• Columbia University E6894: Deep Learning for Computer Vision, Speech, and Language, guest lecturer (Fall 2018)
    • UIUC NPRE 451: Radiation Detection & Instrumentation Laboratory (Fall 2013)
    • UIUC NPRE 451: Radiation Detection & Instrumentation Laboratory (Spring 2013)
    • UIUC NPRE 451: Radiation Detection & Instrumentation Laboratory (Fall 2012)
    • Tsinghua University, part-time lecturer at Work-Study Center (2005-2009)

Academic Services

Grant Review Panel

    • National Institute of Food and Agriculture, United States Department of Agriculture (USDA-NIFA), 2018

Program Organizing Committee

    • Workshop on Real-World Recognition from Low-Quality Images and Videos, ICCV, 2019
    • Workshop on Weakly Supervised Learning for Real-World Computer Vision Applications, CVPR, 2019
    • The 1st Learning from Imperfect Data Challenge, CVPR, 2019
    • IEEE Workshop on Analysis and Modeling of Faces and Gestures, ICCV, 2017
    • Huang Symposium, UIUC, 2016

Review Editor

    • Frontiers in Big Data, ICT and Digital Humanities

Journal Reviewer

    • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
    • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
    • IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
    • IEEE Transactions on Multimedia (TMM)
    • Springer Multidimensional Systems and Signal Processing (MULT)

Conference Technical Program Committee

    • Neural Information Processing Systems (NIPS)
    • International Conference on Machine Learning (ICML)
    • IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    • IEEE International Conference on Computer Vision (ICCV)
    • IEEE International Conference on Image Processing (ICIP)
    • International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
    • International Conference on Information Fusion (FUSION)

Heilmeier Criteria

I learned this criteria from Prof. John Wen, and it was proposed by former DARPA director G. Heilmeier.

I suggest all my students follow this as well.

    1. What are you trying to do? Articulate your objectives using absolutely no jargon.
    2. How is it done today, and what are the limits of current practice?
    3. What is new in your approach and why do you think it will be successful?
    4. Who cares and why?
    5. If you’re successful, what difference will it make? What applications are enabled as a result?
    6. What are the risks?
    7. How much will it cost? How long will it take?
    8. What are the midterm and final check points to evaluate progress towards success?