Professor Javen Qinfeng Shi
Director, Probabilistic Graphical Model Group
Professor, School of Computer Science, University of Adelaide
Director in Advanced Reasoning and Learning, Australian Institute for Machine Learning
Email: javen.shi at adelaide.edu.au
- (May 2022) Congrats to Bao, Ehsan and Damith on ICML 2022 paper Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
- (March 2022) Congrats to Amin etal on CVPR 2022 paper Active Learning by Feature Mixing
- (March 2022) Congrats to Xinyu etal on CVPR 2022 paper Implicit Sample Extension for Unsupervised Person Re-Identification
- (March 2022) Congrats to Qingsen and Dong etal on CVPR 2022 paper Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning
- (July 2021) Check the BBC video (4min) that captures our work using AI+IoT+domain expertise to empower Viticulture.
- (July 2021) Congrats to Dong etal on accepted ICCV 2021 paper Memory-Augmented Dynamic Neural Relational Inference.
- (July 2021) Congrats to Zhenhua etal on accepted ICCV 2021 paper Consistency-Aware Graph Network for Human Interaction Understanding.
- (Jun. 2021) Awarded ~$0.5M Citizen Science grant for our research on flighting bushfires in collaboration with USC. See media release at AIML NEWS and AdelaideNow.
- (Jan. 2021) Awarded $1M for our research on Causality, Trustworthy AI and Bayesian Deep Net. Congrats to Damith, Ehsan and the team. Hiring.
- Awarded $516,000 for our ARC linkage project A Machine Learning driven flow modelling of fragmented rocks in cave mining. Looking for a good postdoc to hire.
- (Oct. 2020) Congrats to AIML's $20M direct investment from the federal gov. to boost advanced reasonsing capabilities in AI.
- (Oct. 2020) Congrats to Amin and Ehsan on NeurIPS 2020 paper Counterfactual Vision-and-Language Navigation: Unravelling the Unseen.
- (Sept. 2020) We entered AUS / NZ 2020 Bushfire Data Quest Challenge. It is great that our technology can help fight the bushfires.
- (Aug. 2020) DeepSightX entered ExploreSA: The Gawler Challenge (over 2200 participates worldwide). Check out the pitch video for our solution using AI to predict ternary domains, pseudo geology and mineral distributions.
- (July 2020) Our Smarter Regions CRC has gone into Round 1 (we have raised $90M cash from industry and we reuqest $71M cash from the federal gov).
- (July 2020) Congrats to Mahsa etal on ECCV 2020 paper Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
- (March 2020) Congrats to Ehsan etal on CVPR 2020 paper Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning
- (March 2020) Congrats to Ehsan etal on CVPR 2020 paper Counterfactual Vision and Language Learning
- (Feb. 2020) Congrats to Chongyu etal on TIP paper Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference
- (Feb. 2020) Congrats to Yan etal on TKDE paper Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study
- (Feb. 2020) Congrats to Qingsen etal on TIP paper Deep HDR Imaging via A Non-local Network
- (Feb. 2020) Congrats to Ali etal on IJCAI paper Sparsesense: Human activity recognition from highly sparse sensor data-streams using set-based neural networks
- (Feb. 2020) Check out our Smarter Regions Cooperative Research Centre (CRC) Bid. The Smarter Regions CRC will empower regional Australia to gain the maximum benefit from the AI revolution. It will transform existing industries and grow a technology sector in and for regional Australia. Our industry cash commitment (before CRC matching) has surpassed $30M. Together with universities and local governments commitments, total cash after matching is expected to be $80M.
- (Sept. 2019) With MIDDOL, we've won Golden Prize (1st place) at SAIC Volkswagen's Logistics Innovation Day with Digital Factory powered by AI! Proud of the team! Great to work with automobile industry.
- (July 2019) Congrats to Zhenhua etal on the ICCV 2019 paper New Convex Relaxations for MRF Inference with Unknown Graphs
- (June 2019) Proud of the team DeepSightX using AI and geoscience to predict the minerals. Within merely 3 months, from zero to exceptional, we've managed to produce tools that make industry domain experts proud. For those who came to my call, stood by me and fought along side of me, you have my gratitude. For those who know me well, know what that means. The trophies match and reflect your attributes and memorable moments in this journey. The 2nd Prize and $200k for the Explorer Challenge is only a prelude for the real journey to begin. [ media release ]
- (May 2019) Congrats to Alireza etal on the IJCAI 2019 paper SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks
- Check out our CVPR 2019 MOTChallenge Workshop on Tracking and Surveillance
- (Feb. 2019) 5 CVPR 2019 papers accepted. Congrats to the team!
- Congrats to Ehsan etal on What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions [pdf]
- Congrats to Ehsan etal on A Generative Adversarial Density Estimator (oral)
- Congrats to Yuhang etal on Variational Bayesian Dropout [pdf]
- Congrats to Qingsen etal on Attention-guided Network for Ghost-free High Dynamic Range Imaging
- Congrats to Jie etal on RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
- (Nov. 2018) We've started a regular Machine Learning Training and Tutorial program.
- (Nov. 2018) Congrats to Qingsen etal on the WACV19 paper Multi-scale Dense Network for Deep High Dynamic Range Imaging.
- (Nov. 2018) Congrats to Yao etal on the PR paper Efficient dense labelling of human activity sequences from wearables using fully convolutional networks.
- (Nov. 2018) Congrats to Zhang etal on the IJCV paper Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction.
- (Nov. 2018) How to turn a two-step adaptive MRF to one-step? Check Suwichaya etal's Signal Processing paper.
- (Nov. 2018) Congrats to Yan etal on the TKDE paper Fast and Low Memory Cost Matrix Factorization.
- (Oct. 2018) Congrats to Suwichaya etal on the TIP paper An Adaptive Markov Random Field for Structured Compressive Sensing.
- (Oct. 2018) Congrats to Dong etal on the TIP paper MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution.
- (Oct. 2018) Want a deep neural net to learn and predict orderless labels/outputs? Checkout our Deep Perm-Set paper v.2 here.
- (Oct. 2018) Congrats to Ali etal on the MobiQuitous18 paper Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables.
- (July 2018) Congrats to Jie etal on the ECCV18 paper Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal.
- (July 2018) Congrats to Yuhang etal on the ECCV18 paper Deblurring Natural Image Using Super-Gaussian Fields.
- (July 2018) Congrats to Ehsan etal on the BMVC18 paper Active Learning from Noisy Tagged Images.
- (Nov. 2017) Want a deep neural net to predict SETs (instead of vectors/matrices/tensors)? Checkout our AAAI18 deep set paper here.
- (July 2017) Congrats to Dong, Mingkui, Yanning, Anton for our ICCV17 deblur paper.
- (July 2017) Check out our tutorial on graphical models on SoCG17 with Chao Chen.
- (April 2017) 1 paper accepted to IJCAI17 on higher order inference for graph matching. Congrats to Zhen, Julian, Yong, Wei and Yanning.
- (Mar. 2017) Congrats to Dong&Jie etal on the CVPR17 deep deblur paper
- (Jan. 2017) 1 Post-doc position now available; Salary: University Level B (AU$94K p.a.); Duration: 2 years (renewable)
- (Nov. 2016) 2 AAAI17 papers accepted (1 Oral, 1 Poster)
- (July 2016) 1 ECCV16 and 1 CIKM16 papers accepted (1 Poster, 1 Oral)
- (Mar. 2016) 4 CVPR16 papers accepted (1 Oral, 3 Posters)
ResearchI 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.
Consulting?Yes, we do consulting too.
Professor Javen Shi is the Founding Director of Probabilistic Graphical Model Group at the University of Adelaide, Director in Advanced Reasoning 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 (25 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 90 peer reviewed papers, over 80% are at ERA [A/A*]. He has over 6500 Google Scholar citations with h-index 33. Google Scholar ranks him 6th globally in Probabilistic Graphical Models.
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, and 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 that led to their winning $0.5M Citizen Science Grant in 2021.