My research interest lies within the intersection of Programming Languages, Formal Methods, and Artificial Intelligence. My long-term research goal focuses on applying Deep Learning techniques to Logical Reasoning – problems such as Program Analysis, Automated Programming, and Theorem Proving – and I believe augmenting the traditional Symbolic and Rule-Based approaches with a machine learning-based “Mathematical Intuition” is the key to reaching true scalability.
So far, my colleagues and I have applied Evolutionary Algorithms to synthesizing input patterns to trigger program worst-case behaviors, and Reinforcement Learning to automatically proving relational program properties. Currently, we are working on sound Type Inference for dynamically-typed programming languages using Deep Learning.
Before I joined UTOPIA, I received my bachelor’s degree in Physics from the University of Science and Technology of China.
PhD in Computer Science, 2018 -- present
The University of Texas at Austin
BSc in Physics, 2013 -- 2017
University of Science and Technology of China
An automated pattern fuzzing framework for determining the worst-case complexity of a given application
A new algorithm for synthesizing strongly-typed recursive programs from input-output examples
An English handwriting synthesizer that can turn plain text into stylish writings and display them as animations