Jeff Phillips

Jeff Phillips is a Professor at the Kahlert School of Computing, University of Utah. His research focuses on Algorithms, Computational Geometry, and Data Management, with contributions to areas like approximate databases, stream processing, and statistical analysis. He is actively involved in academic conferences such as the International Symposium on Computational Geometry (SoCG).

His work emphasizes practical and theoretical advancements in machine learning, bias mitigation in word embeddings, and efficient algorithms for high-dimensional data. Recent projects include an NSF-funded REU site promoting reproducibility in intelligent computation. His research also intersects with statistical methods, geometric algorithms, and scalable data processing techniques.

Grants & Collaborations:

• NSF REU Site: Trust and Reproducibility in Intelligent Computation (2023)
• AF: Small: Geometry of Learning Structured Data Objects (2021)