Dr. Tegan Emerson

Pacific Northwest National Laboratory

Colorado State University

University of Texas, El Paso

Tegan Emerson received her PhD in Mathematics from Colorado State University. She was a Jerome and Isabella Karle Distinguished Scholar Fellow in optical sciences at the Naval Research Laboratory from 2017-2019. In 2014 she had the honor of being a member of the American delegation at the Heidelberg Laureate Forum. Dr. Emerson is now a Senior Data Scientist and Team Leader in the Data Sciences and Analytics Group at Pacific Northwest Laboratory. In addition to her role at Pacific Northwest National Laboratories, Dr. Emerson also holds Joint Appointments as Affiliate Faculty in the Departments of Mathematics at Colorado State University and the University of Texas, El Paso. Her research interests include geometric and topological data analysis, dimensionality reduction, algorithms for image processing and materials science, deep learning, and optimization.

Pacific Northwest National Laboratory

University of Washington

Henry Kvinge received his PhD in Mathematics from UC Davis where his research focused on the intersection of representation theory, algebraic combinatorics, and category theory. After two years as a postdoc in the Department of Mathematics at Colorado State University where he worked on the compressive sensing-based algorithms underlying single-pixel cameras, he joined PNNL as a senior data scientist. These days his work focuses on leveraging ideas from geometry, and representation theory to build more robust and adaptive deep learning models and frameworks.

Dr. Timothy Doster

Pacific Northwest National Laboratory

Tim Doster is a Senior Data Scientist at the Pacific Northwest National Laboratory.  He received the B.S. degree in computational mathematics from the Rochester Institute of Technology in 2008 and the Ph.D. degree in applied mathematics and scientific computing from the University of Maryland, College Park, in 2014. From 2014 to 2016, he was a Jerome and Isabella Karle Distinguished Scholar Fellow before becoming a Permanent Research Scientist in the Applied Optics division with the U.S. Naval Research Laboratory.  During his time with the U.S. Naval Research Laboratory he won the prestigious DoD Laboratory University Collaboration Initiative (LUCI) grant. His research interests include machine learning, harmonic analysis, manifold learning, remote sensing, few-shot learning, and adversarial machine learning.  

Dr. Sarah Tymochko

University of California, Los Angeles

Dr. Sarah Tymochko received her PhD in mathematics in 2022 from the Michigan State University in the Department of Computational Mathematics, Science, and Engineering under the advisorship of Dr. Liz Munch. Her dissertation research focuses on topological tools for time series analysis. In 2017 she recieved a B.A. in mathematics from College of the Holy Cross in Worcester, MA. Her research interests include topological data analysis, dynamical systems, time series analysis, network science, and machine learning.

Dr. Alex Cloninger

University of California, San Diego

Halıcıoğlu Data Science Institute

Alex Cloninger is an Associate Professor in 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.

Dr. Bastian Rieck

Helmholtz Munich

Technical University of Munich

Bastian Rieck, M.Sc., Ph.D. is the Principal Investigator of the AIDOS Lab at the Institute of AI for Health at Helmholtz Munich, focusing on topology-driven machine learning methods in biomedicine. Bastian is also a faculty member of TUM, the Technical University of Munich, and a member of ELLIS, the European Laboratory for Learning and Intelligent Systems. Wearing yet another hat, he serves as the co-director of the Applied Algebraic Topology Research Network. Bastian received his M.Sc. degree in mathematics, as well as his Ph.D. in computer science, from Heidelberg University in Germany. He is a big proponent of scientific outreach and enjoys blogging about his research, academia in general, and software development.

Dr. Sophia Sanborn

University of California, Santa Barbara

Sophia Sanborn is a Postdoctoral Scholar in the Department of Electrical and Computer Engineering at UC Santa Barbara. Her research lies at the intersection of applied mathematics, machine learning, and computational neuroscience. In her work, Dr. Sanborn uses methods from group theory and differential geometry to model neural representations in biology and construct artificial neural networks that reflect and respect the symmetries and geometry of the natural world. She received her Ph.D. in 2021 from UC Berkeley in the Redwood Center for Theoretical Neuroscience and is the recipient of the Beinecke Scholarship, the NSF GRFP, and the PIMS Postdoctoral Fellowship.

Mathilde Papillon

University of California, Santa Barbara

Mathilde Papillon is a Physics PhD student in the BioShape Lab at UC Santa Barbara where she develops novel deep learning methods leveraging geometry and topology. She harnesses these models to study relational data, with a special focus on full-body human movement. Mathilde obtained her BSc in Honours Physics from McGill University, and also works as a data scientist in sports analytics.

Dr. Nina Miolane

University of California, Santa Barbara

Nina Miolane is an Assistant Professor at the University of California, Santa Barbara where she directs the BioShape Lab. Her research investigates the hidden geometries of life: how the shapes of neuronal activity, proteins, cells, and organs relate to their healthy and pathological biological functions. Her team co-develops Geomstats, an open-source software for differential geometry and machine learning. Prof. Miolane graduated from Ecole Polytechnique and Imperial College, received her Ph.D. from Inria, was a postdoctoral fellow at Stanford and a former software engineer. Research fundings include a NIH R01 grant, the NSF SCALE MoDL, Google Season of Codes, and the Noyce Initiative UC Partnerships in Computational Transformation Program grant. Prof Miolane was the recipient of the France-Stanford Award for Applied Science, the L'Oréal-Unesco for Women in Science Award and co-winner of the C3.aigrand Covid-19 challenge