معرفی
Yu Xiang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah since 2018. He holds a B.E. (highest distinction) from Xidian University (2008), a Ph.D. from UC San Diego (2015), and completed a postdoc at Harvard University (2015-2018). His research focuses on statistical signal processing, information theory, and machine learning, with applications in healthcare, IoT, and computational biology. Key areas include distributed testing, trustworthy AI under distribution shifts, and non-stationary time series analysis.
Education:
- Ph.D., Electrical and Computer Engineering, UC San Diego (2015)
- B.E., Telecommunications Engineering, Xidian University (2008)
His work emphasizes causal inference, invariant representation learning, and communication-efficient distributed systems. Recent studies explore Byzantine-robust testing, covariate shift adaptation, and causal discovery in mental health contexts. He has contributed to theoretical bounds in machine learning and signal processing.
Grants & Labs: Active in federally funded projects on distributed inference and causal AI. Leads the Invariant Learning Lab at the University of Utah, focusing on interdisciplinary applications of statistical theory.
