Ren Wang

  • Assistant Professor of Electrical and Computer Engineering

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Education

Ph.D., ECSE, Rensselaer Polytechnic Institute, 2020
M.S., EE, Tsinghua University, 2016
B.S., EE, Tsinghua University, 2013

Research Interests

Ren Wang joined the Department of Electrical and Computer Engineering in 2022. Before joining Illinois Tech, he was a postdoctoral research fellow and a lecturer in the Department of Electrical Engineering and Computer Science at the University of Michigan. His research interests include trustworthy machine learning, high-dimensional data analysis, bio-inspired machine learning, and smart grids.

Publications

For a complete list, see Google Scholar.

  1. Ren Wang, Tianqi Chen, Cooper Stansbury, Stephen Lindsly, Alnawaz Rehemtulla, Alfred Hero, Indika Rajapakse, “RAILS: A Robust Adversarial Immune-inspired Learning System,” IEEE Access, 2022, 10: 22061-22078.
  2. Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang, “On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning,” International Conference on Learning Representations (ICLR), 2021.
  3. Ren Wang, Meng Wang, Jinjun Xiong, “Tensor Recovery from Noisy and Quantized Measurements,” EURASIP Journal on Advances in Signal Processing, 2020, 2020(1): 1-32.
  4. Ren Wang, Meng Wang, Jinjun Xiong, “Achieve Data Privacy and Clustering Accuracy Simultaneously Through Quantized Data Recovery,” EURASIP Journal on Advances in Signal Processing, 2020, 2020(1): 1-36.
  5. Ren Wang, Gauyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, Meng Wang, “Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases,” The European Conference on Computer Vision (ECCV), Springer, Cham, 2020: 222-238.
  6. Ren Wang, Meng Wang, Jinjun Xiong, “Data Recovery and Subspace Clustering from Quantized and Corrupted Measurements,” IEEE Journal of Selected Topics in Signal Processing, 2018, 12(6): 1547-1560.
  7. Pengzhi Gao, Ren Wang, Meng Wang, Joe H Chow, “Low-rank Matrix Recovery from Noisy, Quantized and Erroneous Measurements,” IEEE Transactions on Signal Processing, 2018, 66(11): 2918-2932.