I am currently an Assistant Professor in the Department of Automotive Engineering in Clemson University International Center for Automotive Research (CU-ICAR). My academic profile link is here. The underlying theme of my research activities has been to empower robot and vehicle intelligence and autonomy by sensing, understanding, and learning from dynamic environments. I also have been developing assistive navigation aid technologies to help seniors and people with disabilities.
I am recruiting (Fall 2019) outstanding Ph.D students (full scholarship) and visiting Ph.D students and scholars to join my group and work on cutting-edge research areas such as: autonomous robot/vehicle, multi-modal sensor fusion, visual perception, machine/deep learning, artificial intelligence, signal processing, optimization and intelligent control.
Ph.D applicants shall send your Resume with GPA, transcripts, TOEFL or IELTS, and GRE (required) scores to: bli4 [AT] clemson.edu
Students who are self-motivated and meet the following requirements are encouraged to apply:
Bachelor or Master degree in Electrical / Automotive / Computer Engineering, Automation and Control, Computer Science, Applied Mathematics, Information Science, or related majors.
Mathematical background and programming skills in Matlab, Python, or C/C++.
Previous research experiences with robotics, computer vision, image processing, machine/deep learning, and signal processing are preferred, but not required.
Students who participated in national electronics design contest or mathematical modeling competition and achieved ranks are preferred.
Ph.D. in Electrical Engineering
The City College, The City University of New York
Master of Engineering
Beihang University, Beijing, China
Bachelor of Engineering
Beijing Forestry University, Beijing, China
Sept. 27, 2018: My US patent “Method, apparatus and computer program product for mapping and modeling a three dimensional structure” (Inventors: Bing Li and Rich Valde) was published online.
May 28, 2018: My paper on vision-based assistive navigation was published on IEEE Transactions on Mobile Computing! Project.
Field test at bridge-tunnel vertical surface at Riverside Dr, New York
Wall-climbing robot for non-destructive evaluation using impact-echo and metric learning SVM
I am teaching below graduate-level course currently:
AuE 8930 Automotive Perception and Intelligence
Mon. and Wed.: 9:15-10:30 AM (start from Jan. 9)
Room: 401 CGEC, Clemson University
This course will introduce the fundamental technologies for autonomous vehicle sensors, perception and machine learning, from electromagnetic spectrum characteristics and signal acquisition, vehicle extrospective sensor data analysis, perspective geometry models, image and point cloud processing, to machine/deep learning approaches. We will also have hands on programing experience in vehicle perception problems through homeworks and class projects.
COURSE OBJECTIVES (to provide a fundamental understanding of:)
Electromagnetic spectrum characteristics and Radar signal processing.
The mechanism of human vision: eyes, visual brain, depth and color.
Visual perception using image processing and machine learning recognition.
3D LiDAR and point cloud data representation and processing.
Deep learning for vehicle perceptual sensor data processing.
I enjoy and had quite a bit of experience teaching as Adjunct Lecturer and Teaching Assistant previously in The City College, The City University of New York.