Shirshendu Ganguly is an Associate Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on probability theory, statistical physics, and their applications, including percolation models, phase transitions, Markov chains, and random graphs. He holds a PhD in Mathematics from the University of Washington and has held postdoctoral positions at UC Berkeley. Ganguly has been recognized with the 2019 Sloan Research Fellowship. Education: PhD in Mathematics, University of Washington, 2011–2016 Miller Postdoctoral Fellow, UC Berkeley, 2016–2018 Research Interests: Probability Theory, Statistical Mechanics, Markov Chains, Random Graphs, Percolation Theory, Sparse Combinatorial Structures His work explores geometric and probabilistic phenomena in disordered systems, including polymer models, self-organized criticality, and random matrix theory. He has advised multiple PhD students and contributes to teaching advanced probability courses at Berkeley. Awards: 2019 Sloan Research Fellowship
Reed Maxwell is the William and Edna Macaleer Professor of Engineering and Applied Science in the Department of Civil and Environmental Engineering and the High Meadows Environmental Institute at Princeton University. He serves as Director of the Integrated Groundwater Modeling Center (IGWMC) and leads a research group comprising graduate students, postdoctoral researchers, and staff. His academic appointments include concurrent roles in both the School of Engineering and Applied Science and the High Meadows Environmental Institute. Maxwell's research focuses on understanding connections within the hydrologic cycle and how they relate to water quantity and quality under anthropogenic stresses. His work centers on hard problems in hydrology including groundwater, evapotranspiration and snow. His research group uses integrated hydrologic modeling, field observations, and remote sensing products to study terrestrial freshwater systems. Key research areas include surface water and the terrestrial hydrologic cycle; interactions of the land-surface, surface water and groundwater; and human health risk assessment. Maxwell has authored more than 185 peer-reviewed journal articles with an H-Index of 66 and over 19,000 citations. His recent work emphasizes machine learning applications in hydrology, continental-scale modeling, and physically rigorous scenario generation through projects like HydroFrame and HydroGEN. He teaches courses including CEE 306/ENV 318 Hydrology: Water and Climate and CEE 586/ENV 586 Physical Hydrology. 2020 Distinguished Henry Darcy Lecturer American Geophysical Union Fellow (2019) 2018 Boussinesq Lecturer Belle van Zuylen Chair (visiting), University of Utrecht 2017 School of Mines Research Award recipient Maxwell has mentored 17 PhD students and 20 MS thesis students throughout his career. His current research group includes multiple postdocs, research software engineers, and graduate students working on projects spanning continental-scale hydrologic modeling, groundwater-stream interactions, and machine learning applications in hydrology. The IGWMC maintains an active education and outreach program including STEM fairs, school visits, and digital educational tools like the HydroFrame Education Team's virtual sandtank aquifer model.
Andre Wibisono serves as Assistant Professor in Yale University's Department of Computer Science with a secondary appointment in Statistics & Data Science, joining the faculty in 2021 after postdoctoral research at University of Wisconsin-Madison and Georgia Institute of Technology. His educational background includes: Ph.D. in Computer Science, UC Berkeley M.A. in Statistics, UC Berkeley M.Eng. in Computer Science, MIT S.B. in Mathematics and Computer Science, MIT Wibisono's research focuses on algorithm design for machine learning through optimization, sampling, and game theory , leveraging dynamical systems and information theory to develop accelerated discrete-time algorithms from continuous dynamics. His work provides theoretical foundations for efficient machine learning systems with applications in generative modeling and constrained optimization. Recent publications (2023-2025) demonstrate consistent innovation in Hamiltonian-based optimization , constrained-space sampling , and min-max game convergence , characterized by rigorous mathematical analysis connecting continuous dynamics to discrete algorithms. Key trends include randomized integration for acceleration, phi-divergence convergence guarantees, and symplectic geometry applications to mirror descent. Scientific recognition includes: NSF CAREER Award for developing algorithmic frameworks bridging continuous and discrete dynamics He actively mentors current students (Siddharth Mitra, Kaylee Yang, Jane Lee, Qiang Fu, Peter Wang) and has guided two postdocs to faculty positions. Research is funded through the NSF CAREER award and collaborative CIF grants focused on Hamiltonian dynamics for sampling and optimization. His Yale research group develops theoretical foundations for next-generation machine learning algorithms, emphasizing mathematical rigor in optimization and sampling with applications to generative modeling and constrained inference problems.
Peter Aronow is a Professor at Yale School of Public Health , with appointments in the Department of Statistics and Data Science , Economics Department , and the Institute for Social and Policy Studies . His interdisciplinary work bridges political science, biostatistics, and epidemiology. Professor of Public Health (Biostatistics) Secondary appointments in Political Science and Economics Associate Professor in the Institute for Social and Policy Studies Dr. Aronow specializes in causal inference and statistical methodology, particularly in non-traditional field research contexts. His research encompasses: Design-based approaches to causal inference Complex experimental designs Social network analysis Survey methodology with incomplete data His recent publications focus on spatial experiments under unknown interference, bias correction in RCTs, and temporal validity challenges. While no formal awards are listed, his work is cited across disciplines including: Political Analysis Econometrics Biostatistical Modeling Observational Study Design
Professor Ping Luo is an Associate Professor and Assistant Director (Outreach and Advancement) at the School of Computing and Data Science, University of Hong Kong. He also serves as Associate Director (Innovation and Outreach) of the Musketers Foundation Institute of Data Science. His research focuses on developing advanced machine learning algorithms, particularly in computer vision and deep learning, emphasizing reinforcement learning, meta-learning, and foundational algorithm understanding. Luo holds a PhD from the Chinese University of Hong Kong (2014), supervised by Prof. Xiaoou Tang and Prof. Xiaogang Wang. His notable achievements include over 70 peer-reviewed publications in top venues like TPAMI, IJCV, ICML, and CVPR, alongside competition wins such as the 2014 ImageNet ILSVRC Challenge and the 2017 YouTube-8M Video Classification Challenge. His work spans applications in autonomous driving, video segmentation, and facial recognition. Education: PhD in Information Engineering (2014), Chinese University of Hong Kong Awards: 2011 HK PhD Fellow Award, 2013 Microsoft Research Fellow Award Professional Roles: Former Research Director at SenseTime Research Recruiting Postdocs, PhDs, and RAs His research interests include algorithm development for autonomous systems, deep learning foundations, and practical AI applications in computer vision. He maintains an active presence in academic outreach and industry collaboration.
Jean-François Le Gall is a full Professor at Université Paris-Saclay and a member of the Orsay Mathematics Laboratory (LMO) since 2006. He has held prominent positions at Pierre and Marie Curie University (1988-2006) and École Normale Supérieure (1997-2007). A Senior Member of the University Institute of France (2007-2017) and an elected member of the Academy of Sciences since 2013, he served as Vice-President of Research for the Mathematics Department at Orsay (2020–present) and led the ERC Advanced Grant GeoBrown (2017–2023). Education: Ecole Normale Supérieure (1978–1982), PhD in stochastic differential equations (1982), State Doctorate on Brownian motion (1987) Research Interests focus on probability theory , particularly Brownian motion , superprocesses , random trees , planar maps , and their connections to PDEs and geometric models. His work bridges stochastic analysis , branching processes , and coalescence phenomena . Selected Publications include foundational studies on the Brownian map , random geometry , and spatial branching processes . His 2025 paper on The area of spheres in the Brownian plane explores fractal properties of random metric spaces, while the 2020 Growth-fragmentation processes work links Brownian trees to fragmentation models. Scientific Distinctions : 1986 Rollo Davidson Prize 1997 Loève Prize in Probability 2005 Sophie Germain and Fermat Prizes 2019 Wolf Prize in Mathematics 2022 BBVA Frontiers of Knowledge Award Academic Leadership includes directing the Probability and Statistics Team (2013–2019) and the Master 2 in Probability and Statistics (2007–2015). He chairs editorial roles in Grundlehren der mathematischen Wissenschaften (since 2020) and Probability Theory and Related Fields (2005–2010).
Kenneth J. Falconer is the Regius Professor of Mathematics at the University of St Andrews, where he is a member of the School of Mathematics and Statistics and the Analysis Research Group. He has held prestigious positions at the University of Bristol and Corpus Christi College, Cambridge, and has been a visiting professor at institutions including Oregon State University and the Australian National University. Regius Professor of Mathematics, University of St Andrews (2017–present) Professor of Mathematics, University of St Andrews (1993–2017) Reader, University of Bristol Lecturer, University of Bristol Research Fellow, Corpus Christi College, Cambridge His research centers on fractal and multifractal geometry, geometric measure theory, and related fields. He has made seminal contributions to the understanding of fractal projections, dimensional analysis of self-affine sets, and fractal processes. His work includes the concept of the digital sundial and the introduction of the affinity dimension and Falconer’s distance problem. His research spans dimensional analysis, random fractals, PDEs on fractal domains, and combinatorial geometry. The most recent publications show a sustained focus on intermediate dimensions, projections of fractal sets and measures, and the dimensional properties of stochastic processes. His work frequently involves collaboration with leading mathematicians and appears in top journals such as Transactions of the American Mathematical Society , Ergodic Theory and Dynamical Systems , and Journal of Fractal Geometry . Fellow of the Royal Society of Edinburgh (1998) Shephard Prize, London Mathematical Society (2020) CBE, King’s New Year’s Honours (2024) Kenneth Falconer has supervised numerous students and collaborated with many researchers including Jonathan Fraser, Pertti Mattila, and Xiong Jin. He has served on editorial boards for Fractals , Journal of Fractal Geometry , and Mathematical Proceedings of the Cambridge Philosophical Society . He has been active in professional service, including as Chair of the British Mathematical Colloquium 2018 and Publications Secretary of the London Mathematical Society (2006–2009). He organized major programs at the Isaac Newton Institute and Mittag-Leffler Institute. He is also known for his involvement in the Long Distance Walkers Association, where he served as Chairman and Editor of Strider , and for his mathematical poetry featured in publications like the London Mathematical Society Newsletter .
Prof. Baker Mohammad serves as Professor and Director of the System on Chip Lab in the Department of Computer and Information Engineering at Khalifa University. With over 15 years of industrial experience at Intel and Qualcomm designing microprocessors and DSP chips, he bridges academic research with real-world engineering challenges in high-performance computing and low-power systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin (2008) M.S. in Electrical and Computer Engineering, Arizona State University B.S. in Electrical Engineering, University of New Mexico Dr. Mohammad's research spans cutting-edge domains where VLSI design converges with AI acceleration and emerging memory technologies . His work pioneers Memristor applications in environmental sensing (radiation, vacuum, glucose) and neuromorphic computing, while advancing energy harvesting systems for wearable electronics. The integration of in-memory computing with security primitives represents a paradigm shift in hardware design, moving beyond traditional CMOS limitations. His publication trajectory reveals accelerating focus on self-powered neuromorphic systems and RRAM-based architectures, with recent work (2021-2023) emphasizing hardware-software co-design for edge AI. Over 75% of his recent publications involve cross-disciplinary collaborations spanning materials science, chemistry, and biomedical engineering. Notable scientific recognition includes: IEEE TVLSI Best Paper Award 2016 IEEE MWSCAS Myrill B. Reed Best Paper Award Qualcomm Qstar Award for Performance Leadership KUSTAR IP Excellence Award Multiple SRC Techon Best Session Papers As a dedicated mentor, he has supervised over 15 graduate students while securing competitive funding from Khalifa University, ADEK, Qualcomm, Tii, and UAE space agencies. His grant portfolio demonstrates exceptional translational impact, converting fundamental research in memristive devices into drone flight computers and medical sensors. Current projects integrate academic rigor with industrial deployment timelines. The System on Chip Lab operates as a multidisciplinary hub where semiconductor physicists collaborate with AI researchers to develop RISC-V-based secure processors and piezoelectric nanogenerator systems. Recent expansions include partnerships with Tii for aerospace applications and medical device startups for glucose monitoring technology.
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Marco Castronovo serves as an Assistant Professor in the Mathematics Department at Columbia University, with his office located in Mathematics Hall 614. His academic work bridges continuous and discrete mathematical structures through the lens of symplectic geometry and topology. His research focuses on Symplectic Topology , particularly exploring symplectic structures as frameworks for quantization of classical invariants. Key interests include: Developing open-string versions of Schubert calculus Investigating cluster structures in positroid varieties Constructing Landau-Ginzburg models for Grassmannians Studying Lagrangian cobordisms and exotic tori Analyzing connections between Dubrovin spectra and Fukaya algebras His recent publications reveal a consistent trajectory toward unifying symplectic geometry with combinatorial algebraic structures, particularly through Grassmannian varieties and their mirror symmetric counterparts. The work demonstrates increasing sophistication in connecting Fukaya categories with cluster algebraic frameworks, while maintaining strong ties to quantum topological invariants. As an academic mentor, Castronovo supervises undergraduate researchers including B. Basson (Barnard Summer Research Institute) and S. Kesavan (Columbia Summer Research Fellowship). He actively contributes to the mathematical community through refereeing and co-organizing the Columbia SGGTC Seminar, demonstrating commitment to both research dissemination and academic service. His computational work manifests through three significant open-source projects: Posetroids for exploring Zariski closure orders in Grassmannians, DubrovinDynamics for visualizing spectral evolution in truncated Dubrovin operators, and ClusterExplorer for conducting random walks on cluster structures of Grassmannians. These tools have become valuable resources for researchers working at the intersection of symplectic geometry and combinatorics.
Gustavo J. Bobonis is a Professor in the Department of Economics at the University of Toronto, with affiliations at the Munk School of Global Affairs and Public Policy. He holds a Ph.D. from the University of California, Berkeley (2005) and a B.A. from the University of Puerto Rico at Rio Piedras (2000). He co-directs the Forward Society Lab and is actively involved in research on development, labor, political economy, and economic history. Research Interests: His work focuses on development economics, particularly the impact of public policies on poverty, violence, education, and governance. He employs rigorous empirical methods, including field experiments and quasi-experimental designs, to analyze issues such as intimate partner violence, corruption, clientelism, and human capital accumulation. His research is geographically concentrated in Latin America and Puerto Rico. Recent Research Trends: His recent publications and working papers (2022–2025) demonstrate a strong focus on institutional reform, including anti-corruption audits, domestic violence courts, and education management. He also investigates long-term social impacts of welfare programs and climate adaptation strategies. His interdisciplinary approach bridges economics, public policy, and social science. Scientific Awards: U of T Department of Economics Faculty Award for Excellence in Undergraduate Teaching, 2015 John C. Polanyi Prize in Economic Science, 2009 National Academy of Education / Spencer Foundation Postdoctoral Fellow, 2008 Advising and Grants: While specific student names are not listed, he advises graduate students through the Honours Essay and research workshops. He leads collaborative research projects funded through grants and affiliations with J-PAL and BREAD, often involving large interdisciplinary teams. His work includes randomized evaluations and long-term follow-ups, suggesting sustained funding and research support. Labs and Teams: He co-directs the Forward Society Lab, which likely supports policy-relevant research on social development. He frequently collaborates with economists such as Paul Gertler, Marco Gonzalez-Navarro, Simeon Nichter, and Luis R. Cámara Fuertes. His affiliations with J-PAL and BREAD indicate integration into major global research networks focused on development and poverty alleviation.
Gil Kalai is a Professor of Mathematics at the Hebrew University of Jerusalem since 1992, where he holds the Henry and Manya Noskwith Chair. He also serves as an Adjunct Professor of Mathematics and Computer Science at Yale University since 2004 in a long-term part-time visiting position. His academic career includes visiting positions at prestigious institutions including MIT, Cornell, IAS Princeton, Berkeley, Bell-labs, IBM, and Microsoft. Professor Kalai's research spans multiple areas within mathematics and theoretical computer science. His work in combinatorics encompasses geometric, probabilistic, and topological approaches. He has made significant contributions to the study of convex sets and polytopes, linear programming, and theoretical computer science. His influential 1988 paper with Kahn and Linial on Boolean functions pioneered applications of Fourier analysis in theoretical computer science. Kalai's research has evolved to include the application of Fourier analysis to thresholds, influences, symmetries, noise, percolation, and social choice. He has developed theories in algebraic shifting and studied face-numbers and other combinatorial invariants of polytopes. His work on the diameter of polytopes and randomized simplex algorithms has been influential in optimization theory. In 1993, his collaboration with Kahn produced a groundbreaking counterexample to Borsuk's Conjecture in 1325 dimensions. Professor Kalai's publications reveal a consistent focus on the intersection of combinatorics, geometry, and theoretical computer science. His work shows a progression from foundational combinatorial geometry to increasingly sophisticated applications of harmonic analysis in discrete mathematics. The recurring themes across his 30+ year career include Boolean functions, polytope theory, and probabilistic methods in combinatorics, demonstrating remarkable coherence in his research trajectory. 2016 European congress of Mathematics, plenary speaker 2013 ERC advanced grant 2012 Rothschild Prize 1994 International Congress of Mathematicians invited section talk, Zurich 1994 Fulkerson Prize 1993 Erdos Prize 1992 Polya Prize Though specific details of his advising are not provided in the source material, Kalai has written over 70 scientific papers and maintains an active research blog entitled "Combinatorics and More." His 2013 ERC advanced grant indicates significant research funding for his work. His extensive collaborations with researchers across multiple institutions suggest a robust research program with numerous PhD students and postdoctoral researchers, though specific names are not mentioned in the provided texts. Professor Kalai maintains active research connections across multiple institutions including Hebrew University, Yale, and various research centers worldwide. His work bridges pure mathematics and theoretical computer science, creating a unique interdisciplinary research environment that influences both fields.
David Jerison is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), where he conducts research in Fourier analysis and partial differential equations. His work focuses primarily on free boundary problems and, more recently, on internal Diffusion Limited Aggregation (internal DLA), a stochastic growth model. He maintains an active research program with numerous publications in leading mathematical journals. Professor Jerison's research spans several interconnected areas of mathematical analysis. His primary interests include Fourier analysis and partial differential equations, with particular emphasis on free boundary problems. In recent years, he has expanded his research to include internal Diffusion Limited Aggregation, a stochastic growth model that has connections to probability theory and mathematical physics. His work often bridges geometric analysis, spectral theory, and probabilistic methods, demonstrating the deep connections between different branches of mathematics. Analysis of Professor Jerison's recent publications reveals a consistent focus on geometric aspects of partial differential equations, particularly free boundary problems. His research shows progression from classical PDE theory toward more stochastic and probabilistic approaches, as evidenced by his work on internal DLA. The publications demonstrate interdisciplinary connections between mathematical analysis, probability theory, and mathematical physics, with applications ranging from geometric measure theory to quantum mechanics. Professor Jerison is actively involved in teaching and mentoring at MIT. He has taught courses including Differential Equations (18.03), Fourier Analysis and Applications (18.103), and Differential Analysis (18.155). He also directs the Summer Program for Undergraduate Research (SPUR), which is exclusively for MIT undergraduates, and organizes the mathematics section of the Research Science Institute (RSI) for high school students. His teaching materials are available through MIT's Open Courseware platform, indicating his commitment to educational outreach and accessibility.
Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
Jon Keating is a Professor at the University of Oxford affiliated with the Mathematical Institute . His research spans Mathematical Physics , Number Theory , and Stochastic Analysis , with a focus on Random Matrix Theory and its applications to quantum systems and number theory. Research Trends: His recent work explores connections between random matrices and number-theoretic functions, with contributions to understanding moments of L-functions, characteristic polynomials, and quantum chaos. Key themes include asymptotic analysis, recursive structures, and interdisciplinary applications in nonlinear systems. Publications: Highlights include studies on CUE characteristic polynomials, the Ratios Conjecture, and collaborations in Nonlinearity , International Mathematics Research Notices , and Transactions of the American Mathematical Society .
