I'm an Associate Professor in the Department of Electrical and Computer Engineering at University of Alberta. I did my PhD work in the Department of Electrical and Computer Engineering at University of Toronto from 2008 to 2012, and was an M.A.Sc. student in the same department from 2006 to 2008. I received the B.Eng. degree in 2005 from Sun Yat-sen University, China. Now I'm working with my team on exciting topics in the cross-disciplinary areas of data mining, applied machine learning, text mining and natural language processing, statistical learning, and networked and distributed systems.
Ongoing Research Projects
Currently, Di is actively working with his PhD and Master’s students on the following topics, in collaboration with a few industrial partners and other academic institutes (mainly including Tencent, Wedge Networks, Meridian Lightweight Technologies, and University of Toronto):
To Prospective Students:
I encourage self-motivated students that are interested in working with me to contact me through email 2-3 months before you plan to submit your application to University of Alberta.
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.Fall Term 2020
Overview of parallel/distributed computing including concepts and terminology. Principles of programming with shared memory and synchronization methods. Multithread programming with Pthreads and OpenMP. Message passing computing: the Message Passing Interface library. Design and performance of parallel algorithms. Prerequisites: CMPUT 275 and 379.Winter Term 2021
Approaches, techniques and tools for data analysis and knowledge discovery. Introduction to machine learning, data mining, and the knowledge discovery process; data storage including database management systems, data warehousing, and OLAP; testing and verification methodologies; data preprocessing including missing data imputation and discretization; supervised learning including decision trees, Bayesian classification and networks, support vector machines, and ensemble methods; unsupervised learning methods including association mining and clustering; information retrieval.Winter Term 2021