Computer Science, New York University
I am a Computer Science Ph.D. student at the New York University. I am primarily interested in a safe and trustworthy AI system that is interpretable, unbiased, and robust against adversarial and out-of-distribution attacks. Aside from that, self-supervised AI models that can learn on their own fascinate me greatly. I used to work in the computational sustainability field, primarily in energy sustainability, remote sensing, and satellite imagery.
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.