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Bin LiView profile

Associate Professor

• Quantum Optics
• Quantum Information Science
• Optical Engineering
• +3 more

Bin Li is an Associate Professor and Associate Chair of Actuarial Science in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Applied Mathematical and Computational Sciences (2013, University of Iowa), and master's and bachelor's degrees in Computational Mathematics from Xi’an Jiaotong University (2008 and 2005, respectively). His research focuses on quantum optics and optical engineering, particularly in developing robust laser-driven systems for quantum light sources, solid-state emitters, and trapped ion quantum computing. He has pioneered techniques like Notch-filtered Adiabatic Rapid Passage (NARP) and advanced femtosecond pulse engineering for high-performance quantum systems. His work bridges theoretical quantum physics with experimental optical innovations. Research interests include: Optical driving schemes for quantum emitters Adiabatic inversion and rapid passage methodologies Individual ion addressing in quantum simulators Spin dynamics in 2D perovskite materials Publications span 2018–2025, emphasizing advancements in quantum engineering, ultrafast optics, and atomic-scale systems. While no scientific awards are explicitly listed, his impactful contributions to quantum technologies suggest active recognition in the field. Advising and grants: No specific advisees or grants are documented in the provided texts, though his research activities imply involvement in graduate supervision and funding programs.

Ferit ÖztürkView profile

Professor

• Singularity Theory
• Contact Topology
• Symplectic Topology
• +1 more

Ferit Öztürk is a Professor in the Mathematics Department at Bogazici University, specializing in singularity theory, contact topology, and symplectic topology. He leads the Geometry/Topology Seminar and supervises numerous graduate students in these fields. His research bridges topological methods with applications in robotics and dynamical systems. Recent work includes exotic manifold construction, real contact structures on 3-manifolds, and Morse-Bott theory on Lie groups. Dr. Öztürk holds a Ph.D. from Middle East Technical University (2001) and has developed course materials on Morse Theory and Projective Geometry. Student Supervision: 12+ M.S./Ph.D. students in topology and geometry Research Tools: Contact branched covers, real algebraic models, geometric surgery

Emmanuel BreuillardView profile

Professor

• Combinatorics
• Geometric Group Theory
• Ergodic Theory
• +1 more

Emmanuel Breuillard is a Professor of Pure Mathematics at the University of Oxford. Previously, he held positions as Sadleirian Professor at the University of Cambridge (2017-2021), Professor at Université Paris-Sud (2008-2017), and Associate Hadamard Professor at École Polytechnique (2006-2008). His academic journey includes a PhD from Université Paris-Sud and Yale University, supervised by Frédéric Paulin and Gregory Margulis, and a Master in Mathematics from Cambridge. PhD: Université Paris-Sud (2004), supervised by Frédéric Paulin; Yale University, supervised by Gregory Margulis Master: University of Cambridge (2000) Ecole Normale Superieure (1997-1999) His research spans Combinatorics , Geometric Group Theory , Ergodic Theory , and Number Theory , focusing on approximate groups, expansion in finite simple groups, and Diophantine geometry. His recent work analyzes Elekes-Szabo problems in projective geometries. He has authored 50+ publications in prestigious journals, addressing topics like random polynomials, C*-simplicity, and uniform independence in linear groups. Fellow of the Royal Society (2024) ERC Consolidator Grant (2014-2019) Prix Charles-Louis de Freycinet (2013) European Mathematical Society Prize (2012) ERC Starting Grant (2008-2013) Cours Péccot, Collège de France (2006)

James V. BurkeView profile

Professor

• Convex Optimization
• Non-smooth Optimization
• Variational Analysis
• +7 more

James V. Burke is a Professor of Mathematics at the University of Washington with extensive contributions to optimization theory and its applications. His academic career spans several decades, during which he has developed fundamental theories in nonsmooth and convex optimization, variational analysis, and computational methods for complex optimization problems. Research Focus Burke's primary research centers on convex-composite optimization , where he has established critical theoretical foundations and practical algorithms. His work on weak sharp minima has become foundational in optimization theory, providing essential insights into solution stability and error bounds. He has made significant advances in gradient sampling algorithms for nonsmooth, nonconvex optimization problems, which have broad applications in engineering and data science. More recently, Burke has applied optimization techniques to state estimation problems , particularly developing robust Kalman smoothing methods using Student's t-distributions and other non-Gaussian models. His research bridges pure mathematical theory with practical computational methods, demonstrating consistent innovation across multiple subfields of optimization. Academic Contributions Burke has taught numerous graduate-level courses including Math 509 (Optimal Control), Math 554 (Linear Analysis), and specialized courses on convex analysis and optimization. His research collaborations span multiple institutions, with frequent co-authorship with leading optimization researchers such as Tim Hoheisel, Adrian Lewis, and Michael Overton. He regularly presents his work at major conferences including SIAM Optimization and ICCOPT, with his most recent presentation at the SIAM Conference on Optimization in Seattle (June 2023).

Laurence Perreault-LevasseurView profile

Associate Professor

• Cosmology
• Machine Learning
• Gravitational Lensing
• +5 more

Laurence Perreault-Levasseur is an Associate Professor at Université de Montréal and an Associate Member of Mila. She specializes in applying machine learning methods to cosmology, with affiliations at the Flatiron Institute and Perimeter Institute. Her research focuses on gravitational lensing, dark matter, and precision cosmology. She holds a Canada Research Chair in Computational Cosmology and Artificial Intelligence. Education: PhD (University of Cambridge, 2015), M.Sc. and B.Sc. (McGill University). Research Interests: Machine learning for cosmological inference, strong gravitational lensing, galaxy cluster characterization, and dark matter studies. Affiliations: CRAQ (Québec Astrophysics Research Centre), Mila (Quebec AI Institute). Her work includes developing Bayesian methods for inverse problems and neural networks for astrophysical data analysis. Key projects involve precision cosmology via machine learning and reconstructing early-universe conditions using generative models.

Patricia Alonso RuizView profile

Associate Professor

• Analysis
• Probability
• Fractal Geometry
• +3 more

Patricia Alonso Ruiz is an Associate Professor in the Department of Mathematics at Texas A&M University (on leave AY24/25). She specializes in analyzing random phenomena in fractal-like environments, focusing on connections between analysis, probability, and geometry. Her research involves Dirichlet forms, heat kernels, and stochastic processes on fractals such as the Sierpinski gasket and diamond fractals. Her academic journey includes leadership in the TAMU Fractals Research Team, supported by an NSF grant. She has organized conferences like the BIRS Workshop on New Trends in Stochastic Analysis and the Cornell Conference on Analysis, Probability, and Mathematical Physics on Fractals. Her outreach efforts include the Kenya Workshop and educational programs like the Summer Educational Enrichment in Math. Key research areas include spectral theory of fractals, BV functions on metric measure spaces, and functional inequalities in fractal settings. Collaborations with researchers like Fabrice Baudoin and Alexander Teplyaev have yielded impactful work on heat kernel analysis and Besov spaces. She mentors undergraduate teams exploring spectral features of fractals, fostering the next generation of mathematicians. Publications span topics from nonlocal energies to Orlicz-Sobolev embeddings, reflecting her interdisciplinary approach bridging pure and applied mathematics. Her work highlights the interplay between fractal geometry and stochastic analysis, advancing understanding of complex systems in porous and non-smooth environments.

Pierre PerronView profile

Professor

• Econometrics
• Time Series Analysis
• Structural Change Analysis
• +4 more

Pierre Perron is a Professor of Economics at Boston University, specializing in econometrics and time series analysis. He holds positions in the Department of Economics, with a focus on structural change, unit root testing, and applications in climate change and financial markets. He received his PhD from Yale University. Education: PhD in Economics, Yale University Research Interests: Development of econometric methods for time series with structural breaks and unit roots Applications to climate change, financial markets, and macroeconomic policy Advances in statistical inference for nonstationary processes Key Awards: Fellow of the Econometric Society Clarivate Citation Laureate (2020) Doctor Honoris Causa from Pontificia Universidad Católica del Perú (2023) Recipient of the Distinguished Author Award (2022) Advising & Grants: Supervised numerous PhD students in econometrics, with many contributing to theoretical and applied research globally. His work has been supported by grants and recognized through high-impact publications. Publications: Authored/co-authored over 150 articles and books, including two volumes on Time Series Econometrics (2019) and cutting-edge work on structural change and climate modeling.

Michael BronsteinView profile

Professor

• Geometric Deep Learning
• Graph Representation Learning
• Computer Vision
• +4 more

Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.

• Algebraic Geometry
• Topology
• Homotopy Theory
• +6 more

Frank Neumann is a researcher affiliated with the University of Pavia , specifically within the Algebra and Geometry research group. His work focuses on advanced topics in algebraic geometry, topology, and homotopy theory, particularly moduli stacks, principal bundles, and spectral sequences. Research Areas : Algebraic Geometry, Homotopy Theory, Moduli Stacks, Principal Bundles, Topology, Arithmetic Geometry. The titles of his recent publications highlight his contributions to cohomology theories, geometric structures, and their interactions with number theory and differential geometry. His work often bridges abstract algebraic geometry with topological and homotopical methods. Despite no explicit mention of awards or student supervision in the provided texts, his research outputs indicate sustained engagement with foundational mathematical problems. He is contactable via email at frank.neumann@unipv.it .

Douglas W. CarterView profile

Assistant Professor

• Experimental turbulent flows
• Particle-turbulence interaction
• Noise generation of separated flows
• +7 more

Douglas W. Carter is an Assistant Professor in the Department of Mechanical, Materials, and Aerospace Engineering at the Armour College of Engineering, Illinois Institute of Technology. He leads the Experimental Turbulent Flows Lab, focusing on advanced experimental techniques for fluid dynamics research. Education: Ph.D., University of Minnesota, 2019 M.S., University of Minnesota, 2017 B.S., University of New Hampshire, 2014 Research Interests: Dr. Carter investigates experimental turbulent flows, particle-turbulence interactions, and noise generation in separated flows. His expertise spans particle tracking velocimetry, hypersonics, compressible flows, and data-driven methods for fluid systems. Research emphasizes experimental validation of turbulence models and development of novel diagnostic tools like FLEET velocimetry. Publications: Recent work explores hypersonic flow diagnostics, pressure reconstruction in stalled airfoils, and turbulence cascade dynamics. Publications demonstrate consistent focus on experimental fluid mechanics, high-speed flow measurements, and low-order modeling for aerodynamic prediction. Laboratory: The Experimental Turbulent Flows Lab (Rettaliata Engineering Center) develops cutting-edge techniques for turbulent flow analysis, including multi-scale imaging and optical diagnostics for high-speed applications.

Panagiotis DimitrakopoulosView profile

Associate Professor

• Computational Fluid Dynamics
• Multiphase Flows
• Mesoscale Polymer Dynamics
• +3 more

Panagiotis (Panos) Dimitrakopoulos is an Associate Professor in the Department of Chemical & Biomolecular Engineering at the University of Maryland, College Park. His research focuses on computational fluid dynamics, multiphase flows, and biophysical systems. He specializes in modeling elastic capsules, microfluidic devices, and polymer dynamics using advanced numerical algorithms. His work intersects fluid mechanics, biomedical engineering, and materials science. He has developed spectral boundary element methods for interfacial dynamics and contributed to understanding capsule deformation in microfluidic environments. His studies address drug delivery systems, microfluidic sorting mechanisms, and hemodynamic forces in biological systems. Key applications include designing next-generation drug carriers and analyzing blood cell mechanics in microcirculation. Notable contributions include analyzing elastic capsule migration in converging micro-capillaries, multi-compartment micro-capsule fabrication, and non-Newtonian fluid dynamics in confined geometries. His research spans from theoretical fluid mechanics to biomedical applications, emphasizing computational modeling and experimental validation.

• Statistics
• High-Dimensional Data Analysis
• Statistical Genetics
• +7 more

Aaron J Molstad is an Assistant Professor in the Department of Statistics at the University of Minnesota – Twin Cities, within the College of Science and Engineering. His research lies at the intersection of statistical methodology and genomic data science, with a focus on developing rigorous and scalable methods for modern high-dimensional datasets. His research interests include high-dimensional statistics, covariance and precision matrix estimation, regression modeling with structured responses, variable selection, and integrative analysis of omics data. He develops methods tailored for compositional data, multivariate responses, and ancestry-specific genetic association studies, contributing to both theoretical statistics and public health applications. The recent publications and funded projects highlight a strong trend in developing objective, reliable, and heterogeneous-aware statistical frameworks for genomics and biomedicine. His work emphasizes methodological innovation with direct applicability to complex biological data, particularly in diverse populations and multi-omics integration. Awarded grants from the National Science Foundation and the National Institutes of Health demonstrate recognition of his research’s significance and impact. These include projects on inference from omics data, new regression models for categorical responses, and integrative genomics in African American populations. Objective and reliable methods for inference from modern omics data (NSF, 2024–2027) Collaborative Research: New Regression Models for Multiple Categorical Responses (NSF, 2024–2025) Integrative Genomics into Genetic Association Studies of Blood Pressure and Stroke in African Americans (NIH/Fred Hutchinson, 2023–2024) Dr. Molstad advises and collaborates on major genomic studies involving protein expression, blood pressure, stroke, and ancestry-specific effects. While specific PhD students are not listed, his role as Principal Investigator on multiple grants indicates mentorship of graduate researchers and postdoctoral scholars. He is also active in the broader statistical community, with publications in top-tier journals such as Biometrika , Biometrics , and Genome Biology .

• Crystal Plasticity
• Materials Science
• Computational Materials Engineering
• +5 more

Martin Diehl is a computational materials scientist affiliated with KU Leuven (Departments of Computer Science and Materials Engineering) and the Max-Planck-Institut für Eisenforschung GmbH in Germany. His work focuses on crystal plasticity simulations, computational materials engineering, and multi-physics modeling of metallic systems. Research interests include: Crystal plasticity finite element method (CPFEM) and spectral solvers Microstructure evolution and damage mechanics Machine learning applications in materials design Development of the DAMASK simulation toolkit Multi-phase steel alloys and heterogeneous deformation Integrated computational materials engineering (ICME) Key trends in his publications since 2021 highlight advancements in: Multi-physics DAMASK framework for coupled chemo-mechanical and thermal simulations AI-driven inverse design of steel microstructures Damage modeling in dual-phase steels Collaborative software development for materials science Experimental-simulation integration for stress-strain partitioning High-resolution spectral methods for finite strain analysis He actively collaborates with institutions like Harbin Institute of Technology, University of Oxford, and research groups across Europe and Asia.

Christopher HowlsView profile

Professor

• Asymptotic Analysis
• Semiclassical Analysis
• Applied Mathematics
• +3 more

Professor Christopher Howls is a Professor of Mathematics and Director of the University Doctoral College at the University of Southampton. He holds a First in Joint Honours Mathematics and Physics from the University of Bristol and a PhD in Mathematical Physics under Sir Michael Berry FRS. His career includes roles at the University of Manchester, Brunel, and Southampton, where he has served as Head of the Applied Mathematics group and Director of the Faculty Graduate School. His research focuses on asymptotic analysis and its applications to quantum mechanics, general relativity, and aeroacoustics. He has supervised numerous PhD students and postdocs across mathematics, physics, and engineering. His funding portfolio includes over £35m in grants. He chairs the British Applied Mathematics Colloquium and has held visiting positions globally. Key roles include editorial board memberships (e.g., Proceedings of the Royal Society A) and leadership in EPSRC initiatives like the Meet the Mathematicians outreach program. His awards include Fellowships from the Institute of Mathematics and the Higher Education Academy. He has authored over 70 publications and delivered over 70 invited talks worldwide.

Bruno OlshausenView profile

Professor

• Neuroscience
• Computational Neuroscience
• Theoretical Neuroscience
• +5 more

Bruno Olshausen is a Professor at the University of California, Berkeley, holding appointments in the Helen Wills Neuroscience Institute and the School of Optometry. He also directs the Redwood Center for Theoretical Neuroscience, focusing on mathematical and computational models of brain function. His research explores visual system processing, sparse coding, and neural mechanisms underlying perception. Olshausen earned his B.S. and M.S. in Electrical Engineering from Stanford University and a Ph.D. in Computation and Neural Systems from Caltech. Previously, he was on the faculty at UC Davis (1996–2005) before joining UC Berkeley. Education : Ph.D. in Computation and Neural Systems, California Institute of Technology, 1994 M.S. in Electrical Engineering, Stanford University, 1987 B.S. in Electrical Engineering, Stanford University, 1986 Research Interests : Olshausen's work bridges neuroscience and computer science, emphasizing the development of computational models for understanding visual processing, sparse coding, and neural representation. His lab explores topics such as optic flow analysis, hierarchical scene representation, and neuromorphic systems. Key themes include the study of neural circuits, probabilistic models of perception, and the application of these insights to AI and data compression. Grants & Labs : Director of the Redwood Center for Theoretical Neuroscience Recipient of grants in computational neuroscience and neuromorphic engineering Labs/Teams : His research group collaborates on projects involving neural network models, analog computing with emerging memory systems, and hyperdimensional computing architectures.