Data-driven decision making is now becoming increasingly important in various disciplines, including health care, finance and business analytics. To make data-driven decisions in large-scale systems, learning the statistical associations of different elements is not sufficient. Algorithms need to be equipped with the ability to learn the underlying causal mechanism.
Meanwhile, intelligent decision making also requires us to effectively incorporate domain knowledge from various application domains to improve the statistical efficiency as well as the robustness of existing statistical methods. Probabilistically, such domain knowledge can be incorporated as structural constraints on the underlying distribution. Exploiting these underlying structures calls for new statistical methods for inference with various distributional constraints.
In this talk, I will present my recent progresses for addressing the two fundamental statistical challenges stated above, namely 1) establishing the underlying causal mechanism in complex systems, and 2) incorporating structural constraints to statistical inference.
Yuhao Wang is a 5-th year Ph.D. student at Laboratory for Information & Decision Systems at MIT working with Professor Caroline Uhler. Before joining MIT, he got his bachelor from Tsinghua University. Yuhao is primarily interested in computational and statistical challenges in causal inference, high-dimensional statistics and nonparametric statistics. Yuhao is now working on a variety of research projects, including learning causal graphical models and shape restricted density estimation.