Professor 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.
Professor Javen Shi is the Founding Director of Probabilistic Graphical Model Group at the University of Adelaide, Director in Advanced Releasing and Learning of Australian Institute for Machine Learning (AIML), and the Chief Scientist of Smarter Regions. He is a leader in machine learning in both high-end AI research and also real world applications with high impacts.
He is recognised both locally and internationally for the impact of his work, through an impressive record of publishing in the highest ranked venue in the field of Computer Vision & Pattern Recognition (22 CVPR papers), through an impeccable record of ARC funding (including his DECRA, 2 DP as 1st CI, 4 LP as Co-CI). He has published over 80 peer reviewed papers, over 80% are at ERA [A/A*]. He has over 5000 Google Scholar citations with h-index 31. Google Scholar ranks him 7th globally in Probabilistic Graphical Models, and 3rd globally in Counterfactuals.
He has transferred his research to diverse industries including agriculture, mining, sport, manufacturing, bushfire, water utility, health and education. Recent awards include:
1) 2nd place from a global mining competition OZ Minerals Explorer Challenge 2019 (over 1000 participants from 62 countries);
2) Golden prize (1st place) from Volkswagen in 2019 (digital factory powered by AI);
3) Finalist of SA Department of Energy and Mining’s 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) the top winning team (in collaboration with USC and CSIRO) in AUS/NZ Bushfire Data Quest 2020 using AI to predict fire scar and spread.
He has initiated Smarter Regions CRC bid to empower regional Australia to gain the maximum benefit from the AI revolution and to transform existing industries and grow a technology sector in and for regional Australia.
Date Position Institution name 2020 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 Competency Chinese (Mandarin) Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review
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
Year Citation 2021 Yan, Q., Wang, B., Zhang, L., Zhang, J., You, Z., Shi, Q., & Zhang, Y. (2021). Towards accurate HDR imaging with learning generator constraints. Neurocomputing, 428, 79-91.
2021 Sun, W., Gong, D., Shi, Q., van den Hengel, A., & Zhang, Y. (2021). Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation. IEEE Transactions on Image Processing, 30, 2947-2962.
2020 Yan, Q., Wang, B., Zhang, W., Luo, C., Xu, W., Xu, Z., . . . You, Z. (2020). An Attention-guided Deep Neural Network with Multi-scale Feature Fusion for Liver Vessel Segmentation. IEEE Journal of Biomedical and Health Informatics, PP, 1.
2020 Yan, Q., Wang, B., Li, P., Li, X., Zhang, A., Shi, Q., . . . Zhang, Y. (2020). Ghost Removal via Channel Attention in Exposure Fusion. Computer Vision and Image Understanding, 201, 8 pages.
DOI Scopus2 WoS1
2020 Abedin, A., Ehsanpour, M., Shi, Q., Rezatofighi, H., & Ranasinghe, D. C. (2020). Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors.. CoRR, abs/2007.07172. 2020 Abedin, A., Motlagh, F., Shi, Q., Rezatofighi, H., & Ranasinghe, D. (2020). Towards deep clustering of human activities from wearables. Proceedings - International Symposium on Wearable Computers, ISWC, 1-6.
2020 Guo, Y., Chen, J., Du, Q., Van Den Hengel, A., Shi, Q., & Tan, M. (2020). Multi-way backpropagation for training compact deep neural networks.. Neural Netw, 126, 250-261.
2020 Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2020). Accurate Tensor Completion via Adaptive Low-Rank Representation.. IEEE transactions on neural networks and learning systems, 31(1), 4170-4184.
2020 Yan, Y., Tan, M., Tsang, I., Yang, Y., Shi, Q., & Zhang, C. (2020). Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study. IEEE Transactions on Knowledge and Data Engineering, 32(2), 288-301.
DOI Scopus1 WoS1
2020 Liu, C., Yao, R., Rezatofighi, S. H., Reid, I., & Shi, Q. (2020). Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference. IEEE Transactions on Image Processing, 29, 277-288.
DOI Scopus2 WoS1
2020 Rezatofighi, H., Kaskman, R., Motlagh, F. T., Shi, Q., Milan, A., Cremers, D., . . . Reid, I. D. (2020). Learn to Predict Sets Using Feed-Forward Neural Networks.. CoRR, abs/2001.11845. 2020 Yan, Q., Zhang, L., Liu, Y., Zhu, Y., Sun, J., Shi, Q., & Zhang, Y. (2020). Deep HDR imaging via A non-local network. IEEE Transactions on Image Processing, 29, 4308-4322.
DOI Scopus6 WoS3
2020 Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., & Zhang, Y. (2020). Learning deep gradient descent optimization for image deconvolution. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5468-5482.
DOI Scopus4 WoS1
2019 Guo, Y., Chen, Q., Chen, J., Wu, Q., Shi, Q., & Tan, M. (2019). Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. IEEE Transactions on Multimedia, 21(11), 2726-2737.
DOI Scopus14 WoS6
2019 Liu, W., Gong, D., Tan, M., Shi, Q., Yang, Y., & Hauptmann, A. (2019). Learning Distilled Graph for Large-scale Social Network Data Clustering. IEEE Transactions on Knowledge and Data Engineering, 32(7), 1393-1404.
2019 Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2019). Semantics-Aware Visual Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1687-1700.
DOI Scopus6 WoS4
2019 Gong, D., Tan, M., Shi, Q., van den Hengel, A., & Zhang, Y. (2019). MPTV: matching pursuit based total variation minimization for image deconvolution. IEEE Transactions on Image Processing, 28(4), 1851-1865.
DOI Scopus9 WoS9
2019 Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D., & Qinfeng Shi, J. (2019). An adaptive markov random field for structured compressive sensing. IEEE Transactions on Image Processing, 28(3), 1556-1570.
DOI Scopus4 WoS3
2019 Suwanwimolkul, S., Zhang, L., Ranasinghe, D., & Shi, Q. (2019). One-step adaptive Markov random field for structured compressive sensing. Signal Processing, 156, 116-144.
DOI Scopus4 WoS1
2019 Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Accurate imagery recovery using a multi-observation patch model. Information Sciences, 501, 724-741.
2018 Yao, R., Lin, G., Shi, Q., & Ranasinghe, D. (2018). Efficient dense labelling of human activity sequences from wearables using fully convolutional networks. Pattern Recognition, 78, 252-266.
DOI Scopus26 WoS20
2018 Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., & Shi, Q. (2018). Cluster sparsity field: an internal hyperspectral imagery prior for reconstruction. International Journal of Computer Vision, 126(8), 797-821.
DOI Scopus46 WoS54
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.
DOI Scopus8 WoS8
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.
DOI Scopus10 WoS9
2017 Abbasnejad, M., Shi, Q., Abbasnejad, I., Hengel, A., & Dick, A. (2017). Bayesian Conditional Generative Adverserial Networks.. CoRR, abs/1706.05477. 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.
DOI Scopus35 WoS30
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.
DOI Scopus2 WoS1
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.
DOI Scopus6 WoS5
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.
DOI Scopus111 WoS96 Europe PMC13
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.
DOI Scopus74 WoS63 Europe PMC7
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.
DOI Scopus6 WoS7 Europe PMC1
2012 Gao, J., Shi, Q., & Caetano, T. (2012). Dimensionality reduction via compressive sensing. Pattern Recognition Letters, 33(9), 1163-1170.
DOI Scopus27 WoS23
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.
DOI Scopus93 WoS68
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.
2006 Li, Y., Shi, Q. F., Zhang, Y. N., & Zhao, R. C. (2006). Automatic segmentation for synthetic aperture radar images. Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 28(5), 932-935.
— Li, H., Shen, C., & Shi, Q. (n.d.). Real-time Visual Tracking Using Sparse Representation. — Liu, Y., Liu, L., Rezatofighi, H., Do, T. -T., Shi, Q., & Reid, I. (n.d.). Learning Pairwise Relationship for Multi-object Detection in Crowded
- Total Australian Research Council (ARC) funding awarded: $2.8 M
- Lead (1st) Chief Investigator (CI) (2 DPs, 1 DECRA): $1M
- Co-CI (4 LPs): $1.7M
- Other funding (RDCs, Industry, ...): ~$5M
- 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
- 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
AI, DL, ISML, MBD, ...
Probabilistic Graphical Models
- Representation [ pdf], ACVT, UoA, April 15, 2011
- Inference [ pdf], ACVT, UoA, May 6, 2011
- Learning [ pdf], ACVT, UoA, May 27, 2011
- Sampling-based approximate inference [ pdf], ACVT, UoA, June 10, 2011
- Temporal models [ pdf], ACVT, UoA, August 12, 2011
- Basics [ pdf], ACVT, UoA, April 13, 2012
- VC dimensions and bounds [ pdf], ACVT, UoA, April 27, 2012
- Rademacher complexity and bounds [ pdf], ACVT, UoA, August 17, 2012
- PAC Bayesian Bounds, [ pdf], ACVT, UoA, August 31, 2012
- 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 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 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 2019 Principal Supervisor Machine Learning and Natural Language Processing in Stock Prediction Doctor of Philosophy Doctorate Full Time Mr Jinan Zou 2019 Principal Supervisor Visual Navigation in Embodied Autonomous Agents Doctor of Philosophy Doctorate Full Time Mr Mahdi Kazemi Moghaddam 2018 Principal Supervisor Development of Goal-Oriented Dialogue Systems via Deep Neural Networks Doctor of Philosophy Doctorate Full Time Mr Amin Parvaneh 2018 Co-Supervisor Social Human Activity Recognition and Localization in Video Sequences Doctor of Philosophy Doctorate Full Time Ms Mahsa Ehsanpour 2017 Principal Supervisor Social network analysis using statistical machine learning Doctor of Philosophy Doctorate Part Time Mrs Iman Fahmy Shoaib 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 2017 - 2020 Co-Supervisor Deep Learning Methods for Human Activity Recognition using Wearables Doctor of Philosophy Doctorate Full Time Mr Alireza Abedin Varamin 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
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