I am particularly interested in approximate inference for neural networks and Bayesian optimization. I want to design algorithms that can incorporate prior knowledge, quantify uncertainty, and out-of-distribution detection for large dataset. Especially in the context of neural networks, these problems remain challenging. More specifically, my research leverages Probabilistic Machine Learning to evaluate robustness of different dynamical models (e.g. Neural ODE) in out-of-distribution settings.