[2023/06]Learning structured representations for accelerating simulation and design, at School of Intelligence Science and Technology, Peking University
[2023/05]Steps toward an AI scientist: neuro-symbolic models for concept generalization and theory learning, at Brown University Autonomous Empirical Research group.
[2022/12]Steps toward an AI scientist: neuro-symbolic models for zero-shot learning of concepts and theories, at BIGAI
[2022/09]Learning to Accelerate Large-Scale Physical Simulations in Fluid and Plasma Physics, at SIAM 2022 Conference on Mathematics of Data Science
[2022/06]Learning to accelerate simulation and inverse optimization of PDEs via latent global evolution, Stanford CS ML lunch
[2021/04]Phase transitions on the tradeoff between prediction and compression in machine learning, Stanford CS ML lunch
[2020/10]Machine learning of physics theories and its universal tradeoff between accuracy and simplicity, at Los Alamos National Lab