Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
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.
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.
Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.
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.
Leo Goldmakher is an Associate Professor of Mathematics at Williams College, where he teaches courses in cryptography, topology, Fourier analysis, measure theory, and analytic number theory. He holds a B.A. in Mathematics from Princeton University (2004) and a Ph.D. in Mathematics from the University of Michigan (2009). Research interests Teaching areas Publications His research focuses on number theory, including topics such as Gauss sums, character sums, multiplicative functions, and analytic methods. He has contributed to refinements of classical theorems like Lagrange’s four-square theorem and Artin’s primitive root conjecture. His recent publications explore bounds on character sums, spectral properties of random graphs, and algebraic structures in number theory. The work demonstrates a strong emphasis on analytic and algebraic techniques. Leo has been affiliated with Williams College’s Department of Mathematics and Statistics, which received the 2014 Exemplary Department Award from the American Mathematical Society. He has taught advanced courses such as Cryptography, Topology, and Analytic Number Theory, though these were not offered in the 2025/26 academic year.
Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member
Cynthia Vinzant is an Associate Professor in the Department of Mathematics at the University of Washington. Her research focuses on real algebraic geometry, combinatorics, and convex optimization, with applications to hyperbolic polynomials, determinantal representations, and convex algebraic geometry. She collaborates extensively on projects involving numerical ranges, quasicrystals, and geometric optimization problems. Research Interests: Real algebraic geometry and its connections to combinatorics and optimization Hyperbolic and log-concave polynomials Convex geometry and spectrahedra Applications in matrix analysis and statistical mechanics Her work spans theoretical advances in algebraic geometry and computational methods, including contributions to the study of principal minors, tropical geometry, and phase retrieval problems. Recent publications highlight her focus on Fourier quasicrystals, higher-rank numerical ranges, and combinatorial structures in matroids. Publications: Over 30 peer-reviewed articles, including influential works on quartic curves, determinantal representations, and log-concave polynomials. Grants & Collaborations: Active in interdisciplinary research, with projects supported by NSF and collaborations in algebraic combinatorics and geometric optimization.
Professor Vitali Wachtel of Bielefeld University's Faculty of Mathematics specializes in advanced stochastic processes, probability theory, and their applications in mathematical modeling. Since 2021, he holds a W3 Professorship and serves as Principal Investigator in CRC 1283 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications' since 2023. Chaired Examination Boards for Bachelor & Master Business Mathematics Member, Bielefeld Graduate School in Theoretical Sciences Research focus: Markov processes, random walks in cones, branching processes Research Trends: His recent work spans critical multitype branching in random environments (2025), asymptotic expansions for conditioned random walks (2024), and invariance principles for integrated processes. He explores connections between stochastic processes, combinatorial structures, and risk modeling with level-dependent premiums. Awards: Feodor Lynen Research Fellowship (2017), Alexander von Humboldt Foundation Teaching: Coordinates modules including 'Stochastic Processes' (24-M-PT-STP) and 'Introduction to Probability Theory' (24-B-EW-5). Active in curriculum development and academic governance through multiple university committees.
Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.
Ryan Browne is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a BMath (2004), MMath (2006), and PhD (2009) from the same institution. His research focuses on model-based clustering , classification , and measurement system quality assessment , with applications in multivariate analysis and statistical inference. He is particularly known for contributions to mixture models and their computational optimization. Education: BMath in Statistics, University of Waterloo (2004) MMath in Statistics, University of Waterloo (2006) PhD in Statistics, University of Waterloo (2009) Ryan’s work emphasizes flexible statistical methodologies , including advancements in skewed distributions, high-dimensional data analysis, and robust algorithms for clustering and classification. He has received the prestigious 2011 W.J. Youden Award from the American Statistical Association for his PhD research on measurement system evaluation. His research trends span computational statistics (e.g., sketching algorithms for big data) and model-based clustering innovations (e.g., mixtures of generalized hyperbolic distributions). Recent work explores parsimonious models, nested Gaussian structures, and efficient parameter estimation for complex datasets. Key Awards: 2011 W.J. Youden Award (American Statistical Association) Ryan collaborates on applied projects, including industrial ecology and sensory data analysis. He has developed R packages like mixture and MixGHD , which implement his methodological contributions.
Prof. Philippe Michel is a Professor in the Mathematics Institute at the École Polytechnique Fédérale de Lausanne (EPFL), where he leads research in the Number Theory group (TAN). His office is located at MA C3 634, Station 8, 1015 Lausanne, Switzerland. Michel received his education at ENS Cachan and obtained his PhD from Université Paris XI in 1995 under the supervision of E. Fouvry. His academic career includes positions as maître de conférence at Université Paris XI (1995-1998), full professor at Université Montpellier II (until 2008), before joining EPFL. Prof. Michel's research spans across analytic number theory and related fields. His work integrates diverse mathematical techniques including arithmetic geometry, exponential sums, sieve methods, automorphic forms and representations, L-functions, and more recently, ergodic theory. His research has significant implications for understanding the distribution of prime numbers, properties of L-functions, and connections between number theory and other mathematical disciplines. Michel has made substantial contributions to the study of Kloosterman sums, trace functions, and the analytic properties of families of L-functions. Peccot-Vimont prize Member of the Institut Universitaire de France Invited speaker at the 2006 International Congress of Mathematicians Member of the Academia Europaea (Academy of Europe) since 2011 Fellow of the American Mathematical Society since 2012 Prof. Michel serves on the editorial boards of several prestigious mathematical journals including Archiv der Mathematik, Journal of Algebra and Number Theory, Journal of the European Math. Society, and Journal of Number Theory. His research has been consistently funded by major mathematical institutions, supporting his work on analytic number theory and its applications. At EPFL, Prof. Michel leads the Number Theory group (TAN) within the Mathematics Institute, fostering research collaborations and mentoring young mathematicians. His team focuses on cutting-edge problems in analytic number theory, connecting with broader mathematical fields and maintaining strong international collaborations.
Dr. Michael Gallaugher is an Assistant Professor of Statistical Science at Baylor University. He holds a Ph.D., M.S., and B.S. in Statistics from McMaster University. His research focuses on clustering and classification methodologies, particularly in matrix/tensor variate data, mixed-type data, clickstream analysis, and outlier detection. He has been recognized with prestigious awards including the Vanier Canada Graduate Scholarship and the Banting Postdoctoral Fellowship. Education: Ph.D., Statistics, McMaster University (2020) M.S., Statistics, McMaster University (2017) B.S., Statistics, McMaster University (2015) Research Interests: Dr. Gallaugher's work emphasizes advanced clustering techniques for complex data structures, including high-dimensional datasets, clickstream behavior analysis, and skewed distribution modeling. His contributions span statistical methodology development and applications in fields like bioinformatics and sports science. His recent work explores hidden Markov models for time series and robust co-clustering algorithms. Publications Trends: His publications reflect a strong focus on matrix-variate distributions, skewed data modeling, and algorithmic innovation in clustering. Recent work (2022-2025) highlights advancements in contaminated normal mixtures, co-clustering for high-dimensional data, and spatial regression models. Awards: Vanier Canada Graduate Scholarship Banting Postdoctoral Fellowship Advising & Grants: While no advisees are listed, his research has been supported by grants from the Natural Sciences and Engineering Research Council of Canada. He contributes to statistical consulting services at Baylor and collaborates internationally on methodological projects. Labs & Teams: He is affiliated with Baylor's Department of Statistical Science and collaborates with research groups focused on machine learning and statistical computing.
James Martin is a Lecturer at the Department of Statistics, University of Oxford . He is affiliated with St Hugh's College and has been actively involved in organizing probability seminars since 2018. Research Interests Probability theory Random graphs and percolation Interacting particle systems Models of random growth and coagulation-fragmentation Queueing networks Combinatorial games Teaching Courses: Prelims Probability , Part A Probability , Part B Statistical Lifetime Models , Part C Probabilistic Combinatorics His publications focus on probability theory , statistical physics , and combinatorial structures . Recent work includes studies on last-passage percolation, multispecies exclusion processes, and integrable probability models. James Martin collaborates with researchers from institutions such as Uppsala University, University of Cambridge, Imperial College London, and Kyoto University. He has been a key organizer for the Oxford Probability Seminar since 2018.
László Kozma is an Assistant Professor at the Theoretical Computer Science group of the Institute of Computer Science (Freie Universität Berlin). He obtained his PhD from Saarland University under Raimund Seidel, followed by postdoctoral positions at Tel Aviv University and TU Eindhoven. His research focuses on self-adjusting data structures , adaptive algorithms , and combinatorial optimization with applications to problems like the Traveling Salesman Problem, binary search trees, and geometric data structures. Academic Affiliation: Freie Universität Berlin (since 2018) Education: PhD in Computer Science (Saarland University, 2016); postdoc at Tel Aviv University and TU Eindhoven. His work explores the intersection of data structures, combinatorial algorithms, and geometric methods. He has made significant contributions to problems involving pattern-avoidance in inputs, saddlepoint detection , and self-adjusting heaps . Key areas include: Adaptive algorithms for pattern-avoiding inputs Optimal tree and heap structures Geometric and stochastic approaches to optimization Complexity analysis of classical algorithms Recent publications highlight efficient solutions for exponential cut problems (ESA 2025), balanced TSP partitioning (EuroCG 2025), and randomized saddlepoint algorithms (ESA 2024). His research often bridges theory and practice, exemplified by the smooth heap implementation and fun projects like Recursi and Cuckoo Hashing visualization.
