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Mark SchmidtView profile

Professor

• Machine Learning
• Artificial Intelligence
• Mathematical Optimization
• +6 more

Mark Schmidt is a Professor in the Department of Computer Science at the University of British Columbia, Faculty of Science. He holds the Canada Research Chair in Large-Scale Machine Learning and is a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute. His research spans multiple centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Data Science Institute, and the Machine Intelligence Learning Discovery (MILD) group. Dr. Schmidt's educational background includes a Ph.D. from UBC (2005-2010), an M.Sc. from the University of Alberta (2003-2005), and a B.Sc. from the University of Alberta (2000-2003). His academic career progressed from Postdoc positions at Simon Fraser University, Ecole Normale Superieure, and UBC to Assistant Professor (2014-2019), Associate Professor (2019-2024), and current Professor (2024-present) at UBC. His research focuses on machine learning optimization, with particular emphasis on improving numerical algorithms for large-scale machine learning applications. His work bridges theoretical optimization and practical applications across computer vision, natural language processing, and reinforcement learning. He has developed numerous optimization techniques including variants of stochastic gradient methods, coordinate descent algorithms, and natural gradient approaches. Analysis of his recent publications reveals a strong focus on optimization for over-parameterized models, particularly transformers and large language models. His work addresses critical challenges in step-size selection, convergence guarantees, and efficient implementation of optimization algorithms. His research has significant implications for training deep neural networks more effectively and understanding why certain optimization methods outperform others in practice. Among his notable awards are the Dorothy Killam Fellowship (2025), Arthur B. McDonald Fellowship (2024), Sloan Research Fellowship (2017), and multiple UBC teaching awards. He has also received the Lagrange Prize in Continuous Optimization and Best Paper Award at AISTATS 2021. Dr. Schmidt actively supervises numerous graduate students, with over 40 PhD and Master's students listed as current or alumni members of his research group. His laboratory maintains strong connections with industry partners, with many alumni securing positions at leading AI companies including Google, Amazon, Meta, and Microsoft.

Takuya OouraView profile

Assistant Professor

• Numerical Analysis
• Numerical Integration
• Algorithms
• +3 more

Takuya Ooura is an Assistant Professor at the Research Institute for Mathematical Sciences (RIMS) at Kyoto University, specializing in numerical analysis and mathematical software development. His work bridges theoretical mathematics with practical applications in scientific computing and software development. Dr. Ooura earned his educational credentials through a rigorous academic path: he graduated from Hokuriku High School in 1987; completed his undergraduate studies at Nagoya University's School of Science in 1992; earned his Master of Engineering from the Department of Applied Physics at the University of Tokyo in 1994; and completed his PhD (Engineering) from the same department in 1997. His academic journey continued with a Research Fellowship from the Japan Society for the Promotion of Science (1997) followed by a position as Research Associate at RIMS, Kyoto University (2000). Dr. Ooura's research focuses on numerical integration algorithms, particularly his groundbreaking double exponential formula for Fourier-type integrals, which has been incorporated into Mathematica's NIntegrate function. He has also developed a high-speed FFT library that's utilized in Google Chrome browser (visible in chrome://credits). His work on continuous Euler transformation for accelerating convergence of slowly decaying integrals represents significant innovation in numerical analysis. His research spans both theoretical development and practical implementation of mathematical algorithms with real-world applications. His publication record demonstrates consistent contributions to numerical analysis, with particular emphasis on quadrature methods, integral transforms, and high-precision computation. His work shows a clear progression from theoretical foundations to practical implementations, with several algorithms achieving widespread adoption in commercial and open-source software. Paper prize awarded by JSIAM (2000) for 'A continuous Euler transformation and its application to Fourier transforms of slowly decaying functions' Paper prize awarded by JSIAM (2001) for 'Improvement of the PI Calculation Algorithm and Implementation of Fast Multiple-Precision Computation' Paper prize awarded by JSIAM (2005) for 'An Improved Convergence Test for the Double Exponential Formula' Japan Society for Industrial and Applied Mathematics 4th Achievement Award (2014) for 'Pioneering and practical development of the double exponential numerical integration method' Dr. Ooura has developed several widely used mathematical software packages including the double exponential integral formula, Clenshaw-Curtis numerical integration rule, and a general-purpose FFT library. His FFT package is particularly notable for its speed and accuracy, with benchmark tests showing superior performance compared to other implementations. His software has been incorporated into major projects including Google Chrome and SETI@home, demonstrating the practical impact of his theoretical work. His future research directions include further development of numerical computation libraries and applying his methods to various computational problems.

• Optimization
• Complex Networks
• Statistical Inference
• +3 more

Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.

• Geometric Topology
• Geometric Group Theory
• Low-Dimensional Topology
• +6 more

Prof. Harry Hyungryul Baik is a Tenured Associate Professor at KAIST's Department of Mathematical Sciences since 2017. He holds a PhD from Cornell University (2014) and a B.S. from KAIST (2009), advised by William Thurston, John Hubbard, and Dylan Thurston. His research focuses on geometric topology, geometric group theory, and low-dimensional topology, with notable contributions to mapping class groups, Kleinian groups, and Teichmüller theory. Education: PhD in Mathematics (Cornell, 2014), B.S. in Mathematics (KAIST, 2009). Key research areas include asymptotic translation lengths, laminar groups, and circular orders of groups. He co-leads the KAIST-KIAS joint research group 2K-GATE as Director, emphasizing collaboration between topologists. Research highlights: Characterization of Fuchsian groups via laminations, unsmoothability of mapping class group actions on 1-manifolds, and exponential torsion growth in random 3-manifolds. His work bridges topology with dynamical systems and geometric group theory, often involving collaborations with institutions like KIAS and MPIM. Awards include the Sangsan Prize (2018), Young-KAST membership (2020–2023), and multiple grants from Samsung and POSCO. He advises 7 PhD students and has mentored 15+ alumni, many of whom hold postdoc positions globally. His lab actively hosts conferences like the KAIST Geometric Topology Fair. Labs/Teams: Director of 2K-GATE (KAIST-KIAS), core member of the KAIST Topology Research Group, collaborator with international networks including the Harvard-MIT-Princeton topology axis.

• Language Models
• Diffusion Models
• Theory of Deep Learning
• +6 more

Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.

• Nonparametric Statistics
• High Dimensional Data Analysis
• Statistical Methods in Finance
• +7 more

Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.

Alex BlumenthalView profile

Assistant Professor

• Dynamical Systems
• Smooth Ergodic Theory
• Chaos Theory
• +3 more

Alex Blumenthal is an Assistant Professor in the School of Mathematics at the Georgia Institute of Technology since Fall 2020. His academic background includes a Ph.D. from New York University (2016) with a dissertation titled 'Nonuniformly hyperbolic theory for Banach space mappings.' Prior to joining Georgia Tech, he held positions as an instructor at the University of Maryland, teaching courses in probability theory, linear algebra, and precalculus, and served as a recitation leader at New York University for courses in chaos theory, differential equations, and analysis. Blumenthal's research focuses on dynamical systems and ergodic theory, with specialization in: Chaotic behavior in deterministic and stochastic systems Smooth ergodic theory and SRB measures Lyapunov exponents in random dynamical systems Stochastic fluid mechanics and turbulence modeling Infinite-dimensional dynamical systems on Banach spaces Statistical properties of complex systems His work bridges abstract mathematical theory with physical applications like fluid dynamics and statistical mechanics. Analysis of his recent publications shows strong emphasis on stochastic dynamics, Lyapunov exponents, and fluid mechanical systems, with mathematical techniques drawn from ergodic theory, functional analysis, and probability theory. His publications frequently appear in top mathematical physics and dynamics journals. No scientific awards or honors are mentioned in the source materials. Similarly, no information is available regarding research grants, student advising, or laboratory affiliations.

Lawrence C. WashingtonView profile

Professor

• Number theory
• Cyclotomic fields
• Elliptic curves
• +5 more

Lawrence C. Washington is a Professor of Mathematics at the University of Maryland, College Park . His office is located in Mathematics Building 1105, and he can be reached at lcw@math.umd.edu . Teaching & Courses: In Spring 2023 he is teaching Cryptography 456 (TuTh 11:00–12:15) and co-organising the Algebra Seminar (MW 2–3). Office hours are held Tuesdays 1:30–2:30 and Thursdays 10:00–10:50. Research Interests: His work centres on number theory , with particular emphasis on cyclotomic fields , elliptic curves , cryptology , and Iwasawa theory . He has made extensive contributions to the study of p-adic L-functions , class groups , heuristics for class numbers , and the arithmetic of elliptic curves, often bridging deep theoretical questions with computational investigations. Textbooks & Scholarly Output: Washington is the author of several widely-used textbooks: Introduction to Cryptography with Coding Theory (3rd ed.) Introduction to Cyclotomic Fields Elliptic Curves: Number Theory and Cryptography An Introduction to Number Theory with Cryptography (2nd ed.) Elementary Number Theory Recent Publication Trends: Over the past five years his papers have focused on heuristics for Iwasawa invariants , anti-cyclotomic extensions , class groups of real cyclotomic fields , and analytic estimates for sums of prime powers . The work is characterised by a synthesis of algebraic, analytic, and computational techniques, frequently yielding explicit examples and numerical data that inform broader conjectures in algebraic number theory. Extracurricular Interests: Outside mathematics, Washington enjoys running and playing the bassoon , and he maintains a light-hearted page devoted to his favourite intersection in Chevy Chase, MD. Advising & Grants: While the provided text does not enumerate individual students or specific grants, his extensive publication record and long-standing professorship indicate ongoing supervision of graduate research and participation in funded projects in number theory and cryptography.

Ghaith HiaryView profile

Professor

• Computational Number Theory
• Analytic Number Theory
• Random Matrix Theory
• +3 more

Ghaith Hiary is a Professor and Vice Chair for Graduate Recruitment in the Department of Mathematics at The Ohio State University. He holds a PhD from the University of Minnesota (2008). His research focuses on computational and analytic number theory, with emphasis on the Riemann zeta function, L-functions, random matrix theory, and asymptotic analysis. Hiary's work bridges theoretical mathematics and high-performance computation. He develops efficient algorithms (e.g., amortized-complexity methods) to evaluate zeta functions at extreme heights (e.g., near t=10 28 ), implemented in C++/Mathematica. His research uses random matrix models to study zeta-function moments and employs techniques like van der Corput estimates and hybrid methods for explicit bounds. His publications demonstrate consistent focus on zeta-function computations, factorization algorithms, and arithmetic biases. Recent work (2022-2025) explores invariant measures, Lehman's method generalizations, and sign changes in random multiplicative functions, maintaining strong ties to analytic and probabilistic number theory. Hiary shares code and datasets via GitHub, including implementations of T 1/3 algorithms for zeta computations. He collaborates with institutions like the University of Waterloo and maintains numerical databases of zeta zeros.

Ben GreenView profile

Professor

• Additive Combinatorics
• Analytic Number Theory
• Harmonic Analysis
• +3 more

Ben Green is the Waynflete Professor of Pure Mathematics at the University of Oxford and a Fellow of Magdalen College. His work spans additive combinatorics, analytic number theory, harmonic analysis, ergodic theory, discrete geometry, and group theory, with a focus on interdisciplinary approaches. Research Interests: Additive combinatorics and its applications to primes Analytic number theory (prime distribution, L-functions) Harmonic analysis (Fourier methods, spectral theory) Ergodic theory and its combinatorial applications Discrete geometry (ordinary lines, convex structures) Group theory (approximate groups, expansion) Article Trends: His recent work emphasizes multiplicative functions, Ramsey-type problems in number theory, expansion in finite groups, and extremal set theory. Themes include prime gaps, arithmetic progressions, and interactions between analysis and algebra. Scientific Awards: Clay Research Award (2004) Ostrowski Prize (2005) Whitehead Prize (2005) Leverhulme Prize (2007) European Mathematical Society Prize (2008) Royal Society Fellow (2010) Sylvester Medal (2014) Senior Whitehead Prize (2019) Advising: Ben has supervised numerous D.Phil students across additive combinatorics, analytic number theory, and related fields. Past students hold postdoctoral and academic positions globally.

• Fractal geometry
• Dynamical systems
• Analysis
• +3 more

Prof Lars Olsen is a Professor in the School of Mathematics and Statistics at the University of St Andrews, based in the Mathematical Institute. His research focuses on fractal geometry, dynamical systems, and analysis, with particular emphasis on multifractal theory and metric number theory. He has supervised PhD student Luke Derry and has published extensively in peer-reviewed journals such as Monatshefte für Mathematik , Mathematische Zeitschrift , and Acta Mathematica Hungarica . His work explores intricate mathematical structures such as fractal dimensions, measure-theoretic properties of metric spaces, and applications to number theory. Recent studies include analyses of digit frequencies in infinite iterated function systems, exponential densities of integer subsets, and average distances in self-similar fractals. These contributions bridge pure mathematics with applications in understanding complex natural phenomena through geometric and analytic frameworks. Prof Olsen’s research portfolio demonstrates a strong focus on advanced mathematical analysis, with over 30 peer-reviewed publications since 2017 alone. His investigations into Hewitt-Stromberg measures and Gromov-Hausdorff-Prohoroff spaces exemplify his commitment to advancing foundational theories in fractal geometry and measure theory. Collaboration with international researchers has yielded multidisciplinary insights into topics like Lq-dimension analysis and topological dynamics of continuous functions.

George YinView profile

Professor

• Stochastic Optimization
• Stochastic Control
• Stochastic Systems Theory
• +4 more

George Yin is a Professor in the Department of Mathematics at the University of Connecticut (since 2020). Previously, he held the position of Distinguished Professor at Wayne State University (2017–2020) and has been a faculty member there since 1988. He earned his Ph.D. in Applied Mathematics from Brown University in 1987, along with M.S. degrees in Applied Mathematics and Electrical Engineering, and a B.S. in Mathematics from the University of Delaware (1983). His research focuses on stochastic optimization, control theory, stochastic systems, and numerical methods, with applications to biology, finance, and engineering. He has held editorial roles at journals such as SIAM Journal on Control and Optimization and has received prestigious awards including SIAM Fellow (2015), IEEE Fellow (2002), and IFAC Fellow (2014–2017). Key funding includes continuous NSF support since 1989, grants from the Air Force Office of Scientific Research, and others. His work spans theoretical advancements in stochastic systems and practical applications in energy systems, control engineering, and data science. He has advised numerous students and maintains active collaborations internationally. Labs/Teams: Goldenson Center for Actuarial Research, Quantitative Learning Center. Grants: NSF, AFOSR, ARO, NSA, and multiple institutional grants.

• Functional Analysis
• Complex Analysis
• Operator Theory
• +7 more

Olivia Constantin is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics. She has been actively publishing since 2008, with research contributions in functional analysis, complex analysis, and mathematical fluid dynamics. PhD in Mathematics (implied by academic position and research output) Her research primarily focuses on operator theory on function spaces, especially Fock and Bergman spaces, and the application of complex analysis to fluid flows. Key areas include Hankel operators, integral operators, embedding theorems, and the analysis of water waves and oceanic gyres. Her work combines deep analytical techniques with applications in fluid mechanics. Recent publications show a strong trend toward using complex-analytic methods to study irrotational flows, traveling water waves, and gyre dynamics, indicating an interdisciplinary approach bridging pure and applied mathematics. Her 2025 paper on velocity extrema in ocean gyre flows highlights ongoing contributions to geophysical fluid dynamics. She has led two research projects: Operatorenklassen (2012–2016) and Analysis of Operators on Spaces of Holomorphic Functions (2017–2019), demonstrating sustained research leadership. Active speaker at academic conferences, with 18 recorded scientific activities including talks from 2008 to 2023 Olivia Constantin collaborates with prominent mathematicians such as A. Aleman, J. A. Peláez, A.-M. Persson, and D. Kalaj. She has not received any explicitly mentioned scientific awards. There is no information about her advising students or managing labs or research teams.

• Mathematical Statistics
• Model Selection
• Robust Estimation
• +3 more

Yannick BARAUD is a Full Professor in Mathematics at the University of Luxembourg, leading the group 'Developing Contemporary Mathematical Statistics' within the Department of Mathematics. He holds the ERA-Chair 'SanDAL' in Mathematical Statistics and Data Science, funded by the European Commission. His research focuses on robust estimation, model selection, hypothesis testing, and nonparametric methods. He serves as the Study Programme Director for the Master in Data Science and has held academic positions at the University of Nice Sophia Antipolis and CNRS. His career includes roles as a researcher at École Normale Supérieure (Paris) and a lecturer at the same institution. He earned his PhD from Université Paris-Sud and studied at École Normale Supérieure de Cachan. His work emphasizes rigorous statistical methodologies, including rho-estimation and robust Bayes-like approaches. Notable contributions include advancements in density estimation under shape constraints and robust regression techniques. He has published extensively on topics such as loss functions, empirical processes, and statistical inference, with applications in epidemiology and data science. His leadership in the SanDAL initiative underscores his commitment to bridging mathematical statistics and practical data science challenges. Collaborations and grants further highlight his role in advancing interdisciplinary research.

David Alan GoldbergView profile

Associate Professor

• Applied Probability
• Operations Research
• Stochastic Processes
• +7 more

David Alan Goldberg is an Associate Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University, part of Cornell Engineering. He joined Cornell in 2017 and previously held the A. Russel Chandler III Associate Professorship at Georgia Tech’s Industrial and Systems Engineering department. Goldberg earned his Ph.D. in Operations Research from MIT (2011) and a B.S. in Computer Science from Columbia University (2006). Education: B.S. in Computer Science, Columbia University (2006) Ph.D. in Operations Research, MIT (2011) Research Interests: Goldberg’s work focuses on applied probability and stochastic processes, including optimal stopping, inventory and queueing models, combinatorial optimization, and robust optimization. He develops algorithms and insights for complex systems, addressing challenges like the curse of dimensionality. His research spans applications in data science, operations research, and stochastic modeling. Notable contributions include distributionally robust inventory control and high-dimensional decision-making frameworks. Awards and Honors: 2025 Community-Engaged Practice and Innovation Award (David M. Einhorn Center) 2023 Sunny Yau ’72 Teaching Award (Cornell) 2019 INFORMS Applied Probability Society Best Publication Award 2015 NSF CAREER Award Multiple INFORMS Nicholson Student Paper Competitions (First Place, 2019 & 2015) Teaching and Service: Goldberg leads Cornell ORIE’s undergraduate research program, connecting students to real-world applications of OR and data science. He teaches courses in probability modeling, stochastic models, and academic skills for PhD students. He chairs the INFORMS Applied Probability Society and serves on editorial boards for Operations Research and Stochastic Systems . At Cornell, he advises the Undergraduate ORIE Society and directs undergraduate studies in ORIE. Labs & Collaborations: Goldberg’s research integrates theoretical rigor with practical applications, often involving collaborations across disciplines. His work bridges operations research, statistics, and computer science to address modern challenges in inventory systems, queueing networks, and decision-making under uncertainty.