing conferences, serving on PhD committees, and holding administrative roles such as chair of the educational committee for the Dutch Research School in Theoretical Physics. His research group at the University of Amsterdam's Institute for Theoretical Physics maintains active collaborations with institutions worldwide, including Leiden University, University of Cambridge, University of Bristol, and research centers in France, Germany, and Poland. The group's work combines analytical theoretical approaches with computational methods to tackle fundamental questions in quantum condensed matter physics.
Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University, specializing in applied mathematics, numerical analysis, and computational methods. His research focuses on numerical methods for partial differential equations, fluid mechanics, interface problems, and computer graphics. He holds a PhD from UCSB (2004) and has held academic positions at MIT and McGill since 2005. Currently, he serves on committees such as the Steering Committee of the Institut des Sciences Mathematiques and the CRM Applied Mathematics Lab. His educational background includes a PhD under Professors Xu-Dong Liu and Sanjoy Banerjee. Key research areas include level set methods, fluid-structure interaction, and invariant numerical methods. Notable works include the Correction Function Method for interface problems and the Characteristic Mapping Method for advection problems. Nave’s publications span topics like Poisson equations with discontinuous coefficients, fluid dynamics simulations, and high-order numerical schemes. He has advised numerous graduate and undergraduate students, contributing to their research in applied mathematics and computational science. His work bridges theoretical rigor and practical applications in engineering and physics. He teaches advanced courses such as Numerical Analysis I/II and Computational Methods in Applied Mathematics. His research group collaborates on projects involving fluid dynamics, elasticity, and geometric algorithms, with a focus on developing robust numerical tools for complex systems.
Leif Eriksson is a Professor at Chalmers University of Technology , specializing in Radar Remote Sensing within the Department of Space, Earth and Environment . His career at Chalmers began in 2004, and he was promoted to Professor in 2022 after serving as Group Leader (2012–2017) and Head of Faculty Assembly (2017–2020). His research focuses on developing advanced methods for environmental monitoring using radar data, particularly synthetic aperture radar (SAR) from satellites and aircraft. Leadership Roles: Group Leader (Radar Remote Sensing), Faculty Assembly Head Key Collaborations: Rymdstyrelsen, EU Horizon, VINNOVA, European Space Agency Research Interests : Dr. Eriksson’s work spans forest biomass estimation , sea ice dynamics , and ocean surface current/wind retrieval . He integrates SAR data with in situ observations and climate models to study: Forest degradation (clear cuts, storm damage) via multi-temporal SAR Sea ice concentration, drift patterns, and thickness in Arctic regions Wind vectors and surface currents using interferometric SAR techniques Applications for maritime navigation safety and polar shipping optimization Article Trends : His recent publications emphasize SAR’s role in transport infrastructure monitoring (e.g., Iron Ore Line degradation), pan-Arctic landfast ice stability , and multi-frequency SAR fusion for enhanced sea ice observations. Collaborative work with teams across Europe and the U.S. highlights interdisciplinary approaches to climate and marine research. Projects & Grants : Dr. Eriksson leads or contributes to projects such as: CAISA (2022–2024): Air-ice-sea data assimilation EONav (2016–2019): Copernicus data for maritime navigation SEDNA (2017–2020): Safe Arctic shipping Forest Biomass Monitoring (2017–2018): Spaceborne SAR applications His work is supported by Rymdstyrelsen, EU Horizon, and industry partners like Trafikverket. Labs & Teams : He is central to the Radar Remote Sensing Group at Chalmers, collaborating with institutions like Lund University and international bodies such as ESA. His research often involves satellite campaigns (e.g., TanDEM-X, Sentinel) and field studies in polar regions.
Prof. Dr. Frank Pollmann is a Full Professor (W3) at the Department of Physics PH-I, Technical University of Munich (TUM), leading the Chair of Theoretical Solid-State Physics since 2022. His research focuses on condensed matter theory and quantum information concepts , particularly in systems of correlated electrons and quantum many-body dynamics . PhD: Max Planck Institute for the Physics of Complex Systems / TU Ilmenau (2006) Postdoc: UC Berkeley (2008-2010) Group Leader: MPIPKS Dresden (2011-2016) Associate Professor: TUM (2017-2022) His work spans topological phases , frustrated spin systems , and non-equilibrium quantum dynamics , utilizing tensor network methods and quantum information theory to study phenomena like many-body localization and Hilbert space fragmentation . His publications demonstrate trends in quantum scar states , Kardar-Parisi-Zhang hydrodynamics , and quantum transport anomalies . Scientific Awards : ERC Consolidator Grant (2017) Walter Schottky Prize (2015) Otto-Hahn Medal (2007) He teaches courses including Advanced Methods in Quantum Many-Body Theory , Solid State Theory , and Topology in Condensed Matter , while leading the Pollmann Group under the TUM School of Natural Sciences.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Manuel Del Pino is Professor at the University of Bath's Department of Mathematical Sciences and Royal Society Professor specializing in nonlinear partial differential equations. His research focuses on singularity formation, geometric evolution equations, and asymptotic analysis in fluid dynamics and mathematical physics. His investigations encompass blow-up phenomena in heat equations, vortex dynamics in Euler flows, and minimal surface theory. Current projects examine infinite-time singularity formation in parabolic equations and asymptotic properties of vortex configurations. Del Pino has received the Royal Society Professorship and leads multiple grants including 'Asymptotic patterns in nonlinear evolution problems' (EPSRC). He maintains collaborations with researchers globally through projects on singularity formation in PDEs.
Laurent Mydlarski is a Professor in the Department of Mechanical Engineering at McGill University, affiliated with the Faculty of Engineering. His research focuses on experimental fluid mechanics, particularly turbulent flows and scalar mixing. He holds a Ph.D. from Cornell University and B.A.Sc. from the University of Waterloo. Research interests include turbulence statistics, scalar dispersion, differential diffusion, and industrial cooling applications such as hydroelectric generators and microelectronics. His work combines experimental methods like hot-wire anemometry, laser-induced fluorescence, and particle-tracking velocimetry. Key contributions include studies on multi-scalar mixing in jets, wall shear stress in turbulent flows, and thermal anemometry probe design. His Mydlarski Lab at McGill explores both fundamental fluid dynamics and practical engineering solutions. Recent publications (2023-2025) address multi-scalar mixing metrics, electronic cooling innovations, and drag reduction on porous cylinders. Collaborations with industry focus on applying fluid mechanics principles to real-world thermal management challenges.
John Kingston is a Professor of Linguistics and Director of the Phonetics Lab at the University of Massachusetts Amherst, where he has been since 1990. He holds a BA and MA from the University of Chicago (1976–1977) and a PhD from UC Berkeley (1985). His research focuses on the interplay between phonetics and phonology, particularly speech perception and its influence on phonological representations. He co-founded the Laboratory Phonology Conference series in 1987 and has conducted fieldwork on Otomanguean languages. His work emphasizes experimental methods to study phonological questions, including studies on vowel perception, tone systems, and cross-linguistic phonetic patterns. Kingston’s academic journey includes roles at the University of Texas, Austin (1984–1986) and Cornell University (1986–1990). His research explores how auditory processing and linguistic knowledge shape speech perception, with notable contributions to understanding tonogenesis, perceptual contrast effects, and vowel category learning in second languages. He collaborates on grants examining Ganong effects and phonological inventories, advocating for theories that bridge perceptual and structural aspects of language. His lab, the Phonetics Lab, supports experimental work on speech perception and production. Kingston is also the Honors Program Coordinator, mentoring students in linguistics and related fields. Despite no explicit awards listed, his extensive publications and conference leadership reflect his scholarly impact.
Changhuei Yang is the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering at California Institute of Technology, serving as Executive Officer for Electrical Engineering and Investigator at Heritage Medical Research Institute. He holds a Ph.D. and three master's degrees from MIT, with appointments at Caltech since 2003. Research focuses on: Advanced microscopy techniques including Fourier Ptychography Wavefront shaping for biological tissue imaging Optical phase conjugation for deep-tissue applications Compact medical devices for cerebral monitoring Publications demonstrate leadership in computational imaging, with recent advances in stain-free embryo analysis, portable cerebral blood flow monitors, and high-resolution volumetric imaging techniques using neural representations. Honored as National Academy of Inventors member. Research applications span deep-tissue biochemical imaging, incisionless surgery, and optogenetic activation systems.
James Bremer is a Professor in the Department of Mathematics and holds a cross-appointment in the Department of Computer and Mathematical Sciences at the University of Toronto's Scarborough campus. His research focuses on developing efficient numerical algorithms for solving elliptic boundary value problems, integral equations, and special function transforms.
Eric Grivel is a Professor at the University of Bordeaux affiliated with the IMS Bordeaux (Integration Laboratory from Materials to Systems). His research focuses on Signal and Image Processing Spectral Analysis Stochastic Process Modeling His work spans theoretical contributions to signal processing and practical applications in radar systems and biomedical signal analysis. Key trends in his recent publications include Optimization of Detrended Fluctuation Analysis (DFA) for Hurst exponent estimation Development of divergence metrics for comparing ARMA and Gaussian processes Waveform design in MIMO OFDM DFRC (Dual Function Radar-Communication) systems Integration of AI tools like ChatGPT in educational signal processing projects Collaborations and industrial partnerships evident in his publications involve institutions such as Indian Institute of Science Thales Airborne Systems STMicroelectronics CEA Leti Slb (Schlumberger)
Asaf Ferber is Associate Professor in Mathematics at University of California, Irvine, School of Physical Sciences. His research spans discrete mathematics including combinatorial games, random graphs, extremal hypergraph theory, and quantum computation. Research explores Hamiltonian cycles in random graphs, structural properties of pseudorandom graphs, and quantum algorithms for combinatorial problems. Recent work develops quantum approaches to graph learning and sparse recovery in random matrices. Awards: NSF CAREER Award Sloan Fellowship Distinguished Early Career Faculty Award for Research Air Force Research Grant NSF-BSF Grant Organizes conferences including SoCalDM Symposium and Desert Discrete Math Workshop, mentoring graduate students through UCI's Probability and Combinatorics Seminar.
Daniel A. Spielman is a Sterling Professor of Computer Science, Statistics & Data Science, and Mathematics at Yale University. He serves as the inaugural James A. Attwood Director of the Institute for Foundations of Data Science (FDS) and was previously co-Director of the Yale Institute for Network Science (YINS). He is also a member of TILOS, the NSF Institute for Learning-Enabled Optimization at Scale. His research interests span Spectral Graph Theory, Algebraic Graph Theory, Laplacian Matrices, Expander Graphs, and Random Walks on Graphs. Spielman has made fundamental contributions to understanding the mathematical foundations of data science, including work on smoothed analysis of algorithms, spectral sparsification, and solutions to the Kadison-Singer problem. Spielman's publications demonstrate a consistent focus on developing efficient algorithms with strong theoretical foundations. His work bridges pure mathematics, theoretical computer science, and practical applications in network analysis and machine learning. His research has evolved from foundational work on smoothed analysis to recent contributions in experimental design using discrepancy theory. His scientific honors include: Nevanlinna Prize for contributions to mathematical aspects of computer science Two Gödel Prizes (2008 and 2015) for outstanding papers in theoretical computer science MacArthur Fellowship ('Genius Grant') Simons Investigator award Membership in the National Academy of Sciences and American Academy of Arts and Sciences Spielman has advised numerous PhD students who have gone on to prominent positions in academia and industry. His teaching includes advanced courses in Spectral Graph Theory and Computation and Optimization. He has developed important software packages like Laplacians.jl for solving Laplacian linear equations and related problems.
Maarten de Hoop is the Simons Chair and Professor of Computational and Applied Mathematics at Rice University, part of the George R. Brown School of Engineering. He holds visiting roles at MIT and the Chinese Academy of Sciences. His research spans seismic wave analysis, inverse problems, deep learning, and planetary seismology. He earned his Ph.D. in Technical Sciences from Delft University of Technology (1992), and earlier degrees from Utrecht University. Notable awards include the 1996 J. Clarence Karcher Award and 2001 Fellowship from the Institute of Physics. His work integrates computational mathematics with geophysics, focusing on extracting signal information from large datasets, developing novel inverse scattering methods, and applying deep learning to geoscience challenges. Recent studies include transformer models for in-context learning, semialgebraic neural networks, and seismic waveform foundation models like SeisLM. He leads the Geo-Mathematical Imaging Group, fostering interdisciplinary projects in planetary missions and data-driven discovery.
