My general interests lie in machine learning and computer vision. Currently, I focus on unsupervised learning and AutoML. This fits well with a nascent and fast-evolving research field referred to as neural architecture search. My work covers the entire spectrum of research in this domain: from studying single/multi-fidelity optimization methods, to understand architecture encodings and its clustering effect through theoretical analysis and empirical evaluations. Representative papers are highlighted.