Name: Daniel Alabi, Postdoctoral Research Scientist, Columbia University

Date: Tuesday, Feb. 13, 2024 at 11 a.m. 

Location: KACB, 1116

Title: Private Linear Regression: Options, Obstacles, and Opportunities

Abstract: Researchers and practitioners (including social scientists, political scientists, economists, and healthcare scientists) crucially rely on statistical methods to further the study of individuals, society, and human behavior via inferential analysis. Unfortunately, the naive application and release of resulting statistics could reveal sensitive information about the individuals in the dataset. As a result, differential privacy (DP) has been proposed as a strong definition of privacy that can prevent reconstruction and membership inference attacks. However, we are still in the nascent stages of understanding the statistical validity of DP methods.

One of the most fundamental statistical techniques is linear regression, commonly used for predicting, testing, and augmenting relationships between two or more variables. In this talk, I will discuss my algorithmic and analytical contributions to private linear regression, showcasing the capabilities and limits of such methods in a range of settings.

Bio: Daniel G. Alabi is a Simons Society Junior Fellow at Columbia University. He earned his Ph.D. in computer science from the Theory of Computation group at Harvard University, where he was advised by Prof. Salil Vadhan. Also, he is the president and co-founder of NaijaCoder, Inc. NaijaCoder aims to proliferate early algorithms education in Africa with a focus on Nigeria.

His research interests lie primarily in the design and analysis of algorithms, with a current focus on computational science applications. During his Ph.D., he was supported by a Fellowship from Meta, a Harvard CRCS Graduate Fellowship, and a Courtlandt Memorial Fellowship from Harvard GSAS.