Publications

Author profiles on Google Scholar and MathSciNet.

Preprints

1. Differentially private random feature model.
   Submitted. With D.Needell, H.Schaeffer, and A.Xue. (arXiv)

2. Cauchy Random Features for Operator Learning in Sobolev Space.
   Submitted. With D.Needell and H.Schaeffer. (arXiv)

3. On the Approximation of Koopman Eigenfunctions with Random Features.
   Submitted. Paper

Publications

1. LR-RaNN: Lipschitz Regularized Randomized Neural Networks for System Identification.
   Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025). Paper

2. Solving partial differential equations with random feature models.
   Communications in Nonlinear Science and Numerical Simulation, 152, 109343, 2026. (doi) (arXiv) (code)

3. Radius of information for two intersected centered hyperellipsoids and implications in Optimal Recovery from inaccurate data.
   With S.Foucart. Journal of Complexity, 83, 101841, 2024. (doi) (arXiv) (code)

4. S-Procedure Relaxation: a Case of Exactness Involving Chebyshev Centers
   With S.Foucart. In Explorations in the Mathematics of Data Science, Birkhäuser, 2024. (doi)

5. Optimal Recovery from Inaccurate Data in Hilbert Spaces: Regularize, but what of the Parameter?
   With S.Foucart, Constructive Approximation, 57, 489–520, 2023. (doi) (arXiv) (code)

6. On the Optimal Recovery of Graph Signals
   With S.Foucart and N.Veldt. In Proceedings of SampTA 2023, New Haven. (doi) (arXiv) (code)

7. Learning from Non-Random Data in Hilbert Spaces: An Optimal Recovery Perspective
   With S.Foucart, S.Shahrampour and Y. Wang, Sampling Theory, Signal Processing and Data Analysis, 20, 5, 2022. (doi) (arXiv) (code)

8. A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
   With Mingrui Liu, Zhenxun Zhuang, and Yunwen Lei. In Neural Information Processing Systems 35, 2022. (NeurIPS 2022) (Proceeding) (arXiv)