Computer Science, New York University
I am a Computer Science Ph.D. student at the New York University. I am passionate about designing multimodal medical AI incorporating time series clinical data, genomics, medical images, and clinical texts. I also have a deep interest in developing human-understandable models. More specifically, a language by which we can talk to AI for better alignment by studying the machine's behavior with and without humans. The types of interpretable machine learning techniques that can be useful in various areas of healthcare, such as clinical decision-making and predictive modeling, are what I am now researching.
Before joining NYU, I graduated with a master's in Applied Statistics and Data Science from the University of Texas Rio Grande Valley where I was fortunate to work with Professor Tamer Oraby on Bayesian inference and machine learning.
Our work on linear programming optimization model application in irrigation project was accepted in Irrigation and Drainage Journal.
Our work on design of a stand-alone energy hybrid system for a makeshift health care center was accepted in Journal of Building Engineering.
Our work on improving spatial agreement in machine learning-based landslide susceptibility mapping was accepted in Remote Sensing journal.