Xingyu Li, PhD

Assistant Professor, Faculty of Engineering - Electrical & Computer Engineering Dept


Assistant Professor, Faculty of Engineering - Electrical & Computer Engineering Dept
(780) 492-8571
11-209 Donadeo Innovation Centre For Engineering
9211-116 St
Edmonton AB
T6G 2H5


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


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.


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.)


EXEN 2452 - Applications with Deep and Graphical Networks

ECE740 - Deep Learning in Computer Vision

ECE380 - Introduction to Communication Systems


ECE 380 - Introduction to Communication Systems

Basics of analog communication: amplitude, angle, and analog pulse modulation; modulators and demodulators; frequency multiplexing. Basics of digital communication: sampling, quantization, pulse code modulation, time division multiplexing, binary signal formats. Prerequisite: ECE 240 or E E 238. Credit may be obtained in only one of ECE 380 or E E 390.

ECE 740 - Advanced Topics in Signal and Image Processing

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