Mingming Gong


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Mingming Gong

Mingming Gong

Senior Lecturer in Statistics (Data Science), ARC DECRA Fellow
School of Mathematics and Statistics
Melbourne Centre for Data Science
The University of Melbourne

Affiliated Associate Professor in Machine Learning
Department of Machine Learning
Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates

Office: 111 Old Geology Building South, Parkville, VIC, Australia
E-mail: mingming.gong[at]unimelb.edu.au
[Google Scholar] [UoM Homepage] [MBZUAI Homepage]

I am a senior lecturer in Statistics (Data Science) at the School of Mathematics and Statistics and Melbourne Centre for Data Science, the University of Melbourne (UoM), and an affilated associate professor of Machine Learning at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). I am part of the UoM CLeaR Group and 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. 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 talented PhD applicants, more detailed information about PhD application can be found in the How-to-Apply page.

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

  • To UoM undergraduate students. I typically supervise one student on the Science Research Project (SCIE30001) each year. You should have good GPA (math and computing courses) and Python coding experience.

  • To visiting scholar/students. Please drop me an email 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, Causal Representation Learning, Causal Fairness, Causal Robustness, Causal Reinforcement Learning

  • Transfer Learning, Out-of-Distribution Generalization, Weakly-Supervised Learning

  • Applications in Computer Vision, Medical Image Analysis, Bioinformatics, Cloud Computing, etc.


Recent Updates

  • 10/2022, congrats to Haopeng and Maggie for their papers accepted to WACV 2022.

  • 09/2022, congrats to Aoqi and Erdun for their papers accepted to NeurIPS 2022. Our collaborative work with the PGM group at University of Adelaide on efficient differentiable DAG learning also got accepted.

  • 08/2022, I was promoted to Senior Lecturer (grateful to my students and collaborators).

  • 08/2022, I will serve as an Area Chair for ICLR 2023.

  • 08/2022, I was selected to join the editorial board of Machine Learning Journal.

  • 07/2022, I was awarded the CCF-Tencent Rhino-Bird Young Faculty Open Research Fund. Thank you Tencent and CCF!

  • 07/2022, congrats to Dongting for his first paper accepted to ECCV 2022. Our collaborative work with Alibaba on Adaptive Sparse Radiance Grid also got accepted.

  • 07/2022, Prof Nengkun Yu gave us a wonderful talk on quantum computing and learning.

  • 06/2022, congrats to Haopeng for his paper accepted to IEEE T-PAMI.

  • 04/2022, I was selected as one of Global Top Young Chinese Scholars in AI by Baidu Scholar 2022.

  • 03/2022, I will serve as an Area Chair for NeurIPS 2022.

  • 03/2022, our collaborative work with Ebay on recommender systems was accepted to SIGIR 2022.

  • 03/2022, five papers (2 on image-to-image translation, 2 on self-supervised learning, and 1 collaborative work with Baidu on few-shot font style transfer) were accepted to CVPR 2022.

  • 01/2022, our paper on statistical/causal fair learning under label noise were accepted to CLeaR 2022.

  • 01/2022, four papers (1 on causal robustness, 1 on causal RL, 2 on weakly-supervised learning) were accepted to ICLR 2022.

  • 01/2022, I will serve as an Area Chair for ICPR 2022.

  • 12/2021, I will serve as an Area Chair for ICML 2022.

  • 11/2021, Our Python package for causal discovery causal-learn was released.

  • 11/2021, I will serve as an Area Chair for UAI 2022.

  • 10/2021, our papers on causal treatment of domain adaptation and label noise learning were accepted to NeurIPS 2021.

  • 07/2021, I will be the Area Chair and Publicity Chair at the 1st conference on Causal Learning and Reasoning (CLeaR).

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