Zhiling Gu is a Research Fellow at Yale School of Public Health, having earned her Ph.D. in Statistics at Iowa State University. Her work integrates statistical theory with applications in public health and medicine. Her research spans Functional Data Analysis Network Analysis Spatiotemporal Modeling Statistical AI Foundations Nonparametric Learning applied to neuroimaging, electronic health records, and environmental health studies. Recent publications focus on Adaptive spatiotemporal models Neuroimaging data processing Pandemic forecasting frameworks Environmental exposure modeling with methodological rigor and practical implementation. Scientific achievements include Runner-up in SMI 2023 Student Paper Competition She has taught STAT 305: Engineering Statistics (ISU) STAT 226: Business Statistics Statistical Computing Statistical Learning and actively engages in academic presentations at conferences like SMI 2024 and CMStatistics 2022.
Prof. Igors Gorbovickis is an Associate Professor of Mathematics at the Department of Mathematics, School of Computer Science and Engineering, Constructor University (formerly Jacobs University Bremen). His research focuses on complex dynamical systems, including topics such as renormalization theory, bifurcation analysis, Julia sets, and applications to mathematical physics. He also contributes to discrete geometry, particularly exploring conjectures like the Kneser-Poulsen problem. His work bridges pure mathematics with interdisciplinary applications, emphasizing rigorous analysis of nonlinear systems and geometric configurations. Key areas of investigation include critical point accumulations, Hausdorff dimension estimates, and equidistribution phenomena in parameter spaces. Recent publications highlight advancements in understanding chaotic systems, circle maps, and the interplay between algebraic structures and dynamical behavior. Prof. Gorbovickis collaborates internationally, with co-authored papers appearing in journals like Advances in Mathematics , Ergodic Theory and Dynamical Systems , and Nonlinearity . His office is located at Research I, Room 128 on the Constructor University campus in Bremen, Germany.
Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab , with courtesy appointments in Structural Biology and Molecular & Cellular Physiology . He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute. Education: PhD in Electrical Engineering and Computer Science, MIT MPhil in Biological Sciences, University of Cambridge (Churchill Scholar) BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude) Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design. His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs) , RNA structure prediction , and mitochondrial transport mechanisms . Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology. Scientific Awards: Cheriton Family Professorship (2023) Two Gordon Bell Prizes (2014, 2009) Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006) Science Magazine Top 10 Breakthrough (2010) Fulbright Scholarship , NSF Fellowship , DoD Fellowship , Whitaker Foundation Fellowship Ron has advised numerous doctoral and master’s students including EJ Fine , Masha Karelina , and Briana Sobecks . His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction , GPCR signaling , and mitochondrial metabolism .
Prof. Dr. Philipp Habegger is a faculty member at the University of Basel's Department of Mathematics and Computer Science . His research focuses on Number Theory , specifically Diophantine Geometry, heights on abelian varieties, unlikely intersections, and algebraic number theory. He leads the Research Group in Number Theory and participates in collaborative seminars like the Number Theory Web Seminar with Mike Bennett and Alina Ostafe. Contact : philipp.habegger@unibas.ch | +41 61 207 26 98 Office : Spiegelgasse 1, 4051 Basel, Switzerland Academic Role : Research and teaching in number theory and Diophantine problems Research Overview Habegger's work addresses fundamental questions about the distribution of special points on algebraic varieties and the arithmetic properties of polynomial dynamics. His recent publications analyze degeneracy loci in abelian families, canonical heights, and the geometric Bogomolov conjecture. The 15 most recent articles reflect a focus on number theory, algebraic geometry, and effective bounds in Diophantine problems. Scientific Collaborations Collaborated with Ziyang Gao, Harry Schmidt, Umberto Zannier, and others Contributed to journals: Annals of Mathematics , Forum of Mathematics, Sigma , Compositio Mathematica Key themes: Abelian varieties , Heights , Unlikely intersections , CM jacobians
Professor Ben Goldys is a distinguished academic at The University of Sydney's School of Mathematics and Statistics, where he conducts research at the intersection of pure mathematics and applied sciences. His work spans multiple disciplines including stochastic analysis, partial differential equations, and financial mathematics, with significant contributions to both theoretical frameworks and practical applications in science and finance. Goldys' research interests center on stochastic (ordinary and partial) differential equations and their applications. His specific focus areas include stochastic partial differential equations, stochastic geometric PDEs, stochastic boundary value problems, stochastic fluid dynamics, ergodic theory of infinite-dimensional diffusions, and applications in financial mathematics such as interest rate derivatives, credit risk, and stochastic volatility. His work bridges pure mathematical theory (Functional Analysis, PDEs, Ergodic Theory) with complex real-world problems across multiple domains. His research aligns with the University of Sydney Faculty of Science Research Strengths including Understanding the Universe, Fundamental Laws of Nature, Complex Systems, and Next Generation Materials. Professor Goldys has secured multiple significant research grants from the Australian Research Council, including recent projects such as 'Mathematics for future magnetic devices' (2024), 'Mathematics for breaking limits of speed and density in magnetic memories' (2019), and 'Novel Approaches for Problems with Uncertainties' (2015). His current research projects focus on geometric stochastic partial differential equations and applications in micromagnetism, mean field games in finance, stochastic boundary value problems, and stochastic Navier-Stokes equations on the rotating sphere. He maintains extensive international collaborations with institutions in Germany (University of Tuebingen), Italy (LUISS University), Poland (Institute of Mathematics Polish Academy of Sciences), and the United Kingdom (University of York), working on projects involving optimal control, stochastic systems with memory, and geometric stochastic PDEs. Goldys is an active member of the Applied Mathematics Research Group and The University of Sydney Nano Institute, contributing to interdisciplinary research initiatives that connect mathematical theory with cutting-edge technological applications.
David Perkinson is a Professor of Mathematics at Reed College, where he holds a position in the Department of Mathematics. His research focuses on combinatorics, algebraic geometry, and discrete mathematics, with a particular emphasis on sandpile models, graph theory, and matroid theory. He is the author of the textbook *Divisors and Sandpiles: An Introduction to Chip-Firing*, which explores the combinatorial theory of chip-firing on graphs. Perkinson has also developed software tools like the Sandpile Java App, which visualizes and analyzes the Abelian Sandpile Model. He organizes the Cascade Lectures in Combinatorics (CALICO), a series of conferences funded by the National Science Foundation, aimed at fostering collaboration among researchers in combinatorics. His work bridges discrete mathematics with algebraic geometry, emphasizing connections between graph theory and geometric structures. Perkinson teaches advanced courses in analysis and contributes to the academic community through his research on topics such as divisor theory on graphs, sandpile groups, and combinatorial game theory. His recent publications (2015–2024) address matroid theory, sandpile dynamics, and applications of algebraic methods to discrete systems.
Prof. Dr. Urs Hartl is a faculty member in the Department of Mathematics and Computer Science at the University of Münster, affiliated with the Faculty of Mathematics and Computer Science. He is an Investigator in Mathematics Münster and a member of the Collaborative Research Centre (CRC) 1442 'Geometry: Deformations and Rigidity.' His research focuses on arithmetic geometry, representation theory, algebraic number theory, and arithmetic of function fields. He holds a prominent position in the field, contributing to areas such as Shimura varieties, p-adic Hodge theory, and the Langlands program. Affiliations: Member of CRC 1442 Geometry Investigator in Mathematics Münster Research Interests: Arithmetic algebraic geometry Algebraic number theory Arithmetic of function fields Structure theory of Shimura varieties p-adic Hodge theory p-adic Langlands programme Model theory Recent Publications: Hartl's recent work includes studies on moduli stacks of global G-shtukas, periods of Drinfeld modules, and p-adic Galois representations. His research emphasizes foundational contributions to arithmetic geometry and number theory, often involving collaborations with leading mathematicians such as Rajneesh Kumar Singh and Eva Viehmann. Grants & Advising: Hartl’s involvement in CRC 1442 reflects his leadership in geometric research. While specific advising details are not provided, his extensive publications suggest active mentorship in advanced mathematical research. Labs/Teams: Collaborates within the Mathematics Münster research group and the CRC 1442 team, focusing on geometric and arithmetic structures.
David Jerison is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), where he conducts research in Fourier analysis and partial differential equations. His work focuses primarily on free boundary problems and, more recently, on internal Diffusion Limited Aggregation (internal DLA), a stochastic growth model. He maintains an active research program with numerous publications in leading mathematical journals. Professor Jerison's research spans several interconnected areas of mathematical analysis. His primary interests include Fourier analysis and partial differential equations, with particular emphasis on free boundary problems. In recent years, he has expanded his research to include internal Diffusion Limited Aggregation, a stochastic growth model that has connections to probability theory and mathematical physics. His work often bridges geometric analysis, spectral theory, and probabilistic methods, demonstrating the deep connections between different branches of mathematics. Analysis of Professor Jerison's recent publications reveals a consistent focus on geometric aspects of partial differential equations, particularly free boundary problems. His research shows progression from classical PDE theory toward more stochastic and probabilistic approaches, as evidenced by his work on internal DLA. The publications demonstrate interdisciplinary connections between mathematical analysis, probability theory, and mathematical physics, with applications ranging from geometric measure theory to quantum mechanics. Professor Jerison is actively involved in teaching and mentoring at MIT. He has taught courses including Differential Equations (18.03), Fourier Analysis and Applications (18.103), and Differential Analysis (18.155). He also directs the Summer Program for Undergraduate Research (SPUR), which is exclusively for MIT undergraduates, and organizes the mathematics section of the Research Science Institute (RSI) for high school students. His teaching materials are available through MIT's Open Courseware platform, indicating his commitment to educational outreach and accessibility.
Gil Kalai is a Professor of Mathematics at the Hebrew University of Jerusalem since 1992, where he holds the Henry and Manya Noskwith Chair. He also serves as an Adjunct Professor of Mathematics and Computer Science at Yale University since 2004 in a long-term part-time visiting position. His academic career includes visiting positions at prestigious institutions including MIT, Cornell, IAS Princeton, Berkeley, Bell-labs, IBM, and Microsoft. Professor Kalai's research spans multiple areas within mathematics and theoretical computer science. His work in combinatorics encompasses geometric, probabilistic, and topological approaches. He has made significant contributions to the study of convex sets and polytopes, linear programming, and theoretical computer science. His influential 1988 paper with Kahn and Linial on Boolean functions pioneered applications of Fourier analysis in theoretical computer science. Kalai's research has evolved to include the application of Fourier analysis to thresholds, influences, symmetries, noise, percolation, and social choice. He has developed theories in algebraic shifting and studied face-numbers and other combinatorial invariants of polytopes. His work on the diameter of polytopes and randomized simplex algorithms has been influential in optimization theory. In 1993, his collaboration with Kahn produced a groundbreaking counterexample to Borsuk's Conjecture in 1325 dimensions. Professor Kalai's publications reveal a consistent focus on the intersection of combinatorics, geometry, and theoretical computer science. His work shows a progression from foundational combinatorial geometry to increasingly sophisticated applications of harmonic analysis in discrete mathematics. The recurring themes across his 30+ year career include Boolean functions, polytope theory, and probabilistic methods in combinatorics, demonstrating remarkable coherence in his research trajectory. 2016 European congress of Mathematics, plenary speaker 2013 ERC advanced grant 2012 Rothschild Prize 1994 International Congress of Mathematicians invited section talk, Zurich 1994 Fulkerson Prize 1993 Erdos Prize 1992 Polya Prize Though specific details of his advising are not provided in the source material, Kalai has written over 70 scientific papers and maintains an active research blog entitled "Combinatorics and More." His 2013 ERC advanced grant indicates significant research funding for his work. His extensive collaborations with researchers across multiple institutions suggest a robust research program with numerous PhD students and postdoctoral researchers, though specific names are not mentioned in the provided texts. Professor Kalai maintains active research connections across multiple institutions including Hebrew University, Yale, and various research centers worldwide. His work bridges pure mathematics and theoretical computer science, creating a unique interdisciplinary research environment that influences both fields.
Laura DeMarco is a Professor of Mathematics at Harvard University and holds the Radcliffe Alumnae Professorship at the Radcliffe Institute for Advanced Study. She is affiliated with the Department of Mathematics at Harvard's Science Center (Office 337). Her research focuses on dynamical systems, arithmetic geometry, and complex analysis, with a particular emphasis on algebraic dynamics and the interplay between geometry and number theory. DeMarco earned her Ph.D. in Mathematics from Harvard University in 2002, with a thesis on holomorphic families of rational maps. Her work explores topics such as preperiodic points, moduli spaces of dynamical systems, and arithmetic equidistribution. She has organized events like the Algebraic Dynamics Seminar and participates in conferences worldwide, including the 2025 Diophantine approximation conference in France. Her research publications analyze geometric and arithmetic properties of dynamical systems, such as the geometry of preperiodic points, bifurcation measures, and the classification of polynomial basins of infinity. Her studies often bridge algebraic geometry, complex dynamics, and number theory, contributing to foundational questions in arithmetic dynamics. DeMarco has collaborated extensively with researchers like N. M. Mavraki, H. Krieger, and X. Wang, advancing topics like bounded geometry in dynamical families and uniform results in the Manin-Mumford conjecture. Her work has been published in leading journals such as the Annals of Mathematics, Compositio Mathematica, and the Journal of the European Mathematical Society.
Scientia Professor Gary Froyland is a Professor at the University of New South Wales (UNSW), affiliated with the School of Mathematics & Statistics. He leads the ARC Laureate Centre for Dynamical Systems and Data and holds an Einstein Visiting Fellowship from the Einstein Foundation Berlin. His academic credentials include a BSc (Hons 1, Medal) in Pure and Applied Mathematics from the University of Queensland and a PhD in Mathematics from the University of Western Australia. Professor Froyland's research spans two primary domains: dynamical systems and optimization. In dynamical systems, he investigates the interplay of probability and geometry in nonlinear and chaotic systems, employing tools from ergodic theory, functional analysis, and differential geometry. His work extends to applications in oceanography, atmospheric science, and granular flows. In optimization, he focuses on decision-making in complex systems with uncertain information, developing novel approaches in mathematical programming that have been applied to mining, logistics, and medical treatment planning. His recent publications demonstrate a strong focus on coherent structures in dynamical systems, linear response theory, and applications to geophysical phenomena. The research shows increasing interdisciplinary collaboration, particularly with climate scientists and data analysts, reflecting a trend toward applying advanced mathematical techniques to real-world problems in environmental science and engineering. J.D. Crawford Prize (2025) Elected Member of the Academy of Europe / Academia Europaea (2024) ARC Laureate Fellow (2024-2029) Fellow of the Society for Industrial and Applied Mathematics (SIAM) (2021) Fellow of the Australian Academy of Science (2020) Vice-Chancellor's Award for Teaching Excellence - Postgraduate Research Supervision (2015) Professor Froyland actively supervises PhD and honors students, with current advisees including Kevin Felipe Kühl Oliveira, Nicholas Peters, and Kathrin Völkner. His research is supported by multiple grants, including an ARC Laureate Fellowship (2024-2029) for "Breakthrough mathematics for dynamical systems and data," an Einstein Visiting Fellowship (2022-2026), and several ARC Discovery Projects. His work has practical applications in climate science, mining optimization, and medical treatment planning, particularly in radiotherapy. He leads the ARC Laureate Centre for Dynamical Systems and Data, which brings together researchers to develop new mathematical approaches for analyzing complex dynamical systems. The center focuses on creating methods to identify coherent structures in spatiotemporal data, with applications spanning environmental science, social science, health science, and engineering.
Ivan Dokmanic is an Assistant Professor at the Coordinated Science Laboratory (CSL) within the University of Illinois . His research bridges signal processing , machine learning , and applied inverse problems , with a focus on acoustics, biomedical imaging, and distance geometry. Current Role : Assistant Professor, CSL Email : dokmanic@illinois.edu Research Interests : Dokmanic explores machine learning applications in inverse problems , particularly distance geometry for molecular imaging and acoustics . His work includes unlabeled sensing , where distances between points are known but their arrangement is not. This has implications for powder diffraction , indoor localization , and echo modeling . Article Trends : His recent publications emphasize distance geometry in machine learning , acoustic signal processing , and inverse problem theory . Key areas include molecular imaging , audio encryption , and sensor positioning . Collaborative work spans medical imaging , cyberphysical systems , and geometric invariants . 2016 Google Faculty Award NSF Grant (1 year, $157,079) Students and Grants : Dokmanic mentors PhD students like Puoya, Shuai, and Anadi. His research is funded by the National Science Foundation , Google , VISA , and nVidia .
Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.
Andrea Marchese is an Associate Professor in the Department of Mathematics at the University of Trento since 2019. He holds a PhD from the University of Pisa (2013) and has held academic positions at the University of Pavia, University of Zurich, and Max Planck Institute for Mathematics in the Sciences (Leipzig). His research focuses on Geometric Measure Theory, Calculus of Variations, and Optimal Transport, with contributions to minimal surfaces, branched transportation networks, and singular measure analysis. Education: PhD in Mathematics, University of Pisa (2013), Thesis: 'Two applications of the theory of currents' Master's in Mathematics, University of Milan (2008), Thesis: 'Singular points for coverings of Banach spaces' Bachelor's in Mathematics, University of Milan (2006), Thesis: 'Weak topologies and their metrizability in Banach spaces' Research Interests: Geometric Measure Theory, Calculus of Variations, Optimal Transport, Minimal Surfaces, and their applications to real analysis and geometric flows. Awards & Grants: Habilitation for Full Professor in Mathematical Analysis (2022) ERC Starting Grant (2020, ranked A with a score of 484/3272 proposals) Coordinated multiple INdAM/GNAMPA projects totaling over €25,000 Awarded grants from the University of Pavia, SNF, and Marie Sklodowska-Curie Actions Teaching: Has taught courses on Mathematical Analysis, Geometric Measure Theory, and Functional Analysis at the University of Trento, Pavia, Zurich, and Freiburg. Professional Activities: Organized international workshops, served as referee for over 30 journals (e.g., Inventiones Mathematicae , Journal of Differential Geometry ), and contributed to conferences worldwide.
Haruzo HIDA is a Distinguished Research Professor of Mathematics at the University of California, Los Angeles (UCLA). His work spans advanced topics in Number Theory, Modular Forms, Galois Representations, and Arithmetic Geometry. HIDA has held significant positions globally, including lectures and research visits at institutions in India, China, Japan, and Europe. University: University of California, Los Angeles Department: Mathematics Academic Rank: Research Professor Research Interests: HIDA’s research focuses on complex and p-adic Number Theory, Modular Forms, and their connections to Galois Representations, Iwasawa Theory, L-functions, and Automorphic Forms. His recent work addresses adjoint L-values, Selmer groups, and the interplay between arithmetic invariants and geometric structures. Publications: HIDA’s recent articles (2014-2025) explore themes like Hecke algebras, anticyclotomic Iwasawa theory, Tate-Shafarevich groups, and p-adic rigidity. His work often bridges modular forms with arithmetic geometry and automorphic representations. Students: He has supervised numerous PhD students, including Koji Kitagawa, Chandrashekhar Khare, Eknath Ghate, Ashay Burungale, and Jaclyn Lang, contributing to their research in topics like modular forms and arithmetic geometry. Grants: His research has been partially supported by NSF grants, documented across multiple publications and lecture notes.
