Joseph Kileel

Joseph Kileel is an Assistant Professor in the Department of Mathematics at the University of Texas at Austin, with additional appointments as a Core Faculty Member of the Oden Institute for Computational Engineering and Sciences and as a member of the Machine Learning Laboratory. His academic journey includes a Ph.D. in Mathematics from UC Berkeley (2017) under Bernd Sturmfels and a postdoctoral fellowship at Princeton University (2017-2020) with Amit Singer.

Professor Kileel's research spans applied mathematics, mathematical data science, and computational algebra, with particular expertise in inverse problems for imaging science, tensor methods, and non-convex optimization. His work has important applications in cryo-electron microscopy, 3D reconstruction, and mathematical theory for machine learning algorithms. His research program is supported by the NSF, DOE, and Sloan Foundation.

His publication record demonstrates consistent high-impact contributions across multiple venues including IEEE Transactions, SIAM journals, Foundations of Computational Mathematics, and NeurIPS. His recent work shows a strong trend toward developing algebraic and geometric methods for data science problems, with increasing focus on tensor decompositions and their applications to molecular imaging. His research bridges theoretical mathematics with practical computational methods.

• Charles Chui Young Researcher Best Paper Award
• Bernard Friedman Memorial Prize for Best Thesis in Applied Mathematics

Professor Kileel currently advises six doctoral students and postdocs, maintaining an active research group that combines theoretical depth with practical applications. His students work on diverse projects spanning tensor methods, optimization theory, and applications to imaging science. The group benefits from strong connections with the Oden Institute and Machine Learning Laboratory at UT Austin, providing access to interdisciplinary collaborations and resources.

His research group focuses on developing mathematical foundations for data science problems, particularly those involving algebraic structure. Current projects include tensor decomposition algorithms, geometric methods for 3D reconstruction, and theoretical analysis of non-convex optimization landscapes. The group maintains active collaborations with researchers at Princeton, Berkeley, and international institutions, reflecting the interdisciplinary nature of his work.