I gave a talk “Advancing Synthetic Control for Individual-Level Causal Inference” at University of Copenhagen. Here is an abstract of my talk:Synthetic control is a widely used causal inference method for evaluating the effectiveness of government policies, such as new tariffs and tax increases. Traditionally, synthetic control is applied to aggregate-level datasets, but more recent…

I was at UCSD in January 8-10th to participate in the Workshop on Defining Holistic Private Data Science for Practice. It was great to connect with amazing people concerned about privacy in the practice of data analysis.

I was invited to give a brief talk about my research that applies synthetic control methods to sports analytics.

Our recent work on DP visualization was presented at TPDP 2024 in Boston. The main research question was “how do we explain differential privacy (DP) to tech-savvy but new-to-DP individuals?” Check out our preliminary findings here: A Visualization Tool to Help Technical Practitioners of Differential Privacy.

We are happy to share that a journal version of Differentially Private Synthetic Control paper, with more in-depth analyses of the algorithm’s behavior, is published in Journal of Privacy and Confidentiality. Take a look here!

I was at the NYC Privacy Day to discuss the future of algorithmic privacy and policy requirements. Can you fine me in this picture? 👀

An extension of my work at Amazon AWS as an intern has finally come out as a paper and was presented at SatML 2024. Yay!

I was invited to give a talk at the International Symposium on Artificial Intelligence and Mathematics (ISAIM) Conference in Fort Lauderdale, FL. The talk was about the Bayesian approach to estimating counterfactual fairness measures, with a case study on COMPAS recidivism risk score dataset.

My paper “Differentially Private Synthetic Control” was presented at the AIStats conference in Valencia, Spain.