Xingyu Li, PhD
Area of Study / Keywords
Machine learning Computer vision Computational medical imaging AI for health Deep learning security Visual signal/data analytics
Xingyu Li is an assistant professor with the Department of Electrical and Computer Engineering at the University of Alberta. 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
I am looking for self-motivated students (undergraduate, M.Sc, or Ph.D) and postdocs to work with me. If interested, please contact me with your CV, transcripts, and a brief description of your research interest (a specific topic is a bonus!). Thesis and sample publications are highly welcomed.
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.
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.