加拿大阿尔伯塔大学硕士,博士和博后实验室招聘

Associate Professor, Faculty of Engineering - Electrical & Computer Engineering Dept

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  Email
  
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  lcheng5@ualberta.ca
  
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  Phone
  
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  (780) 492-9305
  
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  Address
  
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  11-365 Donadeo Innovation Centre For Engineering
  9211-116 St
  Edmonton AB
  T6G 2H5
  
  

Overview

About

Li Cheng is an associate professor with the Department of Electrical and Computer Engineering, University of Alberta. He also holds an adjunct position with A*STAR, Singapore, where he has led a group in Machine Learning for Bioimage Analysis at the Bioinformatics Institute. Prior to joining University of Alberta in year 2018, He worked at A*STAR, Singapore, TTI-Chicago, USA, and NICTA, Australia. He received his BSc degree in Computer Science from Jilin University in 1996, M Eng degree from Nankai University in 1999, and PhD in Computing Science from the University of Alberta in 2004. His research expertise is mainly on computer vision and machine learning. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), the Association for Computing Machinery (ACM), and the Association for the Advancement of Artificial Intelligence (AAAI). He has supervised a number of research students and postdocs, many of them have taken scientist or engineer positions at the major tech companies (Google, Nvidia, Bosch, OmniVision, etc.) or hold faculty positions at prestigious universities in China (Southeast University, China University of Geosciences, etc.) and in Australia (University of Adelaide)

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Research

Current research

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  Visual behavior analysis, to look at humans and also animals and to interpret their poses and behaviors, such as pose estimation, tracking, and action recognition.
  
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  Biomedical image understanding, including retinal fundus image analysis, microscopic image analysis, neuronal tracing, etc.
  
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  Computer vision, machine learning, including deep learning, and applications.
  

I am in the process of setting up our Vision and Learning Lab at the University of Alberta, and am actively looking for graduate students and postdocs to work on exciting computer vision and machine learning problems.

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