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
ECE 255 Introduction to Computer Architecture
ECE 591 Career Development I
Courses Taught: ECE/ELEC/SENG Design Project (check out the Robot Pet: http://www.youtube.com/watch?v=PHBreRQFlnM); ENGR 120 Design and Communication II (check out first-year engieering robot competition: https://www.youtube.com/watch?v=DNMqMT64H6M; 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
Current Call for Papers:
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)