Pu Ren
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I am a Postdoc Fellow in AI & Learning Systems Group at Lawrence Berkeley National Lab, where I am working with Prof. Michael Mahoney. I received my Ph.D. at Northeastern University in July 2022, where I worked with Prof. Hao Sun and Prof. Ryan Wang. I was also fortunate to collaborate with Prof. Jerome F. Hajjar, Prof. Yang Liu, and Prof. Jian-Xun Wang. I am broadly interested in AI methods for scientific problems. I work on both the methodology and application sides of scientific machine learning. Specifically, my research focuses on soft- and hard-constrained machine learning, generative modeling, and large pretrained models, with applications spanning fluid dynamics, climate science, geophysics, earthquake engineering, etc. More recently, I have been exploring weight-space analysis and its role in scientific machine learning, particularly through loss landscape analyses of constrained models and eigen-spectral analyses of large pretrained models. My ongoing projects aim to establish theoretical and computational foundations that connect these analyses to the generalization and transferability of scientific foundation models. Email: pren AT lbl DOT gov |
Recent News
[03/2026] Our CGM-GM is published online by Nature Communications. We are also building a broader CGM family for geophysical applications: CGM-FAS and CGM-Wave.
[09/2025] Our CGM work is featured at SCEC2025 Plenary Talk.
[07/2024] We are organizing a workshop at NeurIPS 2024 on Foundation Models for Science.
[05/2024] Our work on data-efficient pretraining and in-context learning for SciML has been accepted by NeurIPS 2024. (arXiv)
[01/2024] Our work on generative models for time series data has been accepted by ICLR 2024. (arXiv)
[11/2023] Our work on physics-informed deep learning for seismic wave propagation has been published in the Computer Physics Communications. (DOI) (arXiv)
[08/2023] Our work on physics-informed super-resolution for spatiotemporal data has been published in the Journal of Computational Physics. (DOI) (arXiv)
[06/2023] Our work SuperBench is online now. We build a super-resolution benchmark for scientific machine learning! (arXiv)
[06/2023] Our work on encoding physics to learn reaction-diffusion processes has been accepted by Nature Machine Intelligence. (DOI) (arXiv)
Selected Awards
[05/2021] Second Place of 2021 EMI Dynamics Committee Student Paper Competition (Link)
[05/2020] Third Place of 2020 EMI SHM&C Committee Student Paper Competition (Link)
[09/2019] Vilas Mujumdar Fellowship
[10/2018] China National Scholarship
[04/2015] Finalist of Mathematical Contest in Modeling (top 0.5%)