| 2025 | ICLR | Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians. | Ishan Amin, Sanjeev Raja, Aditi S. Krishnapriyan |
| 2025 | ICML | Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional. | Sanjeev Raja, Martin Spka, Michael Psenka, Tobias Kreiman, Michal Pavelka, Aditi S. Krishnapriyan |
| 2024 | AISTATS | Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. | Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney |
| 2024 | ICLR | Scaling physics-informed hard constraints with mixture-of-experts. | Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan |
| 2024 | ICLR | Neural Spectral Methods: Self-supervised learning in the spectral domain. | Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan |
| 2024 | ICLR | Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. | Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan |
| 2023 | ICLR | Learning differentiable solvers for systems with hard constraints. | Geoffrey Ngiar, Michael W. Mahoney, Aditi S. Krishnapriyan |
| 2022 | ICML | AutoIP: A United Framework to Integrate Physics into Gaussian Processes. | Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney |