Alex Cloninger

Alex Cloninger is an Associate Professor in the Department of Mathematics and the Halıcıoğlu Data Science Institute at UC San Diego. He holds a PhD in Applied Mathematics and Scientific Computation from the University of Maryland, College Park, and was an NSF Postdoc and Gibbs Assistant Professor at Yale University.

His research focuses on geometric data analysis, applied harmonic analysis, manifold learning, and deep learning. He investigates methods to model data with local lower-dimensional structures, such as manifolds or subspaces, with applications in imaging, medicine, and artificial intelligence. His work integrates techniques from machine learning and statistical algorithms.

Cloninger's articles predominantly focus on geometric data methods, optimal transport, and graph-based learning. Recurring themes include advanced algorithms for manifold learning, robustness in statistical estimation, and computational optimization. Collaborative projects emphasize scalability and theoretical guarantees in learning frameworks.

He received the NSF Postdoctoral Research Fellowship and leads a collaborative NSF Research Training Group grant. His work supports interdisciplinary collaborations bridging mathematics, computer science, and engineering.