Javen Shi

Professor Javen Shi

Professor

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.


Professor Javen Qinfeng Shi is the Founding Director of Causal AI Group at the University of Adelaide, and one of the directors at Australian Institute for Machine Learning (AIML). His research interests include causation, AI, mind and metaphysics. Google Scholar ranks him 4th globally in causation and 7th in probabilistic graphical models. He served as a panellist for the Responsible AI Think Tank from 2022 to 2024 and currently holds the position of an AI Industry Forum panellist from 2024 onward, actively contributing to the cultivation of the national and state AI ecosystem. He has transferred his research to diverse industries including material discovery, agriculture, mining, sport, manufacturing, bushfire, health and education.

Recent awards include: 1) 1st place at Open Catalyst Challenge at NeurIPS AI for Science 2023, using AI to discover energy material; 2) Won AUS/NZ Bushfire Data Quest 2020 using AI to predict fire spread, which led to winning a Citizen Science Grant in 2021, and released a bushfire app NOBURN in 2023 (over 50 media coverages); 3) Finalist of SA Department of Energy and Mining Gawler Challenge 2020 (over 2k participants from 100+ countries) with his team's work being considered as ''The most innovative modelling'' by the judge panel; 4) 2nd place in Explorer Challenge 2019 (over 1k entries from 62 countries); 5) 1st place at SAIC Volkswagen Logistics Innovation Day for Smart Manufacturing 2019.

We try to understand and causally influence the underlying distributions and processes to help and serve humanity

Our world is undergoing inevitable and tumultuous changes. Causality, operating beneath the veneer of cause and effect, is essentially the way of change. Our causal AI methods can identify the root causes, discover latent variables, build immunity from spurious correlations, improve generalistion to diverse domains and distribution shifts, model the consequence of interventions, and answer What-If counterfactual questions. More importantly, causal AI holds the key to answer the reverse question: What is the ideal sequence of interventions, given resources or budgets, to optimise future outcomes?

  • Appointments

    Date Position Institution name
    2020 - ongoing Professor University of Adelaide
    2018 - 2019 Associate Professor University of Adelaide
    2015 - 2017 Senior Lecturer University of Adelaide
    2012 - 2014 ARC DECRA fellow University of Adelaide
    2010 - 2011 Senior Research Associate University of Adelaide
  • Awards and Achievements

    Date Type Title Institution Name Country Amount
    2012 Award ARC Discovery Early Career Researcher Award ARC Australia -
  • Language Competencies

    Language Competency
    Chinese (Mandarin) Can read, write, speak, understand spoken and peer review
    English Can read, write, speak, understand spoken and peer review
  • Education

    Date Institution name Country Title
    2006 - 2010 Australian National University, Canberra Australia PhD
    2003 - 2006 Northwestern Polytechnical University, Xi'an China Master
    1999 - 2003 Northwestern Polytechnical University, Xi'an China Bachelor
  • Research Interests

Grants Summary

Total research funding awarded: $19.74M

  • Total Australian Research Council (ARC) funding awarded: $4.34 M 
    • Lead (1st) Chief Investigator (CI) (2 DPs, 1 DECRA): $1M 
    • Co-CI (4 LPs): $3.34M
  • Other funding (RDCs, Industry, ...): ~$15.4M

ARC Grants

  • ARC Discovery Project Grant 2024-2027, 2nd co-Chief Investigator (CI) 
    Learning to Reason in Reinforcement Learning
  • ARC Linkage Grant 2021-2024, 3rd Chief Investigator (CI), and Machine Learning Lead 
    A Machine Learning driven flow modelling of fragmented rocks in cave mining
  • ARC Discovery Project Grant 2016-2019, 1st Chief Investigator (CI) 
    Probabilistic Graphical Models For Interventional Queries
  • ARC Linkage Project Grant 2014-2017, 2nd CI 
    Sentient Buildings
  • ARC Discovery Project Grant 2014-2016, 1st CI 
    Online Learning for Large Scale Structured Data in Complex Situations
  • ARC Linkage Grant 2013-2016, 4th CI 
    Semantic change detection through large-scale learning
  • ARC Linkage Grant 2012-2015, 3rd CI 
    Scalable classification for massive datasets: randomized algorithms
  • ARC DECRA fellowship, 2012-2014, Sole CI 
    Compressive Sensing Based Probabilistic Graphical Models

University Courses 

AI, DL, ISML, MBD, ...

 

 

Tutorials 

Probabilistic Graphical Models

  1. Representation [ pdf], ACVT, UoA, April 15, 2011 

     

  2. Inference [ pdf], ACVT, UoA, May 6, 2011 

     

  3. Learning [ pdf], ACVT, UoA, May 27, 2011 

     

  4. Sampling-based approximate inference [ pdf], ACVT, UoA, June 10, 2011 

     

  5. Temporal models [ pdf], ACVT, UoA, August 12, 2011 

     

Generalisation Bounds

  1. Basics [ pdf], ACVT, UoA, April 13, 2012 

     

  2. VC dimensions and bounds [ pdf], ACVT, UoA, April 27, 2012 

     

  3. Rademacher complexity and bounds [ pdf], ACVT, UoA, August 17, 2012 

     

  4. PAC Bayesian Bounds, [ pdf], ACVT, UoA, August 31, 2012 

     

  5. Regret bounds for online learning, [ pdf], ACVT, UoA, Nov. 2, 2012 

     

Please email me if you find errors or typos in the slides.

  • Current Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2024 Principal Supervisor Causality on Generative Model Doctor of Philosophy Doctorate Full Time Mr Jiaxin Wang
    2023 Principal Supervisor Leveraging Causality for Robust Multi-Source Domain Adaptation Doctor of Philosophy Doctorate Full Time Miss Tianjiao Jiang
    2023 Principal Supervisor Causal Discovery on Videos for Scene Graph Generation Doctor of Philosophy Doctorate Full Time Mr Hamed Damirchi
    2023 Principal Supervisor Domain Adaptation via Causal Representation Learning Doctor of Philosophy Doctorate Full Time Mr Yichao Cai
    2022 Principal Supervisor Causality and Deep Reinforcement Learning on Financial Trading. Doctor of Philosophy Doctorate Full Time Mr Haiyao Cao
    2021 Principal Supervisor Research on lightweight intelligent models based on deep learning: incorporating artificial intelligence on end devices Doctor of Philosophy Doctorate Full Time Ms Hongrong Cheng
    2021 Principal Supervisor Data efficient learning Doctor of Philosophy Doctorate Full Time Mr Yuhao Lin
    2021 Principal Supervisor Recognizing Individual and Collective Activity Using Videos Doctor of Philosophy Doctorate Full Time Mr Bahram Mohammadi
    2020 Principal Supervisor End-To-End semi-supervised text classification with automated text augmentation and character attention Master of Philosophy Master Full Time Mr Adrian John Orenstein
  • Past Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2020 - 2021 Principal Supervisor Connecting Machine Learning to Causal Structure Learning with Jacobian Matrix Master of Philosophy Master Full Time Mr Xiongren Chen
    2019 - 2022 Principal Supervisor Towards Optimistic, Imaginative, and Harmonious Reinforcement Learning in
    Single-Agent and Multi-Agent Environments
    Doctor of Philosophy Doctorate Full Time Mr Mahdi Kazemi Moghaddam
    2019 - 2023 Principal Supervisor Machine Learning and Natural Language Processing in Stock Prediction Doctor of Philosophy Doctorate Full Time Mr Jinan Zou
    2018 - 2022 Principal Supervisor Interactive Vision and Language Learning Doctor of Philosophy Doctorate Full Time Mr Amin Parvaneh
    2017 - 2020 Co-Supervisor Deep Learning Methods for Human Activity Recognition using Wearables Doctor of Philosophy Doctorate Full Time Mr Alireza Abedin Varamin
    2016 - 2023 Principal Supervisor Deep Learning for Multipitch Detection and Melody Extraction Doctor of Philosophy Doctorate Part Time Mr Xian Wang
    2016 - 2021 Principal Supervisor Deep Learning for Image Deblurring and Reflection Removal Doctor of Philosophy Doctorate Full Time Mr Jie Yang
    2015 - 2018 Principal Supervisor Adaptive Markov Random Fields for Structured Compressive Sensing Doctor of Philosophy Doctorate Full Time Miss Suwichaya Suwanwimolkul
    2014 - 2019 Principal Supervisor Joint Appearance and Motion Model for Multi-class Multi-object Tracking Doctor of Philosophy Doctorate Full Time Mr Chongyu Liu
    2014 - 2016 Co-Supervisor Deep Learning for Multi-label Scene Classification Master of Philosophy Master Full Time Mr Junjie Zhang
    2012 - 2014 Co-Supervisor Markov Random Fields with Unknown Heterogeneous Graphs Doctor of Philosophy Doctorate Full Time Mr Zhenhua Wang
  • Position: Professor
  • Phone: 83130324
  • Email: javen.shi@adelaide.edu.au
  • Fax: 83134366
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor 1
  • Org Unit: Computer Science

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