Mingming Gong


Mingming Gong

Mingming Gong

Lecturer in Statistics (Data Sciences)
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
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I am a lecturer in Data Science at the School of Mathematics and Statistics and a PI at Melbourne Centre for Data Science, 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 co-supervised by Prof Kun Zhang, Master degree from Huazhong University of Science and Technology, and bachelor's degree from Nanjing University. 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 and/or proficient 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!

  • To potential PhD students. Please feel free to contact me if you plan to do research on machine learning/reasoning for your PhD's thesis. The university provides various scholarships for PhD students, more detailed information about PhD application can be found in the How-to-Apply page.

  • To UoM Master students. Please feel free to contact me if you plan to do research on machine learning/reasoning for your Master's thesis. I take students from both statistics and data science.

  • To UoM undergraduate students. I usually supervise 1-2 undergraduates on the Science Research Project (SCIE30001) each year. Please feel free to contact me if you have good GPA (math and computing courses) and Python coding experience.

  • To visiting scholar/students. Please feel free to contact me if your research area overlaps with mine.

Research Interests

I have broad interests in the area of machine learning & reasoning, which is at the heart of data science and artificial intelligence. I am interested in providing theoretical foundations and computational innovations in causal structure learning from real-world complex data. Meanwhile, I explore causal principles to tackle challenges in statistical machine learning, such as transferability, robustness, and interpretability. I am also interested in machine learning/reasoning problems arising from particular areas, such as computer vision and bioinformatics.

  • Causal Discovery & Inference, Causal Representation Learning

  • Transfer Learning, Deep Learning, Weakly-Supervised Learning, Fair Learning, Reinforcement Learning

  • Applications in Computer/Medical Vision, Bioinformatics, Financial Analysis, Robotics, Cloud Computing, etc.

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