Xingyu Li, PhD

Area of Study / Keywords

Machine learning Computer vision Computational medical imaging AI for health Deep learning security Visual signal/data analytics Computer Engineering Signal and Image Processing

About

Xingyu Li is an assistant professor with the Department of Electrical and Computer Engineering at the University of Alberta, an Alberta Machine Intelligence Institute (Amii) Fellow, and also a mermber of AI4Society, Cancer Research Institute of Northern Alberta (CRINA), and The Women and Children’s Health Research Institute (WCHRI). She received the B.Sc degree in Electrical Engineering from Peking University, the M.Sc degree from the University of Alberta, and Ph.D from the University of Toronto, both in Electrical and Computer Engineering. Before joining the ECE department, Xingyu was a postdoctoral fellow at the University of Toronto and a postgraduate affiliation of the Vector Institute for Artificial Intelligence. Besides academia, Xingyu also worked in industry as a video algorithm engineer.

Research

Xingyu's research interests include machine learning, computer vision, computational medical imaging, AI for health, deep learning security, and color visual signal processing. A general theme is intelligent computational systems for (i) multi-dimensional data analytics and understanding, (ii) computer vision, and (iii) health informatics. Her research outcomes have been applied to biologists' state-of-the-art study on pathology image based breast cancer prognosis.

Xingyu's current research projects include:

• Computational medical imaging (computational pathology, trusted diagnostic systems, weak-supervision segmentation, etc)
• AI for health (EEG signal analysis, cranial implant design, assisted-robotic surgical data analysis, computational methods for COVID-19, etc)
• Learning-based visual data analysis (visual-based anomaly detection, open-set problems, etc.)
• Security in deep learning (adversarial attack/training, data poison detection, etc.)

Teaching

EXEN 2452 - Applications with Deep and Graphical Networks

ECE740 - Deep Learning in Computer Vision

ECE380 - Introduction to Communication Systems

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

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