Professor Javen Shi

• 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

• Research Interests

  Artificial Intelligence Computer Vision Knowledge Representation and Machine Learning

• Journals

  Year	Citation
  2024	Cheng, H., Zhang, M., & Shi, J. Q. (2024). A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-20. DOI
  2024	Cheng, H., Zhang, M., & Shi, J. Q. (2024). Influence Function Based Second-Order Channel Pruning: Evaluating True Loss Changes For Pruning Is Possible Without Retraining. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-15. DOI
  2024	Li, H., Li, X., Wang, P., Zhang, Z., Davey, K., Shi, J. Q., & Qiao, S. -Z. (2024). Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism. Journal of the American Chemical Society, 146(32), 22850-22858. DOI
  2024	Zhang, X., Zhang, Z., Chinnici, A., Sun, Z., Shi, J. Q., Nathan, G. J., & Chin, R. C. (2024). Physics-informed data-driven unsteady Reynolds-averaged Navier-Stokes turbulence modeling for particle-laden jet flows. Physics of Fluids, 36(5), 23 pages. DOI
  2024	Jin, S., Lan, Z., Yang, G., Li, X., Shi, J. Q., Liu, Y., & Zhao, C. X. (2024). Computationally guided design and synthesis of dual-drug loaded polymeric nanoparticles for combination therapy. Aggregate, 10 pages. DOI
  2023	Yan, Q., Gong, D., Wang, P., Zhang, Z., Zhang, Y., & Shi, J. Q. (2023). SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring. IEEE Transactions on Image Processing, 32, 2857-2866. DOI Scopus7 WoS1
  2023	Meng, J., Wang, Z., Ying, K., Zhang, J., Guo, D., Zhang, Z., . . . Chen, S. (2023). Human Interaction Understanding With Consistency-Aware Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 11898-11914. DOI Scopus3 Europe PMC1
  2023	Yan, Q., Liu, S., Xu, S., Dong, C., Li, Z., Shi, J. Q., . . . Dai, D. (2023). 3D Medical image segmentation using parallel transformers. Pattern Recognition, 138, 1-14. DOI Scopus29
  2023	Li, X., Li, H., Zhang, Z., Shi, J. Q., Jiao, Y., & Qiao, S. -Z. (2023). Active-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction. Nano Energy, 115, 108695-1-108695-9. DOI Scopus3
  2023	Yan, Q., Fruzangohar, M., Taylor, J., Gong, D., Walter, J., Norman, A., . . . Coram, T. (2023). Improved genomic prediction using machine learning with Variational Bayesian sparsity. Plant Methods, 19(1), 1-14. DOI
  2023	Li, X., Shi, J. Q., & Page, A. J. (2023). Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning. Nano Letters, 23(21), 9796-9802. DOI Scopus1
  2023	Yuwono, J. A., Li, X., Doležal, T. D., Samin, A. J., Shi, J. Q., Li, Z., & Birbilis, N. (2023). A computational approach for mapping electrochemical activity of multi-principal element alloys. npj Materials Degradation, 7(1), 87-1-87-11. DOI Scopus2
  2023	Zhao, Y., Li, H., Shan, J., Zhang, Z., Li, X., Shi, J. Q., . . . Li, H. (2023). Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis. Journal of Physical Chemistry Letters, 14(49), 11058-11062. DOI Scopus1
  2022	Sun, W., Gong, D., Shi, J. Q., van den Hengel, A., & Zhang, Y. (2022). Video super-resolution via mixed spatial-temporal convolution and selective fusion. Pattern Recognition, 126, 1-14. DOI Scopus11 WoS6
  2022	Yan, Q., Gong, D., Shi, J. Q., den Hengel, A. V., Sun, J., Zhu, Y., & Zhang, Y. (2022). High dynamic range imaging via gradient-aware context aggregation network. Pattern Recognition, 122, 16 pages. DOI Scopus22 WoS12
  2022	Doan, B. G., Abbasnejad, E., Shi, J. Q., & Ranashinghe, D. C. (2022). Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 162, 15 pages.
  2022	Ghiasi, A., Moghaddam, M. K., Ng, C. T., Sheikh, A. H., & Shi, J. Q. (2022). Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network. Engineering Structures, 264, 114474-1-114474-16. DOI Scopus31 WoS5
  2022	Wang, X., Liu, L., & Shi, J. Q. (2022). Computationally Efficient Dilated Convolutional Model for Melody Extraction. IEEE Signal Processing Letters, 29, 1599-1603. DOI Scopus1
  2022	Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2022). Show, price and negotiate: a negotiator with online value look-ahead. IEEE Transactions on Multimedia, 24, 1426-1434. DOI Scopus2
  2021	Abedin, A., Ehsanpour, M., Shi, Q., Rezatofighi, H., & Ranasinghe, D. C. (2021). Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors.. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1), 1-22. DOI Scopus47 WoS26
  2021	Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. V. D., Xie, D., & Zeng, B. (2021). Deep Single Image Deraining via Modeling Haze-Like Effect. IEEE Transactions on Multimedia, 23, 2481-2492. DOI Scopus19 WoS7
  2021	Neshat, M., Nezhad, M. M., Abbasnejad, E., Mirjalili, S., Groppi, D., Heydari, A., . . . Wagner, M. (2021). Wind turbine power output prediction using a new hybrid neuro-evolutionary method. Energy, 229, 120617-1-120617-24. DOI Scopus78 WoS51
  2021	Yan, Q., Wang, B., Zhang, W., Luo, C., Xu, W., Xu, Z., . . . You, Z. (2021). An attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation. IEEE Journal of Biomedical and Health Informatics, 25(7), 2629-2642. DOI Scopus71 WoS43 Europe PMC18
  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. DOI Scopus11 WoS10
  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, 1-16. DOI Scopus22 WoS17 Europe PMC1
  2021	Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., . . . You, Z. (2021). COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations. IEEE Transactions on Big Data, 7(1), 13-24. DOI Scopus78 WoS43 Europe PMC28
  2021	Yan, Q., Gong, D., Shi, J. Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2021). Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, 130(1), 19 pages. DOI Scopus27 WoS9
  2021	Rezatofighi, H., Kaskman, R., Taghizadeh Motlagh, S. F., Shi, Q., Milan, A., Cremers, D., . . . Reid, I. D. (2021). Learn to Predict Sets Using Feed-Forward Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 1-15. DOI Scopus5
  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 Scopus137 WoS39 Europe PMC7
  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 Scopus73 WoS46 Europe PMC2
  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 Scopus3 WoS3
  2020	Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., . . . You, Z. (2020). COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution.
  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. DOI Scopus16 WoS13
  2020	Abbasnejad, M. E., Shi, Q., van den Hengel, A., & Liu, L. (2020). GADE: A Generative Adversarial Approach to Density Estimation and its Applications. International Journal of Computer Vision, 128(10-11), 2731-2743. DOI Scopus2 WoS2
  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, 1-8. DOI Scopus23 WoS17
  2020	Dendorfer, P., Rezatofighi, H. S., Milan, A., Shi, Q. J., Cremers, D., Reid, I. D., . . . Leal-Taixé, L. (2020). MOT20: A benchmark for multi object tracking in crowded scenes. CoRR, abs/2003.09003, 7 pages.
  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. DOI Scopus21 WoS15 Europe PMC3
  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 Scopus13 WoS8 Europe PMC1
  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 Scopus59 WoS44
  2019	Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. V. D., Xie, D., & Zeng, B. (2019). An Effective Two-Branch Model-Based Deep Network for Single Image Deraining.
  2019	Wang, Y., Shi, Q., Abbasnejad, E., Ma, C., Ma, X., & Zeng, B. (2019). Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes.
  2019	Kang, L., Liu, J., Liu, L., Shi, Q., & Ye, D. (2019). Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction.
  2019	Wang, Y., Zhang, H., Liu, Y., Shi, Q., & Zeng, B. (2019). Gradient Information Guided Deraining with A Novel Network and Adversarial Training.
  2019	Liu, Y., Liu, L., Rezatofighi, H., Do, T. -T., Shi, Q., & Reid, I. (2019). Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes.
  2019	Liu, W., Gong, D., Tan, M., Shi, Q., Yang, Y., & Hauptmann, A. G. (2019). Learning Distilled Graph for Large-scale Social Network Data Clustering. IEEE Transactions on Knowledge and Data Engineering, 32(7), 1393-1404. DOI Scopus4 WoS1
  2019	Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., . . . Leal-Taixe, L. (2019). CVPR19 Tracking and Detection Challenge: How crowded can it get?.
  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. DOI Scopus1
  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 Scopus29 WoS26
  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 Scopus19 WoS15 Europe PMC1
  2019	Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D. C., & Qinfeng Shi, J. (2019). An adaptive markov random field for structured compressive sensing. IEEE Transactions on Image Processing, 28(3), 1556-1570. DOI Scopus9 WoS6
  2019	Suwanwimolkul, S., Zhang, L., Ranasinghe, D. C., & Shi, Q. (2019). One-step adaptive Markov random field for structured compressive sensing. Signal Processing, 156, 116-144. DOI Scopus5 WoS2
  2018	Yao, R., Lin, G., Shi, Q., & Ranasinghe, D. C. (2018). Efficient dense labelling of human activity sequences from wearables using fully convolutional networks. Pattern Recognition, 78, 252-266. DOI Scopus75 WoS54
  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 Scopus68 WoS80
  2018	Rezatofighi, S. H., Kaskman, R., Motlagh, F. T., Shi, Q., Cremers, D., Leal-Taixé, L., & Reid, I. (2018). Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks.
  2017	Zhang, L., Wei, W., Shi, Q., Shen, C., Hengel, A. V. D., & Zhang, Y. (2017). Beyond Low Rank: A Data-Adaptive Tensor Completion Method.
  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 Scopus12 WoS10
  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 Scopus18 WoS13
  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 Scopus46 WoS39
  2016	Torres, R. L. S., Ranasinghe, D. C., Shi, Q., & Hengel, A. V. D. (2016). Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields.
  2016	Guo, Y., Chen, J., Du, Q., Hengel, A. V. D., Shi, Q., & Tan, M. (2016). The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs.
  2015	Tan, M., Xiao, S., Gao, J., Xu, D., Hengel, A. V. D., & Shi, Q. (2015). Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient.
  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 Scopus3 WoS2
  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 Scopus9 WoS6
  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 Scopus139 WoS127 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 Scopus120 WoS106 Europe PMC8
  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
  2013	Zhang, Z., Shi, Q., Zhang, Y., Shen, C., & Hengel, A. V. D. (2013). Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations.
  2012	Gao, J., Shi, Q., & Caetano, T. (2012). Dimensionality reduction via compressive sensing. Pattern Recognition Letters, 33(9), 1163-1170. DOI Scopus30 WoS25
  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 Scopus110 WoS75
  2010	Shi, Q., Reid, M. D., & Caetano, T. (2010). Conditional Random Fields and Support Vector Machines: A Hybrid Approach.
  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. Scopus152 WoS94
  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. Scopus3
  -	Li, H., Shen, C., & Shi, Q. (n.d.). Real-time Visual Tracking Using Sparse Representation.

• Conference Items

  Year	Citation
  2018	Bennett, B., Westra, S., Cavagnaro, T., Wheeler, S., Shi, Q., & Pagay, V. (2018). Unpacking Agricultural System Complexities for Improved Outcomes: Taking Advantage of Emerging Data, Technologies and Analysis. Poster session presented at the meeting of AGU Fall Meeting.

• Preprint

  Year	Citation
  2021	Moghaddam, M. K., Abbasnejad, E., Wu, Q., Shi, J., & Hengel, A. V. D. (2021). Learning for Visual Navigation by Imagining the Success.
  2019	Parvaneh, A., Abbasnejad, E., Wu, Q., & Shi, J. (2019). Show, Price and Negotiate: A Hierarchical Attention Recurrent Visual Negotiator..

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
  2024	Co-Supervisor	Improving Machine Learning Models on the Out-Of-Distribution Generalization	Doctor of Philosophy	Doctorate	Full Time	Miss Seyedeh Mahdieh Mirmahdi
  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