Algorithms for Designing Randomized Controlled Trials and for Solving Structured Linear Equations

Zoom link for remote attendees: password 123123

Abstract: Two key components of a data science pipeline are collecting data from carefully planned experiments and analyzing data using tools such as linear equations and linear programs. I will discuss my recent work on fast algorithms for designing randomized controlled trials and solving structured linear equations.

In the first part of the talk, I will present efficient algorithms that improve the design of randomized controlled trials (RCTs). In an RCT, we want to randomly partition experimental subjects into two treatment groups to balance subject-specific variables, which might correlate with treatment outcomes. We formulate such a task as a discrepancy minimization question and employ recent advances in algorithmic discrepancy theory to improve the design of RCTs. In the second part of the talk, I will briefly present my recent research on fast solvers for linear equations in generalized graph Laplacians.

Bio:  Peng Zhang is an assistant professor in the Department of Computer Science at Rutgers University. Peng is broadly interested in developing efficient algorithms for the design of randomized experiments and linear equation solving. Her work has been recognized with an NSF CAREER Award, an Adobe Data Science Research Award, a Rutgers Research Council Individual Fulcrum Award, and a FOCS Best Student Paper award. Before joining Rutgers, she obtained her Ph.D. in Computer Science from Georgia Tech and was a postdoc at Yale University.