Mingming Gong


Mingming Gong

Mingming Gong

Lecturer (Assistant Professor) in Data Science
School of Mathematics and Statistics
Melbourne Centre for Data Science
The University of Melbourne

Office: 111 Old Geology Building South, Parkville, VIC, Australia
E-mail: mingming.gong[at]unimelb.edu.au
Phone: +61-3-834-49211
[Google Scholar] [School Homepage]

I am a lecturer in Data Science at the School of Mathematics and Statistics, the University of Melbourne (UoM). I am part of the Melbourne Deep Learning Group. Before joining UoM, I was a postdoctoral research fellow at University of Pittsburgh and Carnegie Mellon University, working with Prof Kayhan Batmanghelich and Prof Kun Zhang. I obtained my PhD from University of Technology Sydney, supervised by Prof Dacheng Tao and Prof Kun Zhang. From 03/2013 - 10/2013, I was a research intern at Max-Planck Institute for Intelligent Systems (Prof Bernhard Schölkopf's lab).

I am always looking for highly-motivated students with strong mathematical background or excellent coding skills. If you are interested in working with me, please send me an email about your interests and background (attaching your CV, transcripts, and any previous research papers). Thanks!

  • The university provides various scholarships for PhD students, more detailed information can be found in the scholarship page. The application dealine for international students is Sep 30 and for domestic students is Oct 31.

  • To UoM Master/undergraduate students. Please be advised that I usually take 2 Master students and 1 undergraduate student each year. Please contact me if you have good GPA (WAM>80) and some coding skills.

Research Interests

My research interests lie in machine learning, artifical intelligence, and data science, especially in causal reasoning, transfer learning, and deep learning. I develop computational methods for causal reasoning from observational data and exploit causal knowledge to build more intelligent machine learning algorithms.

  • Causal Inference

  • Causality for Machine Learning, e.g., Transfer Learning

  • Deep Generative Models

  • Applications in Computer Vision, Bioinformatics, Financial Data Analysis, etc.

Recent Updates

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Research Highlights (* Equal Contribution)

  • M. Gong*, Y. Xu*, C. Li, K. Zhang, and K. Batmanghelich. Twin Auxiliary Classifiers GAN. In Neural Information Processing Systems (NeurIPS 2019). [PDF][CODE]

  • 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][CODE].

  • 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][CODE].