Dr Javen Shi

Qinfeng Shi
Senior Lecturer
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences

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.

Connect With Me

Dr Javen Shi

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.

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 Amount
2012 - 2014 ARC Discovery Early Career Researcher Award ARC

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

Keywords

Artificial Intelligence, Computer Vision, Knowledge Representation and Machine Learning

Journals

Date Citation
2017 Shinmoto Torres,R, Shi,Q, van den Hengel,A, Ranasinghe,D, 2017, A hierarchical model for recognizing alarming states in a batteryless sensor alarm intervention for preventing falls in older people, Pervasive and Mobile Computing, 40, 1-16 10.1016/j.pmcj.2017.04.002
2017 Yao,R, Shi,Q, Shen,C, Zhang,Y, Van Den Hengel,A, 2017, Part-Based Robust Tracking Using Online Latent Structured Learning, IEEE Transactions on Circuits and Systems for Video Technology, 27, 6, 1235-1248 10.1109/TCSVT.2016.2527358
2016 Zhang,L, Wei,W, Zhang,Y, Shen,C, Van Den Hengel,A, Shi,Q, 2016, Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing, IEEE Transactions on Geoscience and Remote Sensing, 54, 12, 7223-7235 10.1109/TGRS.2016.2598577
2015 Shi,Q, Reid,M, Caetano,T, Van Den Hengel,A, Wang,Z, 2015, A hybrid loss for multiclass and structured prediction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1, 2-12 10.1109/TPAMI.2014.2306414
2015 Yin,M, Gao,J, Lin,Z, Shi,Q, Guo,Y, 2015, Dual graph regularized latent low-rank representation for subspace clustering, IEEE Transactions on Image Processing, 24, 12, 4918-4933 10.1109/TIP.2015.2472277
2015 Li,H, Shen,C, Van Den Hengel,A, Shi,Q, 2015, Worst case linear discriminant analysis as scalable semidefinite feasibility problems, IEEE Transactions on Image Processing, 24, 8, 2382-2392 10.1109/TIP.2015.2401511
2015 Shen,F, Shen,C, Shi,Q, Van Den Hengel,A, Tang,Z, Shen,H, 2015, Hashing on nonlinear manifolds, IEEE Transactions on Image Processing, 24, 6, 1839-1851 10.1109/TIP.2015.2405340
2014 Paisitkriangkrai,S, Shen,C, Shi,Q, van den Hengel,A, 2014, RandomBoost: simplified multiclass boosting through randomization, IEEE Transactions on Neural Networks and Learning Systems, 25, 4, 764-779 10.1109/TNNLS.2013.2281214
2012 Gao,J, Shi,Q, Caetano,T, 2012, Dimensionality reduction via compressive sensing, Pattern Recognition Letters, 33, 9, 1163-1170 10.1016/j.patrec.2012.02.007
2011 Shi,Q, Li,C, Wang,L, Smola,A, 2011, Human action segmentation and recognition using discriminative semi-Markov models, International Journal of Computer Vision, 93, 1, 22-32 10.1007/s11263-010-0384-0
2009 Shi,Q, Petterson,J, Dror,G, Langford,J, Smola,A, Vishwanathan,S, 2009, Hash Kernels for Structured Data, Journal of Machine Learning Research (Print), 10, 2615-2637

Conference Papers

Date Citation
2016 Tan,M, Yan,Y, Wang,L, Van Den Hengel,A, Tsang,I, Shi,Q, 2016, Learning sparse confidence-weighted classifier on very high dimensional data
2016 Zhang,L, Wei,W, Zhang,Y, Shen,C, Van Den Hengel,A, Shi,Q, 2016, Cluster sparsity field for hyperspectral imagery denoising, 14th European Conference on Computer Vision (ECCV), Amsterdam, NETHERLANDS 10.1007/978-3-319-46454-1_38
2016 Rezatofighi,S, Milan,A, Zhang,Z, Shi,Q, Dick,A, Reid,I, 2016, Joint probabilistic data association revisited, IEEE International Conference on Computer Vision, Santiago, CHILE 10.1109/ICCV.2015.349
2016 Rezatofighi,S, Milan,A, Zhang,Z, Shi,Q, Dick,A, Reid,I, 2016, Joint probabilistic matching using m-best solutions, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV 10.1109/CVPR.2016.22
2016 Tan,M, Xiao,S, Gao,J, Xu,D, Van Den Hengel,A, Shi,Q, 2016, Proximal riemannian pursuit for large-scale trace-norm minimization, 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV 10.1109/CVPR.2016.633
2016 Gong,D, Tan,M, Zhang,Y, Van Den Hengel,A, Shi,Q, 2016, Blind image deconvolution by automatic gradient activation, 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV 10.1109/CVPR.2016.202
2016 Zhang,L, Wei,W, Zhang,Y, Li,F, Shen,C, Shi,Q, 2016, Hyperspectral compressive sensing using manifold-structured sparsity prior, IEEE International Conference on Computer Vision, Santiago, CHILE 10.1109/ICCV.2015.405
2016 Zhang,WE, Tan,M, Sheng,QZ, Yao,L, Shi,Q, 2016, Efficient orthogonal non-negative matrix factorization over stiefel manifold, ACM International Conference on Information and Knowledge Management (CIKM '16), Indianapolis, IN, USA 10.1145/2983323.2983761
2016 Zhang,Z, Shi,Q, McAuley,J, Wei,W, Zhang,Y, Van Den Hengel,A, 2016, Pairwise matching through max-weight bipartite belief propagation, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV 10.1109/CVPR.2016.135
2015 Yan,Y, Tan,M, Tsang,I, Yang,Y, Zhang,C, Shi,Q, 2015, Scalable maximum margin matrix factorization by active riemannian subspace search
2015 McAuley,J, Targett,C, Shi,Q, Van Den Hengel,A, 2015, Image-based recommendations on styles and substitutes, 38th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Santiago, CHILE 10.1145/2766462.2767755
2015 Tan,M, Shi,Q, Van Den Hengel,A, Shen,C, Gao,J, Hu,F, Zhang,Z, 2015, Learning graph structure for multi-label image classification via clique generation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA 10.1109/CVPR.2015.7299037
2014 Shinmoto Torres,RL, Ranasinghe,DC, Shi,Q, 2014, Evaluation of Wearable Sensor Tag Data Segmentation Approaches for Real Time Activity Classification in Elderly, International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Tokyo, Japan 10.1007/978-3-319-11569-6_30
2014 Lin,G, Shen,C, Shi,Q, Van Den Hengel,A, Suter,D, 2014, Fast supervised hashing with decision trees for high-dimensional data, IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio 10.1109/CVPR.2014.253
2013 Wang,Z, Shi,Q, Shen,C, Van Den Hengel,A, 2013, Bilinear programming for human activity recognition with unknown MRF graphs, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland 10.1109/CVPR.2013.221
2013 Shen,F, Shen,C, Shi,Q, Van Den Hengel,A, Tang,Z, 2013, Inductive hashing on manifolds, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland 10.1109/CVPR.2013.205
2013 Shinmoto Torres,R, Ranasinghe,D, Shi,Q, Sample,A, 2013, Sensor enabled wearable RFID technology for mitigating the risk of falls near beds, IEEE International Conference on RFID, Penang 10.1109/RFID.2013.6548154
2013 Yao,R, Shi,Q, Shen,C, Zhang,Y, Van Den Hengel,A, 2013, Part-based visual tracking with online latent structural learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland 10.1109/CVPR.2013.306
2012 Shi,Q, Shen,C, Hill,R, Van Den Hengel,A, 2012, Is margin preserved after random projection?, International Conference on Machine Learning (ICML), Edinburgh, Scotland
2012 Yao,R, Shi,Q, Shen,C, Zhang,Y, Van Den Hengel,A, 2012, Robust tracking with weighted online structured learning, European Conference on Computer Vision (ECCV), Florence 10.1007/978-3-642-33712-3_12
2012 Li,X, Shen,C, Shi,Q, Dick,A, Van Den Hengel,A, 2012, Non-sparse linear representations for visual tracking with online reservoir metric learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI 10.1109/CVPR.2012.6247872
2011 Li,H, Shen,C, Shi,Q, 2011, Real-time visual tracking using compressive sensing, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO 10.1109/CVPR.2011.5995483
2011 Shi,Q, Eriksson,A, Van Den Hengel,A, Shen,C, 2011, Is face recognition really a compressive sensing problem?, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA 10.1109/CVPR.2011.5995556
2010 Shi,Q, Li,H, Shen,C, 2010, Rapid face recognition using hashing, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco 10.1109/CVPR.2010.5540001
2009 Shi,Q, Zhou,L, Cheng,L, Schuurmans,D, 2009, Discriminative maximum margin image object categorization with exact inference, International Conference on Image and Graphics (ICIG), Xi'an 10.1109/ICIG.2009.162
2009 Shi,Q, Petterson,J, Dror,G, Langford,J, Smola,A, Strehl,A, Vishwanathan,S, 2009, Hash kernels, International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida USA
2008 Shi,Q, Wang,L, Cheng,L, Smola,A, 2008, Discriminative human action segmentation and recognition using semi-Markov model, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Ankorage, AK 10.1109/CVPR.2008.4587557
2007 Shi,Q, Altun,Y, Smola,A, Vishwanathan,S, 2007, Semi-Markov models for sequence segmentation, Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, Czech Republic

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.

Current Staff

  • Ehsan Abbasnejad (postdoc, Dec. 2015 - present, co-supervision, with A/Prof. Anthony Dick ) on Gaussian processes and deep learning
  • Rui Yao (visiting A/Prof, Sept. 2016 - present) on structured learning, tracking and sensor networks 

Current PhD Students

  • Iman Shoaib (Adelaide, March 2017 - present, with Julian McAuley and Ahmad Mahmoud) on social networks and machine learning 
  • Alireza Abedin Varamin (Adelaide, March 2017 - present, co-supervision, with Damith Ranasinghe) on sensor networks, graphical models and deep learning 
  • Yu Liu (Adelaide, March 2017 - present, co-supervision, with Ian Reid) on tracking and deep learning 
  • Xian Wang (Adelaide, July 2016 - present, with Anton Van Den Hengel) on Markov logic networks and question answering 
  • Jie Yang (Adelaide, March 2016 - present, with Chunhua Shen) on deep learning and image processing 
  • Suwichaya Suwanwimolkul (Adelaide, April 2015 - present, with Damith Ranasinghe) on compressive sensing, graphical models, sensor networks and healthcare
  • Chongyu Liu (Adelaide, April 2014 - present, with Hamid Seyed Rezatofighi) on visual tracking with graphical models 

Past Staff

  • Mingkui Tan (postdoc, 2014-2016, now a full professor in South China University of Technology) on optimisation, deep learning and graphical models 

Past Students

  • Lei Zhang (2015,2016, CSC visiting) on hyperspectral image processing with graphical models 
  • Dong (Edward) Gong (2015,2016, CSC visiting) on image debluring, optimsation, graphical models, and deep learning 
  • Zhen Zhang (2013,2014, CSC visiting) on dual message passing, m-best solutions, m-modes inference, higher-order potentials 
  • Rui Yao (2012, CSC visiting, now an Associate Professor) on visual tracking with structured data 
  • Zhenhua Wang (2011-2014, Adelaide, now a lecturer) on inference with unknown graph structures
Position
Senior Lecturer
Phone
83130324
Fax
8313 4366
Campus
North Terrace
Building
Ingkarni Wardli Building
Room Number
5 44
Org Unit
School of Computer Science

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