Brent Pym is an Associate Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on the intersection of differential, algebraic, and noncommutative geometry, with a particular emphasis on Poisson varieties and deformation quantization. He has held academic positions at the University of Edinburgh, University of Oxford, and was a Postdoctoral Fellow at McGill and the University of Toronto. Education: BScE in Engineering Physics, Queen's University (2007) MSc in Mathematics, University of Toronto (2008) PhD in Mathematics, University of Toronto (2013) Research Interests: Pym studies Poisson structures, their quantizations, and connections to mathematical physics. His work involves classical/derived algebraic geometry, D-modules, moduli spaces, the Stokes phenomenon, and multiple zeta values. Recent projects include holonomic Poisson manifolds, log symplectic structures, and software for symbolic calculations in deformation quantization. Awards: Lichnerowicz Prize (2018) Advising & Grants: Pym has openings for graduate students (admission 2026) and undergraduate projects (2026–27). He develops the Star Products software package for symbolic calculations in Poisson brackets and quantization. His work is supported by research collaborations and institutional grants. Labs & Teams: Pym collaborates with researchers in geometry and mathematical physics, contributing to projects in noncommutative algebra and geometric quantization. His software tools enhance symbolic computation in these fields.
Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.
Dr. Tarek Sayed is a Professor at the University of British Columbia's Department of Civil Engineering within the Faculty of Applied Science. He specializes in Transportation Engineering, focusing on road safety analysis, traffic operations, and Intelligent Transportation Systems (ITS). He serves as the Director of the Bureau of Intelligent Transportation Systems and Freight Security (BITSAFS-Engineering) and Editor of the Canadian Journal of Civil Engineering . His research addresses three core areas: improving road safety evaluation techniques, enhancing safety through traffic operations and highway design analysis, and advancing ITS technologies. Notable contributions include frameworks for safety-audits of infrastructure projects like the Sea to Sky Highway and methodologies for evaluating transit signal priority systems in Vancouver. He has authored/co-authored over 250 publications and supervised 60 graduate students. Key Roles: Professor, BITSAFS Director, Journal Editor Awards: Tier 1 Canada Research Chair, Wilbur Smith Award, Sandford Fleming Award, Prince Michael International Road Safety Award Consulting: Global projects in traffic safety and ITS for agencies like ICBC, FHWA, and Ashghal His research integrates Bayesian statistical methods, extreme value theory, and machine learning to model traffic conflicts, pedestrian behavior, and safety interventions. Current projects include real-time safety optimization using autonomous vehicle data and analyzing shared space interactions between cyclists and pedestrians. Dr. Sayed chairs national/international committees, including the U.S. Transportation Research Board’s safety data committee. His work bridges academia and practice, influencing policy and infrastructure investment decisions worldwide.
Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.
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.
Dr. Jianguo Wang is a Professor in the Department of Earth and Space Science Engineering at York University's Lassonde School of Engineering. He has been a faculty member since 2006 and is a founding member of the Lassonde School. With over 35 years of academic and industrial experience, he specializes in multisensor integration, GNSS technology, and precision engineering surveying. He holds a Dr.-Ing. in Geomatics Engineering from Universität der Bundeswehr München, Germany, alongside Bachelor’s and Master’s degrees from Wuhan Technical University of Surveying and Mapping (WTUSM). His research focuses on advanced data processing methodologies, including Kalman filtering, error analysis, and LiDAR systems. He has authored/co-authored over 60 publications, including textbooks like Error Theory and Foundation of Surveying Adjustment and Foundation of Geodesy . He is a Fellow of Engineers Canada and licensed as a Professional Engineer in Ontario. Education: Dr.-Ing., Geomatics Engineering, Universität der Bundeswehr München (Germany) M.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping B.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping Dr. Wang teaches courses such as Advanced Optimization and Applications , GNSS , and Global Geophysics and Geodesy . He leads the Earth Observation Laboratory (PSE 432), focusing on multisensor integration for navigation and positioning. His work explores innovative solutions for sensor calibration, data fusion, and geospatial applications. Grants & Labs: Active in lab-based research with collaborators like Baoxin Hu, his laboratory integrates GNSS, IMUs, LiDAR, and cameras for precision navigation. His recent work addresses challenges in sensor error calibration, LiDAR point cloud accuracy, and Kalman filter enhancements.
Henri Darmon is a Distinguished James McGill Professor in the Department of Mathematics and Statistics at McGill University, affiliated with the Centre Interuniversitaire en Calcul Mathématique Algébrique (CICMA) and the Centre de Recherches Mathématiques (CRM). He holds citizenships of Canada, France, and Switzerland. His research focuses on algebraic number theory, particularly elliptic curves, modular forms, and L-functions, with contributions to the Birch and Swinnerton-Dyer conjecture and Stark conjectures. Education: B.Sc. Mathematics & Computer Science, McGill University (1987) Ph.D. Mathematics, Harvard University (1991) Key Positions: Director of CICMA (1998–2024) Editorial roles at journals like Commentarii Mathematici Helvetici and Transactions of the AMS Organizer of major conferences including CNTA, ICM satellite events, and thematic programs at MSRI and CRM Research Interests: Stark-Heegner points and Euler systems p-adic L-functions and Iwasawa theory Arithmetic of modular curves and Shimura varieties His work bridges analytic and algebraic approaches to number theory, emphasizing computational and geometric methods.
Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.
K. Rajibul Islam is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC) and the Department of Physics and Astronomy. He holds a joint appointment with the Perimeter Institute for Theoretical Physics and co-founded Open Quantum Design and Lightflow Optics Inc. His research focuses on quantum information processing, quantum simulation, and trapped ion systems, with applications in quantum computing and entanglement studies. Education: Ph.D. in Physics (2012, University of Maryland), M.Sc. in Physics (2007, Tata Institute of Fundamental Research), B.Sc. in Physics (2005, Jadavpur University). Postdoctoral research at Harvard University (2012–2015) and MIT (2015–2016). Research Interests : Quantum simulation of spin models, quantum computing with trapped ions, entanglement measurement, frustrated spin systems, and quantum materials. His lab, QITI (Quantum Information with Trapped Ions), develops scalable quantum simulators and open-access quantum computers like 'QuantumIon.' Awards : Fellow of the American Physical Society (2024), VAIBHAV Fellowship (2024), Excellence in Teaching Award (2024), Early Researcher Award (2019), and Distinguished PhD Dissertation Award (2012–13). Teaching : Courses include PHYS 701 (Graduate Quantum Physics), PHYS 234 (Quantum Physics I), PHYS 393 (Physical Optics), and PHYS 256 (Geometrical and Physical Optics). He emphasizes outreach via initiatives like Bigyan.org.in , a Bengali-language science platform. Lab and Collaborations : Active in developing trapped-ion quantum hardware, including ion trap designs, optical addressing systems, and holographic control methods. Collaborates on quantum algorithms, machine learning for quantum systems, and experimental quantum thermodynamics.
Vasu Tewari is an Assistant Professor (CLTA) at the University of Toronto, working across both the Downtown Toronto (St. George) and Mississauga (UTM) campuses. Their office is located at HU1015 (215 Huron), and they can be reached at vasu.tewari@utoronto.ca. As a member of the Department of Mathematics within the Faculty of Arts and Science, Professor Tewari contributes to both teaching and research activities at the university. Professor Tewari's research focuses on advanced topics in algebraic combinatorics, with particular expertise in: Quasisymmetric functions and their geometric interpretations Schubert polynomial theory and related structures Representation theory of symmetric groups and related algebras Combinatorial aspects of algebraic geometry Enumerative combinatorics with connections to symmetric functions Algebraic structures arising from combinatorial objects Analysis of Professor Tewari's recent publications reveals a consistent focus on the interplay between combinatorial structures and algebraic frameworks. Their work often explores generalizations of classical symmetric function theory through the lens of quasisymmetric functions, providing new insights into Schubert calculus, permutation statistics, and geometric combinatorics. A notable trend in their research is the investigation of stability phenomena in combinatorial structures and the development of new algebraic tools for studying these phenomena. Professor Tewari has made significant contributions to understanding the geometry of combinatorial objects through algebraic methods, particularly in the areas of permutahedral varieties, zonotopal algebras, and quiver representations. Their work bridges pure mathematics with potential applications in theoretical physics and computer science.
Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.
Yakov Shlapentokh-Rothman is an Assistant Professor jointly appointed in the Department of Mathematics at the University of Toronto St. George and the Department of Mathematical and Computational Sciences at the University of Toronto Mississauga. His research focuses on the intersection of partial differential equations, general relativity, and geometric analysis, with particular emphasis on black hole physics and the Einstein field equations. Education: PhD: Massachusetts Institute of Technology (2015) BS with Honors: Stanford University (2010) Research Interests: Shlapentokh-Rothman's work explores fundamental problems in mathematical relativity, including black hole stability, singularity formation, wave propagation in curved spacetimes, and the asymptotic behavior of solutions to Einstein's equations. His research combines rigorous PDE analysis with deep geometric insights. Publications focus on: black hole dynamics, scattering theory in curved spacetimes, stability analysis of Kerr and Reissner-Nordström solutions, cosmic censorship conjectures, and self-similar solutions to Einstein's equations. Recent work examines the structure of naked singularities and decay properties of fields in black hole backgrounds. Awards and Recognition: Alfred P. Sloan Fellowship in Mathematics Advising and Grants: Currently advising PhD student: Avyay Venkat Viswanath Research supported by NSERC Discovery Grants (RGPIN-2021-02562, DGECR-2021-00093)
Ke Zhang is a Professor in the Department of Mathematics at the University of Toronto. His research focuses on dynamical systems, particularly Hamiltonian dynamics, weak KAM theory, Arnold diffusion, and smooth dynamics. He also works on thermodynamic formalism and variational methods in Hamiltonian systems. B.S. in Mathematics from Tsinghua University Ph.D. in Mathematics from Pennsylvania State University under Yakov Pesin Postdoctoral research at University of Maryland and Fields Institute Ke Zhang's research explores instability phenomena in Hamiltonian systems (e.g., Arnold diffusion), viscosity solutions of Hamilton-Jacobi equations in weak KAM theory, and normal form/averaging theory. He has also contributed to vector calculus education through interactive Jupyter notebooks and teaching real analysis courses. He teaches courses such as MAT334 (Complex Variables), MAT1000/457 (Real Analysis I), MAT1001/458 (Real Analysis II), and MAT1845 (Dynamical Systems). The recent trends in his work align with geometric and analytical methods in dynamical systems, with applications to physics and nonlinear modeling. Ke Zhang maintains affiliations with institutions including the University of Toronto, University of Maryland, and Fields Institute. His educational background and postdoctoral training reflect a strong foundation in mathematical analysis and dynamical systems.
Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.
Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).
