I’m a fourth-year PhD student at Tsinghua University, co-advised by Prof. Yi Zhong at School of Life Sciences and Prof. Jun Zhu at Department of Computer Science and Technology. Before that, I received the BS degree with major in biological science and minor in computer science from Tsinghua University. I was a visiting scholar from Jun 2016 to Sep 2016 in Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, advised by Prof. Yingxi Lin. From Sep 2019 to May 2022, I was a participant in Tsinghua-Huawei Large Granularity Long Term Cooperation Project, working with Dr. Lanqing Hong and Dr. Zhenguo Li.

I have an interdisciplinary background in neuroscience and machine learning. My primary research interest lies in the development of bio-inspired machine learning methodologies and generic computational models for neuroscience. The current focus includes continual / incremental / lifelong learning and transfer learning, by exploring “natural algorithms” in biological learning and memory.

Selected Publication

2023

• Jianjian Zhao, Xuchen Zhang, Bohan Zhao, Liyuan Wang, Wantong Hu, Yi Zhong, Qian Li. Genetic Dissection of Mutual Interference between Two Consecutively Learned Tasks in Drosophila. eLife, 2023, 12:e83516.

• Gengwei Zhang$^{\ast}$, Liyuan Wang$^{\ast}$, Guoliang Kang, Ling Chen, Yunchao Wei. SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model. arXiv preprint arXiv:2303.05118, 2023.

• Liyuan Wang, Xingxing Zhang, Hang Su, Jun Zhu. A Comprehensive Survey of Continual Learning: Theory, Method and Application. arXiv preprint arXiv:2302.00487, 2023.

2022

• Liyuan Wang$^{\ast}$, Xingxing Zhang$^{\ast}$, Qian Li, Jun Zhu, Yi Zhong. CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One. In proc. of European Conference on Computer Vision (ECCV), 2022.

• Xingxing Zhang, Zhizhe Liu, Weikai Yang, Liyuan Wang, Jun Zhu. The More, The Better? Active Silencing of Non-Positive Transfer for Efficient Multi-Domain Few-Shot Classification. In proc. of ACM Multimedia (MM), 2022.

• Liyuan Wang$^{\ast}$, Xingxing Zhang$^{\ast}$, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu. Memory Replay with Data Compression for Continual Learning. In proc. of International Conference on Learning Representations (ICLR), 2022.

2021

• Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong. AFEC: Active Forgetting of Negative Transfer in Continual Learning. In proc. of Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu. ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning. In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

• Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong. Triple-Memory Networks: A Brain-Inspired Method for Continual Learning. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021, 33(5):1925-34.

Academic Service

• Conference Reviewer: NeurIPS, ICLR, CVPR, ICCV, ECCV, ACM MM, CoLLAs
• Journal Reviewer: IEEE TPAMI, IEEE TNNLS, Artificial Intelligence, Neural Networks