Xingyu Li, PhD

Area of Study / Keywords

Trustworthy AI machine learning computer vision anomaly/fault detection and monitoring computational medical imaging visual data analytics

About

Xingyu Li is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Alberta. She is also a Fellow at the Alberta Machine Intelligence Institute (Amii) and a member of AI4Society, the Cancer Research Institute of Northern Alberta (CRINA), and the Women and Children’s Health Research Institute (WCHRI). Xingyu received the B.Sc. in EECS from Peking University, the M.Sc. from the University of Alberta, and the Ph.D. from the University of Toronto, all in ECE. Before joining the University of Alberta, Xingyu was a postdoctoral fellow at the University of Toronto and held a postgraduate affiliation with the Vector Institute for Artificial Intelligence. In addition to her academic career, Xingyu has experience in the semiconductor industry, having worked as a video algorithm engineer.

Research

Xingyu's research interests are centered on machine learning, computer vision, and their applications for structured/unstructured data analysis, with the ultimate goal of developing novel techniques to build intelligent systems more reliable and safer. In addition to the pursuits in trustworthy AI, Xingyu is keen on leveraging AI and machine learning for healthcare-focused innovations.

Xingyu's current research projects include:

• Trustworthy AI (adversarial attack and robustness, anomaly detection, domain generalization, safe unlearning, etc)
• Computer vision (label-efficient learning, biomedical image analysis, open-set problems, etc.)
• AI for health (assisted-robotic data analysis, automated cranial implant design, novel medical imaging modality, etc.)

Teaching

EXEN 2452 - Applications with Deep and Graphical Networks

ECE740 - Deep Learning in Computer Vision

ECE380 - Introduction to Communication Systems

ECE342 - Probability for Electrical and Computer Engineers

ECE 342 - Probability for Electrical and Computer Engineers

Deterministic and probabilistic models. Basics of probability theory: random experiments, axioms of probability, conditional probability and independence. Discrete and continuous random variables: cumulative distribution and probability density functions, functions of a random variable, expected values, transform methods. Pairs of random variables: independence, joint cdf and pdf, conditional probability and expectation, functions of a pair of random variables, jointly Gaussian random variables. Sums of random variables: the central limit theorem; basic types of random processes, wide sense stationary processes, autocorrelation and crosscorrelation, power spectrum, white noise. Prerequisite: MATH 209. Credit may be obtained in only one of ECE 342 or E E 387.

ECE 740 - Advanced Topics in Signal and Image Processing

Browse more courses taught by Xingyu Li

• Xingyu's personal webpage