I am a data scientist at the McCombs School of Business, the University of Texas at Austin. My research focuses on developing machine learning and artificial intelligence methods to improve decision-making and to benefit people, organizations, and society. Most of my work aims to augment ML & AI by bringing to bear the particular problems that machine learning and AI inform (e.g., health care, business decisions) and the context in which learning itself occurs, with the goal of effectively dealing with the constraints and taking advantage of the opportunities presented in these environments. My research integrates business, machine learning and artificial intelligence, and I have addressed challenges in different domains, including health care, smart electricity grid, fraud, finance, and the future of work, such as making online labor markets work better for all.
My research also addresses general machine learning challenges that affect machine-learning-driven decision making, such as dealing with missing values, and problems related to active learning and reinforcement learning.
Below are descriptions of some recent works. See more details in my CV and Google Scholar profile.
In 2014 I co-founded Sweetch, a platform for large-scale prediction, prevention and outcome improvement of chronic diseases.