Greetings! My name is Sang. I’m a Computer Science Ph.D. student at Stanford University. My research aims to develop artificial agents that can adapt, plan, and act in diverse and uncertain environments. My research work span topic includes model-based reinforcement learning, Bayesian optimization, and active learning. I am particularly enthusiastic about utilizing these techniques to advance medicine and scientific discovery.
I completed undergraduate triple majoring in Computer Science, Economics, and Computational Chemistry at DePauw University, where I worked on decision-making for various applications such as intelligence game playing. Computational chemistry is an interdisciplinary degree I designed to find the synergy of first-principle computational modeling and experimental chemical synthesis to accelerate drug and material discovery. I was a visiting student at the University of Oxford studying probabilistic and reinforcement learning.
In 2016, I spent a gap-year working on sustainable solar technology by developing novel nano-semiconductors based on titanium dioxide nanotubes and metallic nanoparticles. These scientific discovery challenges are still inspiration and test beds for my algorithmic research in decision-making these days.