About Me

I’m a Ph.D. student with a specialization in natural language processing from the Department of Statistics at the University of California, Los Angeles. I perform research on energy-based generative models applied to language problems at the Center for Vision, Cognition, Learning, and Autonomy at UCLA. My advisors are Dr. Ying Nian Wu and Dr. Song-Chun Zhu. Here is my Github.

I earned a Master of Science from the Department of Computer Science at UCLA, submitting a thesis “Deep Generative Classifier with Short Run Inference.” A deep generative classifier uses Short Run Markov Chain Monte Carlo inference, Langevin dynamics, and backpropagation through time to achieve similar classification accuracy as an analogous discriminative classifier, i.e., a convolutional neural network, while it has the advantages that it can generate data, it can learn unsupervised with additional unlabeled data, and it exhibits robustness to adversarial attacks due to the stochasticity of the Langevin equation and the top-down architecture of the underlying generator network.

Before my M.S., I worked as a Full Stack Software Engineer in the San Francisco bay area for two to three years, most recently at NatureBox in Redwood City. Before this, I earned a Bachelor of Arts from the Department of Philosophy at UCLA, focusing my studies on the philosophy of language and propositional and first-order logic.