Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.
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.
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.
Beth Anne Bennett is a Senior Lecturer in the Department of Mechanical Engineering at Yale University. Her research focuses on computational methods for solving complex fluid dynamics and combustion problems, particularly involving adaptive grid refinement techniques for nonlinear PDEs. She holds a Ph.D. from Yale University, where her doctoral work centered on developing efficient numerical algorithms for multidimensional combustion phenomena. Her research interests include laminar combustion, fluid dynamics, heat transfer, and solidification processes. She has pioneered solution-adaptive gridding techniques like Local Rectangular Refinement (LRR) for both nonreacting and reacting flows, with applications to steady and unsteady multidimensional systems. Bennett has been recognized with the National Science Foundation ADVANCE Fellows Award (2002-2006). Her publications span computational studies of ethanol/dimethyl ether blending effects in flames, oxygen-enhanced methane flames, and axisymmetric coflow flames. She actively contributes to professional societies including The Combustion Institute, ASME, SIAM, ASEE, and SWE. Her work integrates computational innovation with experimental validation, addressing challenges in parallelization, sparse matrix treatments, and algorithm optimization for convection-diffusion problems. Bennett's research bridges fundamental numerical methods and applied combustion engineering, advancing both theoretical frameworks and practical applications in energy systems.
Xi Chen is an Associate Professor in the Department of Computer Science at Columbia University. Prior to this, he was a postdoctoral researcher at the Institute for Advanced Study (Princeton University) and the University of Southern California. He holds a B.S. in Physics/Maths from Tsinghua University (2003) and a Ph.D. in Computer Science from Tsinghua University (2007), advised by Professor Bo Zhang under the guidance of the Institute for Theoretical Computer Science led by Andrew Chi-Chih Yao. His research focuses on Algorithmic Game Theory, Economics, and Complexity Theory. His work is supported by an NSF CAREER award, a Sloan Research Fellowship, and Columbia University startup funds. He has received the EATCS Presburger Award and multiple best paper awards, including at FOCS 2006, ISAAC 2009, and CCC 2017. Xi Chen has taught courses such as Analysis of Algorithms , Lower Bounds in Theoretical Computer Science , and Introduction to Computational Complexity . He co-advises current PhD students Tim Randolph and Erik Waingarten, and has graduated students like Timothy Sun (Emory University) and Xiaorui Sun (University of Illinois at Chicago). He has served on program committees for conferences like WINE, SODA, and STOC. His research spans theoretical computer science, including property testing, graph isomorphism, and fixed-point computation. He is affiliated with Columbia's Theory Group and actively participates in the Theory Seminar organized by Alex Andoni. Xi Chen's research also extends to algorithmic economics, exploring mechanisms, pricing strategies, and market equilibria. His work on complexity theory includes contributions to counting problems and circuit complexity. He maintains a lab and collaborates with researchers in theoretical computer science and algorithmic game theory.
Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.
Arian Novruzi is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, Faculty of Science. His expertise lies in partial differential equations (PDEs), shape optimization, numerical analysis, and mathematical modeling. He holds an MSc from the University of Tirana and a PhD from the University of Nancy. His research integrates theoretical and applied mathematics, with a focus on fluid dynamics, biomedical applications, and engineering challenges. Education: MSc in Mathematics, University of Tirana PhD in Mathematics, University of Nancy Dr. Novruzi’s research interests include the analysis and numerical solutions of PDEs, optimization of geometric shapes for engineering systems, and modeling of complex physical phenomena such as blood flow and tumor radiation therapy. His work bridges pure mathematics with practical applications, addressing problems in fluid mechanics, materials science, and biomedical engineering. His recent publications highlight advancements in non-diffusive neural network methods for hyperbolic conservation laws, blood flow modeling using Navier-Stokes equations, and the optimization of convex domains for energy maximization. These studies emphasize both theoretical rigor and computational innovation. Awards: No scientific awards explicitly mentioned in the provided texts. Dr. Novruzi has supervised students such as Terence C. Ngouoko. His grants and collaborations are not detailed here, but his research has implications for energy-efficient engineering designs and medical treatments. He has authored a Springer textbook on PDEs, reflecting his commitment to educational resources in mathematical sciences.
Chen Xu is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds an M.A. from York University and a PhD from the University of British Columbia. His research focuses on sparse modeling, statistical learning, and big data processing, with an emphasis on both theoretical and computational advancements. Dr. Xu is affiliated with the Faculty of Science and contributes to editorial roles for journals such as the Journal of the American Statistical Association and Electronic Journal of Statistics. Education: M.A., York University PhD, University of British Columbia Research interests include feature selection, regularization methods, kernel methods, and high-dimensional regression. His work addresses computational challenges in big data, proposing efficient algorithms for tasks like singular value decomposition, clustering, and distributed feature screening. Recent publications highlight advancements in multiview PCA, low-tubal-rank tensor recovery, and model-free regression techniques. Publications span prestigious journals like the Journal of the American Statistical Association and IEEE Transactions series, focusing on statistical methodology, machine learning applications, and scalable computational solutions for complex data problems. Editorial Service: Associate Editor, Journal of American Statistical Association-T&M (2023–present) Associate Editor, Electronic Journal of Statistics (2023–present) Former Associate Editor, The Canadian Journal of Statistics (2019–2021) His research groups are Statistics and Biostatistics, and Data Science, Machine Learning, and Artificial Intelligence. He has no listed awards but maintains active editorial and academic collaborations in computational statistics and machine learning.
Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.
Cédric Soutil is a Researcher at the Conservatoire National des Arts et Métiers (CNAM) , affiliated with the CEDRIC Laboratory. His work spans combinatorial optimization , integer programming , quadratic programming , and algorithm design , with a focus on solving complex optimization problems in scheduling and graph theory. Recent publications highlight his expertise in non-separable and non-convex quadratic integer programming , knapsack problems , and online computation . His research trends emphasize mathematical reformulations , upper bound algorithms , and optimization models for real-world applications like horse race scheduling and hydrogen production . He has collaborated extensively with researchers such as A. Houdayer , D. Quadri , and P. Tolla , contributing to over two decades of academic output in operations research and combinatorial optimization .
Yao Li is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. in Mathematics from Georgia Institute of Technology (2012) and previously served as a Courant Instructor at New York University’s Courant Institute. His research focuses on applied probability, dynamical systems, numerical analysis, and their intersections with machine learning, neuroscience, and statistical mechanics. He has contributed to understanding stochastic dynamics, invariant measures, and the mathematical foundations of machine learning. His work bridges theoretical mathematics with interdisciplinary applications in biology, physics, and engineering. Education : Ph.D. in Mathematics, Georgia Institute of Technology, 2012 Courant Instructor, Courant Institute of Mathematical Sciences, New York University Research Interests : Dr. Li’s research spans applied probability, dynamical systems, and numerical analysis. Key areas include: Machine learning and data-driven computational methods Neuroscience modeling (spiking neural networks, cortical processing) Nonequilibrium statistical mechanics (thermodynamic laws, energy exchange models) Stochastic processes and their ergodic properties His work emphasizes rigorous mathematical analysis combined with numerical simulations, addressing challenges in high-dimensional stochastic systems. Teaching & Advising : He teaches advanced courses such as Numerical Analysis, Applied Math Project Seminar, and Statistics. While specific student advisees are not listed, his research group likely engages graduate students in interdisciplinary projects. His courses integrate theoretical concepts with real-world applications, reflecting his research focus on computational and applied methods. Labs/Teams : Dr. Li’s research is conducted within the Department of Mathematics and Statistics at UMass Amherst, collaborating with institutions like the Courant Institute and Georgia Tech. His work often involves interdisciplinary teams addressing problems at the intersection of mathematics, physics, and biology.
Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.
Marc Teboulle is a distinguished Professor holding The Eric and Sheila Samson Chair of Optimization in the School of Mathematical Sciences at Tel Aviv University. With a career spanning over three decades, he has established himself as a leading figure in optimization theory and applications. His work bridges theoretical foundations with practical implementations across multiple scientific domains. Professor Teboulle's research focuses on continuous optimization, with particular emphasis on convex optimization, complexity analysis of algorithms, Lagrangian and dual decomposition methods, variational inequalities, and nonconvex nonsmooth large-scale optimization. His work has significant applications in engineering science, machine learning, and finance, demonstrating the interdisciplinary impact of optimization techniques. He has developed novel frameworks for center-based clustering algorithms and contributed to the theoretical understanding of first-order methods beyond traditional Lipschitz gradient continuity assumptions. His publication record shows a clear evolution toward increasingly sophisticated optimization frameworks, with recent work focusing on nonconvex composite optimization, Lagrangian-based methods, and complexity analysis of gradient-based algorithms. The trend indicates growing interest in non-Euclidean geometries for optimization and applications to high-dimensional data problems, reflecting the evolving challenges in modern optimization. As an educator and mentor, Professor Teboulle has supervised numerous PhD and MSc students since 1990, including prominent researchers like Amir Beck, Ron Shefi, and Yoel Drori. His graduate courses include Convex Analysis and Optimization, Advanced Topics in Modern Optimization, Algorithms for Continuous Optimization, and Advanced Seminar in Continuous Optimization. He has been exceptionally active in the academic community, delivering invited lectures at major international conferences from 2003 through 2024 across Asia, Europe, and North America. His book 'Asymptotic Cones and Functions in Optimization and Variational Inequalities' (co-authored with A. Auslender) has become a standard reference in the field, while his edited volume 'Grouping Multidimensional Data: Recent Advances in Clustering' has influenced data science applications.
Zevi Miller is a Professor in the Department of Mathematics at Miami University, located in Oxford, Ohio. He holds a B.S. (Honors Mathematics) from the University of Michigan (1972) and a Ph.D. in Mathematics from the same institution (1979). His research focuses on Graph Theory, Combinatorics, Complexity Theory, and Theoretical Computer Science, with particular emphasis on graph embeddings, permutation arrays, and algorithm design. Professional History: Miller began as a Teaching Fellow at the University of Michigan (1972–1978), then joined Miami University as Assistant Professor (1978–1982), progressing to Associate Professor (1982–1988) and Full Professor (1988–present). He has held visiting roles, including Visiting Professor at the University of Texas–Dallas (1985) and Visiting Scholar at UC Berkeley (1986), where he participated in the Mathematical Sciences Research Institute’s complexity theory program. Research interests include graph theory applications, combinatorial optimization, and theoretical computer science challenges such as Steiner trees and hypercube embeddings. His work often intersects algorithm design and discrete mathematics, with contributions to bandwidth optimization, graph layouts, and permutation arrays. Publications span over four decades, including foundational papers on graph embeddings, Steiner trees, and algorithmic graph theory. His collaborative research with scholars like I.H. Sudborough and D. Pritikin highlights interdisciplinary approaches to complex computational problems.
Matthias Mnich is a Professor and Head of the Institute for Algorithms and Complexity at Hamburg University of Technology (TUHH), within the School of Electrical Engineering, Computer Science and Mathematics. He also serves as Deputy Dean International, reflecting his leadership in academic administration and international collaboration. He is a principal investigator at the Helmholtz Graduate School for the Structure of Matter, further emphasizing his interdisciplinary impact. His research lies at the intersection of theoretical computer science and practical algorithm design, focusing on parameterized algorithms , approximation algorithms , combinatorial optimization , scheduling , and algorithmic game theory . His work often bridges theoretical guarantees with real-world applications in energy systems, quantum computing, and logistics. The recent publications (2023–2025) highlight his sustained excellence in top-tier venues such as FOCS, ICALP, ESA, STACS, and journals like Mathematical Programming and ACM Transactions on Algorithms . These works explore foundational problems in vector bin packing , integer programming , graph algorithms , and kernelization , while also applying algorithmic techniques to microgrid energy optimization and quantum algorithm engineering . He is deeply embedded in the theoretical computer science community, having served on program committees of major conferences including: STACS 2023 ESA 2024 FOCS 2023 ICALP 2024 IJCAI 2019–2025 AAAI 2018 SWAT 2018 He has successfully supervised several PhD students to completion, including Matthias Kaul , Roland Vincze , and Alexander Göke , many of whom have taken postdoctoral positions at institutions like the University of Bonn and University of Augsburg. His current research projects include PATTERN (2025–2031) , Hamburg Quantum Computing (2024–2029) , and Kernelization for Big Data , indicating long-term funding and strategic research directions. He leads the Institute for Algorithms and Complexity (E-11) , fostering a research environment focused on high-impact algorithmic research.
