- PhD, Statistics and Machine Learning, Carnegie Mellon University, 2017
- M.Eng., Electrical Engineering, National University of Singapore, 2013
- B.Eng., Electrial Engineering, National University of Singapore, 2011
Yu-Xiang Wang is an Assistant Professor of Computer Science at UCSB. Prior to joining UCSB, he was a scientist with Amazon Web Services’s AI research lab in Palo Alto, CA from 2017 to 2018. Yu-Xiang received his PhD in Statistics and Machine Learning in 2017 from the world’s first Machine Learning Department in the School of Computer Science of Carnegie Mellon University (CMU). Before that, he received his master’s and bachelor’s degrees in Electrical Engineering from National University of Singapore in 2011 and 2013 respectively. Yu-Xiang’s research interests revolve around the intersection of machine learning, statistics and optimization with special focus on statistical theory and methodology, differential privacy, large-scale machine learning, reinforcement learning and deep learning.
My research interests lie in the broad area of statistical machine learning — a research field that addresses the statistical and computational properties of machine learning algorithms and their optimality guarantees. Specifically, my work focuses on developing provable and practical methods for various challenging learning regimes (e.g., high dimensional, heterogeneous, privacy-constrained, sequential, parallel and distributed) and often involves exploiting hidden structures in data (generalized sparsity, union-of-subspace, graph or network structures), balancing various resources (model complexity, statistical power and privacy budgets) as well as developing scalable optimization tools (e.g., those tailored for deep learning).
I am also interested in applications of statistics and machine learning, such as those in clean energy, health care, housing, financial market, web services and so on. The key challenges of many such problems are in fact about how we can effectively and efficiently use the available data to make sensible predictions (supervised learning), quantify uncertainty (statistical inference), design sequential experiments (active learning / bandits) and to infer long-term consequences of a sequence of decisions (reinforcement learning).