1. Tailin Wu, Max Tegmark. “Toward an Artificial Intelligence Physicist for Unsupervised Learning.” Physical Review E, 2019, 100(3).

★ Spotlight for PRE Machine Learning for Physics. Featured in MIT Technology Review.

2. Tailin Wu*, Takashi Maruyama*, Long Wei*, Tao Zhang*, Yilun Du*, Gianluca Iaccarino, Jure Leskovec. “Compositional Generative Inverse Design”, ICLR 2024.

★ Spotlight.

3. Tailin Wu*, Takashi Maruyama*, Qingqing Zhao*, Gordon Wetzstein, Jure Leskovec. “Learning Controllable Adaptive Simulation for Multi-scale Physics.” ICLR 2023.

★ Notable Top-25%.

4. Tailin Wu, Takashi Maruyama, Jure Leskovec. “Learning to Accelerate Partial Differential Equations via Latent Global Evolution.” NeurIPS 2022.

5. Tailin Wu*, Hongyu Ren*, Pan Li, Jure Leskovec. “Graph Information Bottleneck.” NeurIPS 2020.

6. Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, Jure Leskovec. “ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time.” NeurIPS 2022.

7. Tailin Wu, Ian Fischer. “Phase Transitions for the Information Bottleneck in Representation Learning.” ICLR 2020.

8. Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec. “Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator.” SIGKDD 2022.

9. Tailin Wu, Michael Sun, H.G. Jason Chou, Pranay Reddy Samala, Sithipont Cholsaipant, Sophia Kivelson, Jacqueline Yau, Zhitao Ying, E. Paulo Alves, Jure Leskovec, Frederico Fiuza. “Learning Efficient Hybrid Particle-continuum Representations of Non-equilibrium N-body Systems.”, NeurIPS 2022 AI for Science: Progress and Promises Workshop.

10. Tailin Wu, Thomas Breuel, Michael Skuhersky, Jan Kautz. “Nonlinear Causal Discovery with Minimum Predictive Information Regularization.” ICML 2019 Time Series Workshop.

★ Best Poster Award.

11. Tailin Wu, Ian Fischer, Isaac L.Chuang, Max Tegmark. “Learnability for the Information Bottleneck”, Entropy, 2019, 21(10): 924.

12. Guangwei Si, Tailin Wu, Qi Ouyang, Yuhai Tu. “Pathway-based Mean-field model for Escherichia coli Chemotaxis.” Physical Review Letters, 2012.07, 109(4): 048101-048105.

13. Curtis G. Northcutt*, Tailin Wu*, Isaac L. Chuang. “Learning with Confident Examples. “Rank Pruning for Robust Classification with Noisy Labels.” UAI 2017.

14. Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark. “AI Feynman 2.0: Pareto-optimal Symbolic Regression Exploiting Graph Modularity.” NeurIPS 2020 Oral.