Kin Fun LI, PhD, MBA, PEng, SMIEEE

Professor, Electrical and Computer Engineering

Director, Professional Master of Engineering Programs

Telecommunications and Information Security (MTIS)


Applied Data Science (MADS)

Vice Chair, IEEE Victoria Section

Office: Engineering Office Wing 409; Office Hours: appointment by email

Telephone: +1-250-721-8683; email: kinli(AT)uvic(DOT)ca

Current/Recent Lectures

Fall 2021:

ECE 255 Introduction to Computer Architecture

ECE 591 Career Development I

Courses Taught: ECE/ELEC/SENG Design Project (check out the Robot Pet:; ENGR 120 Design and Communication II (check out first-year engieering robot competition:; ENGR 330 Professional Career Planning and Engineering Leadership; ECE 255 Introduction to Computer Architecture; ECE 355 Microprocessor Systems; ECE 356 Engineering System Software; ECE 420 Artificial Intelligence; ECE 450 Computer Systems and Architecture; ECE 455 Real Time Computer Systems Design Project; ECE 460 Computer Communication Systems; ECE 499A Mobile Robot Design Competition; SENG 380 Applied Cost Engineering; SENG 440/540 Embedded Systems; ECE 563 Advanced Computer Architecture; ECE 579A Practice of Applied Data Analytics; ECE 590 Information Retrieval on the Web; ECE 590 Data Mining and Application; ECE 590 FPGA-based Object Detectiion for Computer Vision; ECE 5909 FPGA-based Classifier for Computer Vision; ECE 590 Artificial Intelligence Hardware for Internet of Things; ECE 590 Sensing Technologies in Telemedicine; ECE 590 Social Media Mining, Techniques, Challenges, and Applications; ECE 592 Career development; ECE 699 Web and Business Intelligence

Research Interests: hardware accelerator, data mining and analytics; imagege processing, web mining and search engines, business intelligence

Research Publications

Current Call for Papers:

Professional Activities:

University of Victoria, Faculty of Engineering, Department of Electrical and Computer Engineering.

Master of Engineering in Telecommunications and Information Security (MTIS).

Master of Engineering in Applied Data Science (MADS)