Alexander Cloninger

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Department of Mathematics,
Halıcıoğlu Data Science Institute
University of California at San Diego
Office: AP&M 5747
Email: acloninger (at) ucsd (dot) edu

Bio

Alex Cloninger is an Associate Professor in the Department of Mathematical Sciences 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.

Current Speaking / Organizing Activities

  • I will be organizing a minisymposium at SIAM Data Science 2022 in San Diego, currently scheduled for Wednesday, September 28, 2022. I hope you can attend.

  • I will be giving a talk at the Institut Henri Poincare in Paris, as a part of the Geometry, Topology and Statistics in Data Sciences thematic quarter, during the week of October 3, 2022.

  • Call for Papers Open: I'm guest editing a special issue of Sampling Theory, Signal Processing and Data Analysis on Data Science, approximation, and harmonic analysis, along with Laura De Carli, Emily King, Wenjing Liao, Rayan Saab, and Mark Iwen. You can find more information here.

  • Call for Papers Open: I'm guest editing a special issue of La Matematica on Topology, Algebra, and Geometry in the Data Sciences, along with Tim Doster, Tegan Emerson, Emily King, Henry Kvinge, and Rachel Neville. You can find more information here.

Research

My research interests include

  • Applied Harmonic Analysis

  • Manifold Learning

  • Approximation Theory for Deep Learning

  • Statistical Distances and Optimal Transport

  • Analysis on Graphs

  • Medical and Remote Sensing Applications

Find out more.

Teaching

Current courses:

  • Fall 2022: Math 173 - Optimization for Data Science

Link to all courses taught

Publications

Full list of publications.
Google Scholar

Research Group, Colleagues, and Friends

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Dinner, 10-29-2019