Javen Shi

Associate Professor Javen Shi

Director Probabilistic Graphical Model Group

School of Computer Science

Faculty of Engineering, Computer and Mathematical Sciences

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


I am leading a machine learning team developing efficient algorithms and systems that can evolve and learn from massive amounts of data in almost any domain, and be capable of reasoning and decision making with super-human performance.

Our systems are very effective at modelling complex relationships between variables. These might be the relationships between symptoms and diseases, or the relationships between a set of sensor inputs and the state of the system being modelled, or the relationships between cellular metabolic reactions and the genes that encode them, or the relationships between users in a social network about whom we wish to draw inferences.

More specifically, my current research projects involve
1) developing core machine learning algorithms and theory in probabilistic graphical models, structured learning, optimisation, and deep learning;
2) developing algorithms and systems for applications ranging from computer vision, social networks, healthcare, smart agriculture, smart manufacturing (Industry 4.0), automated trades etc.

I am leading a machine learning team developing efficient algorithms and systems that can evolve and learn from massive amounts of data in almost any domain, and be capable of reasoning and decision making with super-human performance.

Our systems are very effective at modelling complex relationships between variables. These might be the relationships between symptoms and diseases, or the relationships between a set of sensor inputs and the state of the system being modelled, or the relationships between cellular metabolic reactions and the genes that encode them, or the relationships between users in a social network about whom we wish to draw inferences. 

More specifically, my current research projects involve

  • developing core machine learning algorithms and theory in probabilistic graphical models, structured learning, optimisation, and deep learning; 
  • developing algorithms and systems for applications ranging from computer vision, social networks, healthcare, smart agriculture, smart manufacturing (Industry 4.0), automated trades etc.
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  • Appointments

    Date Position Institution name
    2015 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 Australian Research Council (ARC) funding awarded: $2,300,787. 
    • Lead (1st) Chief Investigator (CI) (2 DPs, 1 DECRA): $1,073,787. 
    • Co-CI (3 LPs): $1,227,000.

ARC Grants

  • 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 

  • Artificial Intelligence (COMP SCI 3007/7059), Semester 1, 2017 (Coordinator and Lecturer)

     

  • Scientific Computing (COMP SCI 1012/1012BR), Semester 1, 2017 (Lecturer)

     

  • Introduction to Statistical Machine Learning (COMP SCI 4401/7401), Semester 2, 2016 (Lecturer)

     

  • Introduction to Geometric Algorithms (COMP SCI 4402/4802/7402), Semester 2, 2016 (Lecturer)

     

  • Scientific Computing (COMP SCI 1012/1012BR), Semester 1, 2016 (Lecturer)

     

  • Introduction to Statistical Machine Learning (COMP SCI 4401/7401), Semester 2, 2015 (Coordinator and Lecturer)

     

  • Artificial Intelligence (COMP SCI 3007/7059), Semester 1, 2015 (Lecturer)

     

  • Scientific Computing (COMP SCI 1012/1012BR), Semester 1, 2015 (Coordinator and Lecturer)

     

  • Introduction to Statistical Machine Learning (COMP SCI 4401/7401), Semester 2, 2014 (Coordinator and Lecturer)

     

  • Introduction to Statistical Machine Learning (COMP SCI 4401/7401), Semester 2, 2013 (Lecturer) 

     

  • Introduction to Statistical Machine Learning (COMP SCI 4401/7401), Semester 2, 2012 (Guest Lecturer) 

     

 

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.

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

    Date Role Research Topic Program Degree Type Student Load Student Name
    2018 Principal Supervisor Sentiment Analysis and Opinion Mining via Generative Adversarial Networks Doctor of Philosophy Doctorate Full Time Mr Amin Parvaneh
    2018 Principal Supervisor Machine Learning and Strategic Reasoning in Financial Market Doctor of Philosophy Doctorate Full Time Mr Xiongren Chen
    2018 Co-Supervisor Application of Deep-Set Networks for Object Detection Doctor of Philosophy Doctorate Full Time Ms Mahsa Ehsanpour
    2017 Principal Supervisor Beyond "IBM WATSON": Enable Machines to Learn and Reason Knowledge Doctor of Philosophy Doctorate Full Time Mr Minming Qian
    2017 Principal Supervisor Social network analysis using statistical machine learning Doctor of Philosophy Doctorate Part Time Mrs Iman Fahmy Shoaib
    2017 Co-Supervisor Exploiting Pervasive Computing for Elderly care Doctor of Philosophy Doctorate Full Time Mr Alireza Abedin Varamin
    2016 Principal Supervisor Markov Logic Networks. Generalisations and Applications Doctor of Philosophy Doctorate Part Time Mr Xian Wang
  • Past Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    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: Director Probabilistic Graphical Model Group
  • Phone: 83130324
  • Email: javen.shi@adelaide.edu.au
  • Fax: 8313 4366
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor 5
  • Room: 5 44
  • Org Unit: School of Computer Science

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