Qiang Ji

Professor
Department of Electrical, Computer, and Systems Engineering
Rensselaer Polytechnic Institute
JEC 7004
Troy, NY 12180-3590
Email: qiangjirpi@gmail.com  

*About Me

Qiang Ji received his Ph.D. degree in Electrical Engineering from the University of Washington. He is a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI) and Director of the Intelligent Systems Laboratory (ISL).

His longstanding research spans two closely connected areas: human-centered computer vision, particularly nonverbal human behavior analysis, and probabilistic graphical models for representing uncertainty, structure, and dependencies in complex problems. Building on this foundation, his recent research focuses on probabilistic machine learning, including Bayesian deep learning, causal representation learning, and knowledge-augmented deep learning, with applications to human-centered and embodied AI.

From January 2009 to August 2010, he served as a Program Director at the National Science Foundation (NSF), managing NSF's machine learning and computer vision programs. Prior to joining RPI in 2001, he was an Assistant Professor in the Department of Computer Science at the University of Nevada, Reno. He also held research and visiting positions at the Beckman Institute at the University of Illinois at Urbana-Champaign, the Robotics Institute at Carnegie Mellon University, and the U.S. Air Force Research Laboratory. Dr. Ji currently serves as the director of the Intelligent Systems Laboratory (ISL). Prof. Ji is a fellow of the IEEE and the IAPR.

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*Research Interests

Human-Centered Computer Vision; Probabilistic Graphical Models; Probabilistic Machine Learning, including Bayesian Deep Learning, Causal Representation Learning, and Knowledge-Augmented Deep Learning; Human-Centered and Embodied AI. For details on current research projects, click here

Book: Probabilistic Graphical Models for Computer Vision

Updated appendix

 


*Teaching

ECSE 4850/6850 Introduction to Deep Learning

ECSE 6965: Advanced Topics in Probabilistic Deep Learning

ECSE 4961/6650 Computer Vision

ECSE 4810/6810 Introduction to Probabilistic Graphical Models

ECSE 6610 Pattern Recognition

 


*Research Projects

Human Centered Computer Vision

Probabilistic Machine Learning

Scene Graph Generation and Applications

Applications

Publications

Video Demos

Databases

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Previous Projects