Mingming Gong


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Mingming Gong

Mingming Gong

Lecturer in Data Science
School of Mathematics and Statistics
The University of Melbourne

E-mail: gongmingnju [at] gmail.com
[Google Scholar]

I am a lecturer in Data Science at the School of Mathematics and Statistics, The University of Melbourne. Feel free to drop me an email if you are interested in working with me.

I obtained the PhD degree from University of Technology Sydney (2012 - 2017), supervised by Prof Dacheng Tao and Prof Kun Zhang. I received the the M.S. degree in communications and information system from Huazhong Unviersity of Science and Technology, Wuhan, China, and the B.S. degree in electrical engineering from Nanjing University, Nanjing, China, in 2012 and 2009, respectively.

From 05/2017-06/2019, I was a postdoctoral research fellow at the Department of Biomedical Informatics, University of Pittsburgh and the Department of Philosophy, Carnegie Mellon University, working with Prof Kayhan Batmanghelich and Prof Kun Zhang. From 03/2013 - 10/2013, I was a research intern at the Empirical Inference Department of Max Planck Institute for Intelligent Systems, working with Dr Kun Zhang and Prof Bernhard Schölkopf.


News

  • 04/2019, one paper accepted to ICML19.

  • 03/2019, I will join School of Mathematics and Statistics, The University of Melbourne, as a lecturer (assistant professor) on July 1st.

  • 02/2019, two papers accepted to CVPR19.

  • 02/2019, two papers accepted to AISTATS19.

  • 09/2018, one paper accepted to NeurIPS18.

  • 07/2018, three papers accepted to ECCV18.

  • 06/2018, our team won the 1st prize in single image depth prediction competition in Robust Vision Challenge 2018.

  • 06/2018, our paper on disease severity prediction has been accepted to MICCAI18. Congrats to Sumedha Singla.

  • 06/2018, our paper on causal discovery has been accepted to UAI18 as plenary presentation.

  • 02/2018, two papers have been accepted to CVPR18. Congrats to Huan Fu and Xiyu Yu.

  • 11/2017, our paper on domain generalization has been accepted to AAAI18 as oral presentation. Congrats to Ya Li.

  • 10/2017, our paper on pose estimation has been accepted to ICCV17 as spotlight presentation.

  • 06/2017, our paper entitled Causal Discovery from Temporally Aggregated Time Series has been accepted to UAI17.

  • 04/2017, I start working at University of Pittsburgh and Carnegie Mellon University as a postdoctoral research fellow.

  • 04/2016, our paper entitled Domain Adaptation with Conditional Transferable Components has been accepted to ICML16.


Research Interests

  • Causal Discovery

  • Transfer Learning (Domain Adaptation, Few-shot Learning, etc.)

  • Deep Generative Models

  • Applications in Computer Vision, Biomedical Informatics, etc.


Selected Publications (* co-first author)

    subsample
  • M. Gong*, K. Zhang*, B. Schölkopf, D. Tao, and P. Geiger. Discovering Temporal Causal Relations from Subsampled Data. In International Conference on Machine Learning (ICML 2015). [PDF][CODE]

  • M. Gong, K. Zhang, B. Schölkopf, C. Glymour, and D. Tao. Causal Discovery from Temporally Aggregated Time Series. In Conference on Uncertainty in Artificial Intelligence (UAI 2017). [PDF]

  • ctc
  • M. Gong, K. Zhang, T. Liu, D. Tao, C. Glymour, B. Schölkopf. Domain Adaptation with Conditional Transferable Components. In International Conference on Machine Learning (ICML 2016). [PDF][CODE].

  • depth
  • H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao. Deep Ordinal Regression Network for Monocular Depth Estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018). [PDF][Project Page].

  • gcgan
  • H. Fu*, M. Gong*, C. Wang, K. Batmanghelich, K. Zhang and D. Tao. Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). [PDF].