Dissertation Defense Talks
PhD students in the Department of Statistics and Data Science give a talk that is open to Northwestern faculty and graduate students as part of their dissertation defense.
Summer 2024 dissertation defense talks
Summarizing and Characterizing Heterogeneous Treatment Effects
Date: Thursday, July 25, 2024
Time: 1:00pm
Location: Harris Hall room L28
Speaker: Jingyang (Judy) Zhang, PhD candidate
Abstract: Whenever an intervention is proposed, people want to know if it works. Quantifying and summarizing treatment effects has been a focus in fields such as public policy, business, and medicine. Current practices emphasize finding “the” treatment effect, assumed to be shared by all individuals. For a continuous outcome, individual treatment effects are summarized using the standardized mean difference, an effect size comparing the mean outcomes of the treatment group to those of the control group.
In theory, the standardized mean difference can sufficiently summarize individual treatment effects if the variations among those effects are small and completely due to sampling error. However, individuals often respond to an intervention differently because of differences in their characteristics. The standardized mean difference alone does not reflect this heterogeneity in individual effects. Furthermore, some ad hoc alternative effect sizes, such as the robust standardized mean difference, fail to fully account for the heterogeneity in treatment effects, leaving the problem of better summarizing heterogeneous effects unsolved.
My dissertation focuses on developing a novel approach that better summarizes treatment effects by providing effect size parameters that characterize the probability distribution associated with the treatment effect. Additionally, I explore the relationship between treatment effects and baseline outcomes to characterize interventions as either inequality-increasing or decreasing.
I demonstrate how these novel effect size parameters can be estimated and derive the sampling properties of the corresponding estimators. The proposed methods are applied to empirical data, including studies from the What Works Clearinghouse, the National Study of Learning Mindsets, and a meta-analysis on the effect of school-based programs on cyberbullying perpetration and victimization. These examples reveal results that current practices would likely overlook, highlighting the additional insights provided by the proposed methods. The goal of my dissertation is to enhance research on variations in treatment effects, ultimately enabling more effective implementation of interventions.
Fall 2023 dissertation defense talks
Optimization, Sampling and Their Interplay: Theory and Applications to Statistics and Machine Learning
Date: Friday, November 10, 2023
Time: 4:15pm-5:15pm
Location: University Hall 102
Speaker: Tim Tsz-Kit Lau, PhD candidate
Abstract: Optimization and sampling are two main pillars of modern high-dimensional statistics and machine learning, and more broadly, data science. Optimization theory and algorithms have been heavily involved in the development of numerical solvers for high-dimensional statistical estimation problems under the frequentist paradigm as well as the success of deep learning, whereas efficient sampling procedures have been the major workhorse of Bayesian inference and uncertainty quantification. Leveraging the recently revived and intriguing connection between optimization and sampling, I study the theoretical underpinning in their interplay, and develop novel algorithms for applications to statistics and machine learning. In particular, I address two intrinsic issues arising in both high-dimensional statistical estimation and sampling problems—"nonsmoothness'' and "nonconvexity'', which are exacerbated by the notoriously inevitable "curse of dimensionality'' brought by massive datasets and gigantic models, employing tools from convex optimization and diffusion processes. Finally, I also explore the use of deep learning to develop novel estimation procedures for various high-dimensional regularized M-estimation problems.