Tian Xie
Postdoc @ MIT CSAIL. Previously, PhD @ MIT DMSE, intern @ DeepMind & Google X
Welcome! I am a postdoc in Artificial Intelligence Laboratory (CSAIL) at MIT. I am co-advised by Tommi Jaakkola and Regina Barzilay.
Research interests: I am broadly interested designing physics motivated machine learning algorithms for solving real-world problems related to material design, drug design, and other engineering domains. I study how domain-specific inductive biases can be incorporated into neural networks to improve generalizability and interpretability. I work closely with chemists, material scientists, and other domain experts to deploy machine learning algorithms to accelerate existing workflows and gain scientific insights.
Previously: I got my PhD in Materials Science and Engineering at MIT in 2020, advised by Jeffrey C. Grossman. I got my BS in Chemistry at Peking University in 2015. I was a research intern at DeepMind, working with Pushmeet Kohli and James Kirkpatrick in 2019/05. I was a research intern at Google X in 2019/02.
news [archive]
| 2021/05 | Our lastest work “Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics is published on Energy & Environmental Science. |
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| 2020/12 | I will present our recent work “Accelerate the screening of complex materials by learning to reduce random and systematic errors” as a Spotlight Talk at the NeurIPS ML4Molecules workshop. |
| 2020/10 | I joined MIT CSAIL as a postdoc. I will be working with Regina Barzilay and Tommi Jaakkola on developing physics motivated ML models for drug and material discovery. |
