geometric aspects of statistical inference in high-dimensional settings
low-rank (and more generally structured) matrix approximation
scalable algorithms for learning from massive datasets
Huang Engineering Center
475 Via Ortega
Stanford, CA 94305
Exact inference after model selection with the Lasso, Jason Lee, Dennis Sun, Yuekai Sun, Jonathan Taylor.
Learning mixtures of linear classifiers, Yuekai Sun, Stratis Ioannidis, Andrea Montanari.
On model selection consistency of M-estimators
with geometrically decomposable penalties, Jason Lee, Yuekai Sun, Jonathan
Proximal Newton-type methods for minimizing composite functions, Jason Lee*, Yuekai Sun*, Michael Saunders.
Humidity effects on anisotropic nanofriction behaviors of aligned carbon nanotube carpets, Jiang-nan Zhang, Hao Lu, Yuekai Sun, Lijie Ci, et al., ACS Applied Materials and Interfaces 5 (2013).
Robust flux balance analysis of multiscale biochemical reaction networks, Yuekai Sun, Ronan Fleming, Ines Thiele, Michael Saunders, BMC Bioinformatics 14 (2013). (highly accessed)
Convergence analysis of the inexact proximal Newton method, Jason Lee*, Yuekai Sun*, Michael Saunders, NIPS Workshop on Optmization for Machine Learning, December 2012.
Nanostructure on taro leaves resists fouling by colloids and bacteria under submerged conditions, Jianwei Ma, Yuekai Sun, Karla Gleichauf, Jun Lou, et al., Langmuir 27 (2011).
PNOPT, a MATLAB package for minimizing composite functions
ICCOPT 2013, Lisbon, Portugal.
ICME Linear Algebra and Optimization seminar, March 2013.
ICME Refresher Course, Stanford University, September 2012.
Systems Biology Short Course, University of Iceland, June 2012.
Teaching assistant at Stanford University for:
Linear Algebra with Application to Engineering Computations (CME 200), Autumn 2013.
Large-scale Numerical Optimization (CME 338/MS&E 318), Spring 2012.
B.A. Computational and Applied Mathematics (magna cum laude), Rice University, May 2010.