geometric aspects of high-dimensional data analysis
scalable algorithms for learning from massive datasets
econometrics and the endogeneity problem
Huang Engineering Center
475 Via Ortega
Stanford, CA 94305
Random projections for non-negative matrix factorization, Anil Damle, Yuekai Sun.
Valid post-selection inference for censored regression problems, Yuekai Sun, Jonathan Taylor.
Exact post-selection inference with the lasso, Jason Lee, Dennis Sun, Yuekai Sun, Jonathan Taylor.
Learning mixtures of linear classifiers, Yuekai Sun, Stratis Ioannidis, Andrea Montanari, ICML 2014, Beijing, China.
On model selection consistency of M-estimators with geometrically decomposable penalties, Jason Lee, Yuekai Sun, Jonathan Taylor. A preliminary version appeared at NIPS 2013.
Proximal Newton-type methods for minimizing composite functions, Jason Lee*, Yuekai Sun*, Michael Saunders, SIAM Journal on Optimization, in press. A preliminary version appeared at NIPS 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, 2011, 27 (16), pp. 10035–10040.
Friction and adhesion properties of vertically aligned multi-walled carbon nanotube arrays and fluoro-nanodiamond films, Hao Lu, Jason Goldman, Feng Ding, Yuekai Sun et al., Carbon, 2008, 46 (10) pp. 1294–1301.
PNOPT, a MATLAB package for minimizing composite functions with proximal Newton-type methods.
Introduction to MATLAB (CME 192), Stanford University, Winter 2014.
ICME Refresher Course, Stanford University, September 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.