Ph.D, Stanford Univeristy

Ph.D. in ICME, Stanford Univerisity Office: Huang Engineering Center |

I am a Ph.D. student in Institute for Computational and Mathematical Engineering(ICME) at Stanford Univeristy. I am co-advised by Chris Ré and Michael Mahoney. My research interest lies in randomized linear algebra, optimization and machine learning. In particular I am interested in leveraging tools from randomized linear algebra to provide efficient and scalable solutions for large-scale optimization and learning problems. Before joining Stanford, I recieved my B.S. in Mathematics from Fudan University.

**Accelerated Stochastic Power Iteration**,

Christopher De Sa, Bryan He, Ioannis Mitliagkas, Christopher Ré, and Peng Xu*.

arXiv preprint, 2017, [arXiv][code][blog]**Socratic Learning: Correcting Misspecified Generative Models using Discriminative Models**,

Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, and Christopher Ré.

arXiv preprint, 2017. [arXiv]**Sub-sampled Newton Methods with Non-uniform Sampling**,

Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, and Michael W. Mahoney,

Neural Information Processing Systems (NIPS), 2016.[paper][arXiv][code]

Teaching assistant at Stanford University for:

Statistical Learning Theory (CS229T), Winter 2015

Stochastic Methods in Engineering (CME 308), Spring 2015

Linear Algebra with Application to Engineering Computations (CME 200), Autumn 2015

Teaching assistant at PCMI Graduate Summer School 2016: The Mathematics of Data