Julie Clutterbuck is an Associate Professor in the School of Mathematics at Monash University. Her research focuses on geometric analysis, partial differential equations, and spectral theory. She has led major projects funded by the Australian Research Council, including studies on curvature flows, spectral estimates, and optimal shapes. Clutterbuck has been recognized with the Gavin Brown Prize (2014) for outstanding mathematical research. She contributes to academic activities as a conference organizer and speaker, including roles at the AMSI Winter School and the New Zealand Mathematical Society Colloquium. Her teaching commitments include advanced courses in metric spaces and multivariable calculus. Her research explores geometric evolution equations, eigenvalue problems, and capillary surfaces. Notable projects include analyzing the fundamental gap conjecture and studying ancient solutions to curvature flows. Collaborations span international institutions, reflecting her contributions to global mathematical research networks.
Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.
Dr. Bence Borda is an Assistant Professor in Mathematics at the University of Sussex, affiliated with the School of Mathematical and Physical Sciences. He holds a PhD from Rutgers University (2016), advised by József Beck, and has held postdoctoral positions at the Alfréd Rényi Institute of Mathematics (2017–2019) and Graz University of Technology (2019–2024), supported by a Lise Meitner Fellowship (FWF) from 2022–2024. PhD: Mathematics, Rutgers University (2016) His research lies at the intersection of probabilistic number theory , Diophantine approximation , random walks on groups , and harmonic analysis , with a focus on equidistribution theory and discrepancy measures. Recent publications explore limit laws for cotangent sums, quantum modular forms, and stochastic behavior in number-theoretic sequences. Key trends in his work include the use of Wasserstein metrics for quantifying equidistribution, random matrix eigenvalues in compact groups, and determinantal point processes on geometric manifolds. His contributions span both theoretical analysis and computational applications. Scientific Awards Lise Meitner Fellowship (FWF), 2022–2024 He has supervised teaching at institutions including Rutgers University and Budapest Semesters in Mathematics, covering calculus, analysis, and graduate-level courses. He co-organized the 2025 Analysis and Optimal Transport Workshop at Sussex and actively participates in international conferences.
Nikhil Srivastava is an Associate Professor of Mathematics at the University of California, Berkeley, and a Senior Scientist at the Simons Institute. He received his PhD in Computer Science from Yale University in 2010 under the supervision of Daniel Spielman, following his undergraduate studies at Union College. After postdoctoral positions at the Institute for Advanced Study (Princeton), MSRI, and a research position at Microsoft Research India, he joined UC Berkeley in 2015. His research spans theoretical computer science and mathematics, with particular focus on spectral graph theory, random matrices, the geometry of polynomials, asymptotic convex geometry, and numerical analysis . His work often bridges the gap between pure mathematics and theoretical computer science, developing deep connections between polynomial methods, linear algebra, and combinatorial structures. Srivastava's research has been recognized with several prestigious awards including the SIAM Polya Prize , the NAS Held Prize , and the AMS Foias Prize , highlighting his significant contributions to the field. His most recent work focuses on numerical linear algebra, spectral theory, and computational mathematics, with publications spanning from algorithmic foundations to applications in mathematical physics. He actively mentors graduate students, currently advising Rikhav Shah, Zack Stier, and Isabel Detherage. His past students include Jorge Garza Vargas (co-advised with Dan Voiculescu), Theo McKenzie, Jess Banks, Satyaki Mukherjee, Archit Kulkarni, Nick Ryder, and Aaron Schild (co-advised with Satish Rao). His research has been supported by the NSF and the Sloan Foundation. At Berkeley, Srivastava has been actively involved with the Simons Institute, participating in numerous programs including Complexity and Linear Algebra (Fall 2025), Sublinear Algorithms (Summer 2024), and several others dating back to 2013. He regularly teaches courses in mathematics, including Discrete Mathematics (Math 55) and Multivariable Calculus (Math 53), and runs a seminar on Discrete Analysis in Evans Hall.
Professor Fabian Waleffe is an Applied Mathematician at the University of Wisconsin, Madison, holding a joint appointment between the Department of Mathematics and the Department of Engineering Physics. His academic career spans multiple decades with extensive teaching experience at both UW Madison and MIT. Professor Waleffe's primary research focuses on the fundamental problem of turbulence in fluid flows and its relation to Exact Coherent States. He developed the Self-Sustaining Process theory for shear flows, which provides an ab initio method to discover families of 3D traveling wave solutions of the Navier-Stokes equations in all canonical shear flows. His research also encompasses geophysical flows and computational methods for solving partial differential equations, including spectral integration methods and numerical techniques for incompressible flows. His publication record demonstrates consistent focus on hydrodynamic instabilities, turbulence, and coherent structures. Recent work examines optimal heat transport in Rayleigh-Bénard convection, streak instability, and near-wall turbulence, integrating theoretical analysis, numerical computation, and physical insight to address fundamental questions in fluid dynamics. His research has evolved from early work on triad interactions in homogeneous turbulence to the current focus on exact coherent structures that form the 'backbone' of turbulent shear flows. Professor Waleffe has taught extensively across the mathematics and engineering curriculum. He has taught Math 321 (Vector and complex calculus for the physical sciences) for 29 semesters at UW Madison, along with numerous other undergraduate and graduate courses. His teaching spans from introductory calculus to advanced graduate topics in hydrodynamic instabilities and turbulence, reflecting his deep commitment to mathematical education in the physical sciences.
Ankur Jain is a Professor in the Mechanical and Aerospace Engineering Department at The University of Texas at Arlington, with a joint appointment in Bioengineering. His research focuses on heat transfer in Li-ion batteries, microscale thermal transport, bioheat transfer, and additive manufacturing. He holds leadership roles, including serving as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technologies and Secretary of the ASME Heat Transfer Division's K16 Committee. Education: Ph.D. (2007) and M.S. (2003) in Mechanical Engineering from Stanford University; B.Tech. (2001) in Mechanical Engineering from IIT Delhi with top honors. Research interests span energy conversion/storage, thermal management of electronics, and biomedical heat transfer applications. Notable achievements include the NSF CAREER Award (2016), ASME Fellow status (2022), and UTA President's Award for Excellence in Teaching (2022). His work has been supported by NSF, DOE, ONR, and Indo-US Science & Technology Forum. Advancing thermal runaway prevention in Li-ion batteries and improving additive manufacturing processes are key current focuses. Collaborations include industry partners like Underwriters Laboratories and Cuberg, Inc.
Shouhei Honda is a Professor at the Graduate School of Mathematical Sciences, The University of Tokyo, specializing in Geometric Analysis. His research focuses on metric measure spaces with Ricci curvature bounded below, collapsing Riemannian manifolds, and spectral convergence phenomena. Education: PhD (2009) from Kyoto University. Awards: Geometry Prize (2018), Young Scientists' Prize by MEXT (2017), Takebe Katahiro Prize (2015). His work bridges abstract metric geometry with concrete geometric structures, particularly in Ricci limit spaces and RCD spaces. Recent studies emphasize spectral properties, Sobolev mappings, and stability theorems under curvature constraints. The 15 most recent articles highlight advancements in Ricci curvature bounds, collapsing geometry, spectral convergence, and non-collapsed RCD spaces. Key topics include topological stability, Weyl's law, harmonic maps, and weakly second-order differential structures. Scientific Awards: Geometry Prize, Mathematical Society of Japan (2018) Young Scientists' Prize, MEXT (2017) Takebe Prize, Mathematical Society of Japan (2015) Grants: Grant-in-Aid for Transformative Research Areas (2022-2027) Grant-in-Aid for Scientific Research (B) (2021-2026) Grant-in-Aid for Scientific Research (B) (2018-2021) Honda actively contributes to academic and public engagement, including lectures on geometry and Ricci curvature at institutions like the University of Tokyo and Tohoku University. He collaborates with leading researchers in geometric analysis and curvature bounds.
Dr Jon Warren is a Reader in Statistics at the University of Warwick, specializing in probability theory. His research spans stochastic flows, random matrices, and properties of Brownian motion, with significant contributions to understanding complex stochastic systems. Research Interests: Dr Warren's work is centered on probability theory, particularly in the areas of stochastic flows, random matrices, and Brownian motion. His research delves into the intricate behaviors of these systems, exploring their properties and applications in various mathematical contexts. Publications: His recent publications cover a wide range of topics within probability theory, including stochastic heat equations, Dyson Brownian motion, and random matrix theory. These works highlight his expertise in both theoretical developments and practical applications of stochastic processes. Teaching: He teaches ST910 Introduction to graduate probability, demonstrating his commitment to educating the next generation of statisticians and probabilists. Contact: Dr Warren can be reached at J.Warren@warwick.ac.uk for academic inquiries or collaboration opportunities.
Sebastian Cioaba is a Professor in the Department of Mathematical Sciences at the University of Delaware (UD), part of the College of Arts & Sciences. His research focuses on spectral graph theory, algebraic combinatorics, and their applications. He earned his Ph.D. from Queen’s University (2005) and joined UD in 2009 after postdoctoral work at UC San Diego and the University of Toronto. Cioaba has advised 8 Ph.D., 4 M.Sc., and numerous undergraduate researchers, with current advisees including John Byrne and Isabel Byrne. His work is supported by NSF, NSA, and international grants. Education - B.Sc. Mathematics & Computer Science, University of Bucharest (Undergraduate) - Ph.D. Mathematics, Queen’s University (2005) Research & Awards - 2024 College of Arts & Sciences Award - Co-editor of Discrete Mathematics and Linear Algebra and its Applications - Over 70 publications and two books: A Bridge to Advanced Mathematics (2023) and A First Course in Graph Theory and Combinatorics (2022, 2nd ed.) Teaching & Service - Organized conferences in discrete mathematics - Supervised over 25 undergraduate and high school students in research projects Advising - Current Ph.D. students: John Byrne, Isabel Byrne, Colby Sherwood - Notable past advisees include Vishal Gupta (Ph.D. 2025, Rochester) and Dheer Noal (Ph.D. 2022, Memphis postdoc)
Shaun Lui is Professor and Head of Mathematics at the University of Manitoba's Faculty of Science. His research develops advanced numerical methods for partial differential equations with applications in fluid dynamics and electromagnetics. Education includes B.Sc./M.Sc. from University of Toronto and Ph.D. from Caltech. Research focuses on spectral collocation methods in space-time, domain decomposition, and finite volume schemes. Recent work establishes spectral accuracy for Stokes flows and matrix singularity bounds. Supervises graduate students in numerical PDE projects.
Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.
Kyle Luh is an Assistant Professor at the University of Colorado Boulder in the Department of Mathematics, part of the College of Arts and Sciences. His research focuses on probability, random matrix theory, and randomized algorithms. Education: Ph.D. in Mathematics from Yale University (2017) His recent work explores eigenvalue gaps in random matrices, controllability of non-Hermitian systems, and applications to sparse reconstruction. Publications span topics like circular law for block band matrices, Littlewood–Offord inequalities, and stability analysis in quantum walks. Key trends in his research include spectral analysis of random graphs, robustness in learning algorithms, and combinatorial aspects of matrix theory. His articles highlight intersections between pure probability and applied computational methods.
James R. Lee is a Professor in the Department of Computer Science at the University of Washington. His research spans theoretical computer science, probability, and geometry. He has held visiting scientist roles at Microsoft Research (2023, 2018, 2017) and participated in programs at the Simons Institute (2023, 2020, 2018, 2017, 2014). Research Interests: Algorithms, complexity theory, convex optimization, metric embeddings, spectral graph theory, probability, stochastic processes, and the interplay between discrete and continuous analysis. Teaching: Courses on modern algorithms, quantum computing, optimization theory, and spectral methods in theoretical computer science. Scientific Contributions: Developed sparsification algorithms for generalized linear models and norms with near-linear size guarantees (STOC'24, FOCS'23). Extended Cheeger-type inequalities to higher eigenvalues (STOC'12, STOC'18). Proved super-polynomial lower bounds for LP/SDP relaxations in constraint satisfaction (STOC'15, FOCS'13). Disproved Benjamini-Papasoglou conjectures on annular separators (Discrete Comp. Geom. 2024). Advanced understanding of random walks in geometric and unimodular graphs (Israel J. Math. 2023, GAFA 2023). Scientific Awards: Best Paper Award, STOC 2015
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.
Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.
