I have a very broad range of interests spanning many fields in machine learning, e.g., optimization, representation learning, architecture design (Transformer, Graph Neural Networks, etc.). To summarize in one sentence, I’m mostly interested in the theories that can inspire us to make better algorithms. My tenet is to make machine learning more general [1, 2, 3], efficient [5, 6, 9], and reliable [4, 7, 8] (please see my publications below).