Educational Background：

• 2018.8–2022.8: University of Technology Sydney, Faculty of Engineering and Information Technology, Doctoral Degree (Full-time)
• 2016.9–2018.6: Sun Yat-sen University, School of Computer Science, Master’s Degree (Full-time)
• 2012.9–2016.6: Sun Yat-sen University, School of Computer Science, Bachelor’s Degree (Full-time)

Work Experience：

• 2024.12–Present: School of Computer Science, Sun Yat-sen University, Associate Professor
• 2023.5–2024.11: Faculty of Science and Engineering, Macquarie University, Postdoctoral Researcher
• 2022.8–2023.5: Faculty of Engineering and Information Technology, University of Technology Sydney, Postdoctoral Researcher

Zhilin Zhao is a Researcher / Associate Professor at the School of Computer Science, Sun Yat-sen University. He is the principal investigator of the National Overseas Young Talent Program and a National Natural Science Foundation of China (NSFC) General Program. He received his bachelor and master degrees from Sun Yat-sen University, and his Ph.D. degree from the University of Technology Sydney, Australia. He subsequently conducted postdoctoral research at the University of Technology Sydney and Macquarie University. His main research direction is out-of-distribution machine learning, focusing on the reliability and generalization of models under unknown or uncertain environments. His research has been carried out in areas including embodied intelligence, large language models, extreme weather forecasting, and medical-assisted diagnosis. He has published more than ten papers as the first author in top-tier international journals and conferences such as TPAMI, AIJ, and NeurIPS.

[TPAMI] Zhilin Zhao, Longbing Cao, Yixuan Zhang, Kun-Yu Lin, Wei-Shi Zheng. Distilling the Unknown to Unveil Certainty. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.

[TPAMI] Zhilin Zhao, Longbing Cao, Kun-Yu Lin. Supervision Adaptation Balancing In-Distribution Generalization and Out-of-Distribution Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 15743-15758, 2023.  

[TPAMI] Zhilin Zhao, Longbing Cao, Kun-Yu Lin. Revealing the Distributional Vulnerability of Discriminators by Implicit Generators. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 7, pp. 8888-8901, 2022.

[AI] Zhilin Zhao, Longbing Cao, Philip S. Yu. Out-of-distribution Detection by Regaining Lost Clues. Artificial Intelligence，vol. 339, pp. 104275, 2024.

[TMLR] Zhilin Zhao, Longbing Cao. Dual Representation Learning for Out-of-Distribution Detection. Transactions on Machine Learning Research, pp. 1-21, 2023.  

[ML] Zhilin Zhao, Longbing Cao. Weighting Non-IID Batches for Out-of-distribution Detection. Machine Learning, vol. 113, no. 10, pp. 7371-7391, 2024.  

[TNNLS] Zhilin Zhao, Longbing Cao, Kun-Yu Lin. Out-of-Distribution Detection by Cross-Class Vicinity Distribution of In-Distribution Data. IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 10, pp. 13777-13788, 2024.  

[TNNLS] Zhilin Zhao, Longbing Cao, Chang-Dong Wang. Gray Learning from Non-iid Data with Out-of-distribution Samples. IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1396-1409, 2025.

[NeurIPS] Zhilin Zhao，Longbing Cao, Xuhui Fan, Wei-Shi Zheng. Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data. Advances in Neural Information Processing Systems, pp. 1-28, 2024.

[NeurIPS] Zhilin Zhao，Longbing Cao. R-divergence for Estimating Model-oriented Distribution Discrepancy. Advances in Neural Information Processing Systems, pp. 1-19, 2023.

[AAAI] Zhilin Zhao, Longbing Cao, Yuanyu Wan. Mixture of Online and Offline Experts for Non-stationary Time Series. Association for the Advancement of Artificial Intelligence, pp. 1-8，2025.

[IJCAI] Zhilin Zhao, Longbing Cao, Philip S. Yu. Out-of-distribution Detection by Regaining Lost Clues. International Joint Conference on Artificial Intelligence，2025.