Professor Adam Harper specializes in analytic and probabilistic number theory at the University of Warwick. His research examines moments of random multiplicative functions, distribution of zeta sums, and extreme values in number theory. Teaches MA4L6 (Analytic Number Theory) and MA257 (Introduction to Number Theory). Recent publications (2019-2024) address Halász's theorem, Fyodorov-Keating conjectures, and character sum distributions. Research bridges combinatorics, probability, and statistical mechanics, with focus on multiplicative chaos and Gaussian processes.
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.
Adam Bouland is an Assistant Professor of Computer Science at Stanford University, affiliated with the CS Theory Group. He holds a Ph.D. from MIT (advised by Scott Aaronson), followed by postdoctoral research at UC Berkeley and the Simons Institute for the Theory of Computing (advised by Umesh Vazirani). His research focuses on quantum computing theory, computational complexity, and their connections to physics. He teaches courses such as Quantum Complexity Theory (CS 359D) and Quantum Computing (CS 259Q), and has advised numerous doctoral, master’s, and postdoctoral researchers. His research group includes Tamara Kohler (postdoc), Shaun Datta, Jack Zhou, Jordan Docter, and Chenyi Zhang (doctoral students), among others. Bouland’s work bridges quantum algorithms, entanglement theory, and complexity theory, with contributions to quantum supremacy, pseudorandomness, and holographic principles. Recent highlights include studies on BosonSampling hardness, AdS/CFT duality constraints, and efficient quantum compilation. He has served on program committees for FOCS 2019, QIP 2020, STOC 2023, and ITCS 2025. His research is supported by grants including the NSF CAREER Award for exploring quantum pseudorandomness and complexity frontiers.
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.
Zeev Rudnick is a Professor of Mathematics at Tel Aviv University, holding the Cissie and Aaron Beare Chair in Number Theory since 2012. He is affiliated with the School of Mathematical Sciences and the Department of Theoretical Mathematics. Education: Ph.D., Yale University, 1990 M.Sc. summa cum laude, The Hebrew University, 1985 B.Sc. summa cum laude, Bar-Ilan University, 1984 Research Interests: Professor Rudnick's work focuses on the interface of Number Theory and Mathematical Physics, particularly Quantum Chaos. His research includes eigenvalue distribution, zeros of L-functions, quantum unique ergodicity, arithmetic problems in function fields, lattice point counting, and spectral statistics. Recent publications explore zeros of modular forms, quantum models, sparse exponential sums, and spectral properties of geometric domains. Awards and Honors: Heilbronn Distinguished Visiting Professor (2019) David Rees Distinguished Visiting Fellowship (2017) Aisenstadt Chair (2014) Invited Speaker at ICM 2014 ERC Advanced Grants (2013–2024) Fellow of the AMS (2012–) AHP Distinguished Paper Award (2011) Erdős Prize (2001) Alon Fellowship (1995) Sloan Dissertation Fellowship (1989/90) Advising and Grants: He has supervised 28 Ph.D. and M.Sc. students in number theory and mathematical physics. His research is supported by an ERC Advanced Grant (RMAST, 2019–2024), following a previous ERC grant (2013–2018). He currently teaches graduate/undergraduate seminars and analytic number theory.
Jared Duker Lichtman is a Szegö Assistant Professor at Stanford University's Department of Mathematics, beginning in the 2024-25 academic year. Previously, he served as an NSF Postdoctoral Fellow at Stanford under Prof. Kannan Soundararajan. He completed his PhD at the University of Oxford in 2023, focusing on multiplicative number theory. His research centers on prime number distribution, multiplicative structures, and analytic number theory. Notably, he established a world record in studying primes' distribution within arithmetic progressions. His work bridges classical number theory with modern techniques like sieve methods and L-function analysis. Jared's publications explore topics such as the abc conjecture, twin primes, and Erdős problems. While no formal awards are listed, his groundbreaking contributions to prime distribution and multiplicative number theory mark him as a rising star in the field. His academic trajectory includes a focus on primes in arithmetic progressions, Goldbach conjecture extensions, and probabilistic number theory applications. Ongoing research likely continues this trajectory, with potential implications for cryptography and additive number theory.
Zhou Zhou is a Senior Lecturer in the School of Mathematics and Statistics at the University of Sydney. His academic roles include Senior Lecturer (2022–present) and Lecturer (2018–2021) at the University of Sydney, as well as postdoctoral positions at the University of Michigan and University of Minnesota. He holds a Ph.D. in Applied & Interdisciplinary Mathematics from the University of Michigan (2015) and a B.S. in Mathematics from Nankai University (2010). His research focuses on stochastic control, mathematical finance, and game theory, with particular emphasis on time-inconsistent problems, optimal stopping, and equilibrium strategies. Key areas include applications in financial mathematics, stochastic processes, and dynamic optimization. His work has been published in journals such as Mathematical Finance, SIAM Journal on Control and Optimization, and Finance and Stochastics. Zhou has secured grants including the 2023 Faculty of Science Startup Scheme for time-inconsistent control research and the 2022 Australian Research Council grant on green investment impacts. He teaches courses like Arbitrage Pricing in Continuous Time and supervises research students in financial mathematics. His academic contributions span over 50 publications, with notable work on binomial-tree approximations for stopping problems, equilibrium strategies in mean-field games, and policy iteration for stochastic control. Presentations include talks at international conferences and universities worldwide, emphasizing interdisciplinary applications of stochastic analysis.
Adam Bouland is an Assistant Professor of Computer Science at Stanford University, where he leads the CS Theory Group. His research focuses on quantum computation, computational complexity theory, and their connections to physics. He completed his Ph.D. at MIT under Scott Aaronson, followed by postdoctoral research at UC Berkeley and the Simons Institute under Umesh Vazirani. His research explores fundamental questions in quantum computing including quantum complexity classes, quantum supremacy demonstrations, pseudorandom quantum states, quantum algorithm design, and connections to high-energy physics through AdS/CFT correspondence. Recent work investigates computational aspects of quantum systems, quantum learning theory, and noise resilience in quantum devices. Professor Bouland leads an active research group including 6 PhD students and 1 postdoctoral researcher, with joint appointments across Computer Science, Physics, and ICME departments. He regularly teaches graduate courses on Quantum Computation (CS259Q) and Quantum Complexity Theory (CS359D). His service includes program committee membership for major theoretical computer science conferences including FOCS, STOC, ITCS, and QIP.
Prof. Alessandro Zaccagnini is an Associate Professor at the Department of Mathematical, Physical and Computer Sciences, University of Parma. He graduated in Mathematics in 1989 and obtained his PhD in 1994, focusing on Analytic Number Theory. His academic journey includes a competitive appointment as Associate Professor in 2004 after serving as a Researcher from 1993 to 2004. Education : PhD in Mathematics (1994), University of Parma Academic Positions : Researcher (1993–2004), Associate Professor (2004–present) His research spans Analytic Number Theory, focusing on additive problems like the Hardy-Littlewood conjectures, Goldbach representations, prime distribution in short intervals, Diophantine equations with primes, and Mertens' constants. He also contributes to Cryptography education. Key Research Areas Additive Number Theory (Goldbach, Goldbach-Linnik problems) Prime Distribution in Short Intervals Diophantine-type Problems with Prime Variables Mertens' Constant for Arithmetic Progressions Cryptography and Mathematical Education Recent publications include studies on Laplace convolutions, Cesàro averages for additive problems, and educational materials using everyday objects for mathematical teaching. His work bridges theoretical research (e.g., Riemann zeta function) and practical applications (e.g., cryptographic protocols). Teaching responsibilities include Mathematical Analysis , Cryptography , and Elementary Number Theory for undergraduate and graduate programs in Mathematics and Computer Science.
Haipeng Luo is an Associate Professor at the Thomas Lord Department of Computer Science, University of Southern California, holding the IBM Early Career Chair. He previously worked as a Postdoctoral Researcher at Microsoft Research, NYC, and has held visiting roles at Google and Amazon. His research focuses on developing practical machine learning algorithms with strong theoretical guarantees, particularly in online learning, bandit problems, reinforcement learning, and game theory. PhD in Computer Science, Princeton University (2011–2016) BSc in Computer Science, Peking University (2007–2011) His work spans adversarial and stochastic environments, addressing challenges in reinforcement learning, game dynamics, calibration, and omniprediction. Recent publications highlight advancements in regret minimization, game equilibrium computation, and robust optimization frameworks. Key contributions include algorithms for zero-sum games, bandit problems with feedback graphs, and theoretical analyses of convergence properties in multi-agent systems. Scientific accolades include Best Paper Awards at COLT 2021, COLT 2018, NeurIPS 2015, and ICML 2015. He has received prestigious grants such as the NSF CAREER Award (2020), Google Faculty Research Award (2020), and NSF CRII Award (2018). His students have secured academic and industry positions, and he actively teaches graduate courses in machine learning and online optimization.
Cynthia Vinzant is an Associate Professor in the Department of Mathematics at the University of Washington, College of Arts and Sciences. Her research lies at the intersection of real algebraic geometry, combinatorics, and convex optimization, with a focus on polynomials, determinants, and matroids. Ph.D., Mathematics, UC Berkeley, 2011 B.A., Mathematics and Neuroscience, Oberlin College, 2007 Her research interests include real algebraic geometry, combinatorics, convex optimization, tropical geometry, and spectrahedra. She studies the algebraic and combinatorial structures underlying optimization problems and geometric objects, particularly through the lens of hyperbolic and log-concave polynomials. Her recent publications span topics such as tropicalization of principal minors, determinantal representations, Fourier quasicrystals, and log-concave polynomials. These works demonstrate strong interdisciplinary connections across algebraic geometry, combinatorics, optimization, and mathematical physics. Sloan Research Fellowship (2020) Best Paper Award, STOC (2019) von Neumann Fellowship, IAS (2020–2021) Bernard Friedman Prize, UC Berkeley (2011) Rebecca Cary Orr Prize, Oberlin College (2007) She has advised several Ph.D. students including Tracy Chin, Jonathan Niño-Cortes, Joseph Rogge, Faye Pasley Simon, Michael Ruddy, Georgy Scholten, and Abeer Al Ahmadieh. She has received significant NSF funding, including a CAREER award (2020–2025) on determinantal, hyperbolic, and log-concave polynomials. She has taught courses in tropical geometry, convex algebraic geometry, and optimization at both the University of Washington and North Carolina State University.
Mallesh M Pai is the Lay Family Associate Professor in the Department of Economics at Rice University and a CEPR Research Fellow. His research bridges mechanism design, auction theory, and blockchain economics, with applications to decentralized finance (DeFi) and privacy economics. Prior to Rice, he was a Janice and Julian Bers Assistant Professor at the University of Pennsylvania. PhD in Managerial Economics and Strategy, Kellogg School of Management, Northwestern University Bachelor's in Computer Science and Engineering, Indian Institute of Technology, Delhi His work focuses on designing robust mechanisms in environments with information asymmetry, privacy constraints, and decentralized systems. Recent publications address dynamic transaction fees, blockchain centralization, and algorithmic collusion. He serves on program committees for major conferences in computer science and economics. Indirect Persuasion (Journal of Political Economy) Dynamic Transaction Fee Mechanism Design (with Max Resnick) Collusive Outcomes via Pricing Algorithms (Marketing Science) Mallesh has received significant recognition for his work, including the Best Paper award at the American Economic Journal: Microeconomics (2021) and Best Pricing Paper from the American Marketing Association RAPSIG (2023). His research is supported by NSF grants on fair data analysis and digital privacy foundations. He teaches graduate and undergraduate courses in microeconomic theory and market design at Rice, emphasizing practical applications of theoretical models. His Erdős number is 3, reflecting interdisciplinary collaborations.
Burhaneddin İzgi serves as an Associate Professor in the Department of Mathematics Engineering at Istanbul Technical University, where he maintains an active research profile with projects extending through 2025. His work bridges theoretical mathematics with computational applications, particularly in game-theoretic problem solving. Research interests center on Game Theory and Stochastic Differential Equations, with specialized focus on matrix norm-based solution methods for zero-sum, fuzzy, and stochastic matrix games. Recent work integrates Artificial Intelligence techniques to address large-scale game complexity, reflecting interdisciplinary innovation across Numerical Analysis and Fuzzy Mathematics. Article trends (2022-2025) reveal consistent advancement of matrix norm methodologies across diverse game types, increasingly incorporating machine learning for computational efficiency. Publications demonstrate strong theoretical grounding in Mathematics while addressing practical applications in finance (e.g., IPO modeling) and decision systems. Scientific recognition includes: 2210 - Yurt İçi Yüksek Lisans Burs Programı (2008) 2211 - Yurt İçi Doktora Burs Programı (2010) Matematik Bölüm Birinciliği (2008) Üniversite İkinciliği ödülü (2008) Research funding includes TUBITAK-supported projects such as 'Stokastik Oyunlar İçin Matris Norm Tabanli Yeni Çözüm Yöntemleri Ve Yapay Zeka Uygulamalari' (2021-2023) and current grants developing chaos theory approaches for stochastic matrix games (2024-2025), demonstrating sustained external validation of his research program.
Lisa Sauermann is a Professor at the University of Bonn's Institute of Applied Mathematics, specializing in extremal and probabilistic combinatorics. She holds affiliations with the Institute of Mathematics and has been recognized with prestigious awards including the Richard-Rado Prize (2020), European Prize in Combinatorics (2021), and von Kaven Award (2023). PhD from Stanford University (2019), supervised by Jacob Fox Postdoc at Stanford University and Institute for Advanced Study (IAS), Princeton Assistant Professor at MIT (2021-2023) Her research focuses on additive combinatorics, discrete geometry, and polynomial methods, particularly the slice rank polynomial method. This technique was instrumental in deriving new bounds for three-term progression-free sets in F_pⁿ and solving problems like the Erdős-Ginzburg-Ziv conjecture in high dimensions. Her work bridges theoretical combinatorics with applications in finite field geometry and extremal set theory. The four lectures she presented at Collège de France in 2025 highlight her innovative use of the slice rank polynomial method to tackle longstanding problems in additive combinatorics, including three-term progression-free subsets and zero-sum configurations. Richard-Rado Prize (2020) European Prize in Combinatorics (2021) von Kaven Award (2023) Gauß Lectures (2024) Cours Peccot International laureate (2024-2025) Lisa Sauermann's career spans institutions like Stanford, MIT, and the IAS, with current leadership in advancing combinatorial methodologies at the University of Bonn. Her work continues to influence both theoretical and applied mathematical domains.
Wouter M. Koolen-Wijkstra is a Professor of Mathematical Machine Learning at the University of Twente (Statistics group) and a Scientific Staff Member at Centrum Wiskunde & Informatica (CWI), Amsterdam, in the Machine Learning department. His research bridges theoretical machine learning, game theory, and statistics, with active projects on multi-armed bandits, online learning, and safe inference methodologies. He co-leads INRIA-CWI associate teams (6PAC and 4TUNE) and is an ELLIS Scholar. His work emphasizes provable guarantees in learning algorithms, including: Regret minimization under risk-averse scenarios Multi-scale adaptation in online decision-making Game-theoretic equilibria computation Anytime-valid statistical inference via e-processes Recent publications demonstrate a focus on robust learning frameworks , particularly in bandit problems, hypothesis testing, and Nash equilibrium characterization, often leveraging information-theoretic and optimization principles. Awards include: Veni Grant (2015) for 'Learning at the Intrinsic Task Pace' QUT Vice-Chancellor's Fellowship (2013) for multitask learning Rubicon Grant (2010) for game-theoretic online learning ELLIS Scholar recognition He teaches graduate courses on Machine Learning Theory and Graphical Models at CWI. Current grants include collaborations with INRIA (4TUNE and 6PAC teams) and industry partnerships (e.g., PPS Booking.COM).
