One World Mathematics of INformation, Data, and Signals (1W-MINDS) Communications

Mark Iwen is a professor at Michigan State University with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE). His research interests include computational harmonic analysis, mathematical data science, signal processing, and algorithms for the analysis of large and high dimensional data sets. He is the lead guest editor of this collection.

Hung-Hsu Chou is an assistant professor in the mathematics department at the University of Pittsburgh working on signal processing and machine learning problems. He obtained his bachelor's degree in Physics from UCSB, PhD in mathematics from UCLA, and was a postdoctoral researcher at RWTH, LMU, and TUM. He studies problems in high dimension and non-convex optimization related to various machine learning models. His interests include compressed sensing, sparse/low rank approximation, implicit regularization, neural tangent kernel, diffusion model, and out-of-distribution detection.

Alex Cloninger is a Professor in the Department of Mathematics and the Halıcıoğlu Data Science Institute at UC San Diego. He received his PhD in Applied Mathematics and Scientific Computation from the University of Maryland in 2014, and was then an NSF Postdoc and Gibbs Assistant Professor of Mathematics at Yale University until 2017, when he joined UCSD. Alex researches problems in the area of geometric data analysis and applied harmonic analysis. He focuses on approaches that model the data as being locally lower dimensional, including data concentrated near manifolds or subspaces. These types of problems arise in a number of scientific disciplines, including imaging, medicine, and artificial intelligence, and the techniques developed relate to a number of machine learning and statistical algorithms, including deep learning, network analysis, and measuring distances between probability distributions.

Axel Flinth obtained his PhD Degree in 2018 from Technische Universität Berlin. He is currently an assistant professor at Umeå University. His research interests include geometric deep learning and compressed sensing.

Weilin Li is an assistant professor of mathematics at the City University of New York, City College. He received his PhD in mathematics from the University of Maryland, College Park. His research interests are in applied harmonic analysis, signal processing, and machine learning.

Jamie Haddock is the Iris & Howard Critchell Assistant Professor of Mathematics at Harvey Mudd College. Prior to joining Harvey Mudd, she received her BS in Mathematics from Gonzaga University, and Ph.D. in Applied Mathematics from University of California, Davis. After completing her degrees, Jamie was a postdoctoral fellow at UCLA where she was mentored by Prof. Deanna Needell. In 2025, Jamie received the National Science Foundation CAREER award. Her research leverages mathematical tools, such as those from probability, combinatorics, and convex geometry, on problems in data science and optimization.

Kevin Miller is an Assistant Professor of Mathematics at Brigham Young University in Provo. He earned his Ph.D. in Mathematics from UCLA in 2022, where he was awarded the National Defense Science and Engineering Graduate (NDSEG) Fellowship and the Pacific Journal of Mathematics Dissertation Award. Before joining BYU in 2024, he was a Peter J. O'Donnell Jr. Postdoctoral Fellow at the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin. His research develops data-efficient machine learning methods with mathematical guarantees, drawing on active learning, graph-based learning, and statistical learning theory.

Karin Schnass is an Austrian mathematician and computer scientist known for her research on sparse dictionary learning. She is a professor of mathematics at the University of Innsbruck.

Rongrong Wang is an associate professor in the Department of Computational Mathematics, Science, and Engineering (CMSE) and the Department of Mathematics at Michigan State University. She received her B.S. in Mathematics and B.A. in economics from Peking University in Beijing, China, and her Ph.D. in Applied Mathematics from the University of Maryland College Park. Her research interests include machine learning, bayesian networks, tensors analysis, compressive sensing, and inverse problems.