Mingming Gong


Academic Talks

  • Talk on "On the Identifiability of ODEs/SDEs for Causal Inference", International Center for Mathematical Research, Peking University, Beijing, China, Sep 15, 2023

  • Talk on "Opportunities and Challenges in Causal Machine Learning", Southeast University, Nanjing, China, Sep 7 2023

  • Talk on "Overview of AI general concepts and methods", Doherty Institute, Melbourne, Australia, Sep 5, 2023

  • Talk on "Opportunities and Challenges in Causal Machine Learning", Data61, CSIRO, Aug 4, 2023 (online)

  • Talk on "On the Role of Causality in Robust Machine Learning", Souteast University, Dec 1, 2022 (online)

  • Talk on "Domain Adaptation as A Problem of Inference on Graphical Models", Guizhou Normal University, Dec 1, 2022 (online)

  • Talk on "Domain Adaptation as A Problem of Inference on Graphical Models", Melbourne Centre for Data Science Seminar, Melbourne, Australia, Oct 7, 2022

  • Talk on "Deep Learning-based Depth Estimation", Black.ai, Aug 23, 2022 (online)

  • Talk on "Identifiability and Estimation of Causal Structures from Time Series Data", Shanghai Jiaotong University, July 29, 2022 (online)

  • Talk on "Bridging Causality and Machine Learning: How Do They Benefit from Each other?", MBZUAI, July 5, 2022 (online)

  • Talk on "Towards Causal Transfer Learning", Huazhong Agricultural University, Dec 8, 2021 (online)

  • Talk on "Causal Understanding of Domain Adaptation", Huawei Strategy and Technology Workshop, Oct 16, 2021 (online)

  • Talk on "Domain Adaptation as A Problem of Inference on Graphical Models", University of Sydney, Oct 13, 2021 (online)

  • Talk on "Domain Adaptation as A Problem of Inference on Graphical Models", NeurIPS 2020 Australia Pre-Conference, Dec 5, 2020 (online)

  • Talk on "A Tutorial on Depth Estimation", Meituan, Dec 4, 2020 (online)

  • Talk on "Causal Inference from Observational Data", DICTA workshop on Learning and Inference from Complex Data, Dec 3, 2020 (online)

  • Talk on "Domain Adaptation as A Problem of Inference on Graphical Models", 青源Seminar丨NeurIPS 2020中国预讲会, Nov 27, 2020 (online)

  • Talk on "Domain Adaptation as A Problem of Inference on Graphical Models", University of Copenhagen, Denmark, Nov 20, 2020 (online)

  • Talk on "Bridging Causality and Learning: How Do They Benefit from Each Other?", University of Adelaide, Adelaide, Australia, Nov 19, 2020 (online)

  • Talk on "Discovering Temporal Causal Relations from Low-Resolution Data", International Symposium on Data Science Research and Practice, Shiga University, Japan, Nov 13, 2020 (online)

  • Talk on "Causal Generative Modeling for Domain Adaptation", Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia, Oct 30, 2020 (online)

  • Talk on "Bridging Causality and Learning: How Do They Benefit from Each Other?", Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia, Oct 13, 2020 (online)

  • Talk on "Bridging Causality and Learning: How Do They Benefit from Each Other?", Department of Data Science & AI, Monash University, Melbourne, Australia, Oct 7, 2020 (online)

  • Talk on "Deep Generative Models and Transfer Learning", Institute of Computing Technology, Chinese Academy of Science, Beijing, China, Dec 7, 2019

  • Talk on "Causal Discovery and Transfer Learning", International Center for Mathematical Research, Peking University, Beijing, China, Dec 5, 2019

  • Tutorial on "From Statistical to Causal Learning", Australasian Joint Conference on Artificial Intelligence/Australasian Data Mining Conference, Adelaide, Australia, Dec 2, 2019

  • Talk on "Causal Learning", Causal Modeling and Machine Learning Workshop, Guangzhou, China, Nov 23, 2019

  • Talk on "Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping", CVPR, Long Beach, USA, Jun 18, 2019

  • Talk on "Causal and Causally-Inspired Learning", School of Computer Science, University of Science and Technology of China, Hefei, China, April 19, 2019

  • Keynote Speech on "Causal Domain Adaptation", 3rd International Workshop on Big Data Transfer Learning, in conjunction with IEEE Big Data Conference 2018, Seattle, Washington, USA, Dec 10, 2018.

  • Talk on "Causal and Causally-Inspired Learning", School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia, Dec 6, 2018

  • Talk on "Causal and Causally-Inspired Learning", School of Viterbi Engineering, University of Southern California, Los Angeles, USA, Aug 14, 2018

  • Talk on "Causal and Causally-Inspired Learning", Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, USA, Aug 13, 2018

  • Talk on "Domain Adaptation from a Causal Perspective", School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China, July 4, 2018

  • Talk on "Discovering Causal Relations from Low-Resolution Time Series", School of Computer Science, Wuhan University, China, July 3, 2018

  • Talk on "Deep Ordinal Regression Network for Monocular Depth Estimation", CVPR Robust Vision Workshop, Salt Lake, USA, Jun 18, 2018

  • Talk on "Causal and Causally-Inspired Learning", School of Computer Science, Stevens Institute of Technology, Hoboken, USA, Mar 19, 2018

  • Talk on "Domain Adaptation with Conditional Transferable Components", International Conference on Machine Learning (ICML) 2016, New York, USA, Jun 20, 2016.