Chao Ren (任超)
Scholar

Chao Ren (任超)

Google Scholar ID: Y6OuBMYAAAAJ
Assosiate Research Professor, College of Electronics and Information Engineering, Sichuan University
image/video processingcomputer visionmultimedia
Citations & Impact
All-time
Citations
1,924
 
H-index
20
 
i10-index
37
 
Publications
20
 
Co-authors
11
list available
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • Papers published in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), NeurIPS 2022 (CCF A), Knowledge-Based Systems (KBS), Information Fusion (IF), Pattern Recognition (PR), ACM MM 2021, etc. Received support from the National Natural Science Foundation of China.
Research Experience
  • Currently an associate research professor at the College of Electronics and Information Engineering, Sichuan University. Achieved the highest average score (about Top 0.5%, 1/200) in the department of information and communication engineering at Sichuan University during undergraduate study, and received various contest awards (e.g., National Undergraduate Electronic Design Contest, 2011) and scholarships (e.g., National Scholarship, 2016).
Education
  • Received B.S. in Electronics and Information Engineering from Sichuan University in 2012; Ph.D. in Communication and Information System from Sichuan University in 2017, supervised by Prof. Xiaohai He (Sichuan University). Visiting scholar at the Department of Electrical and Computer Engineering, University of California at San Diego, CA, USA, from 2015.9 to 2016.10, supervised by Prof. Truong Nguyen.
Background
  • Research interests include programming, computer vision, image processing, deep learning, and mathematics. Biography: Bachelor's degree in Electronics and Information Engineering, Ph.D. in Communication and Information System, currently an associate research professor at the College of Electronics and Information Engineering, Sichuan University.
Miscellany
  • Looking for self-motivated Ph.D./M.S. students with solid background in programming, computer vision, image processing, deep learning, or mathematics.