Jason D Lee
Statistics (Statistical Learning and High-dimensional Statistics)
Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form
Jason D Lee, Yuekai Sun and Michael Saunders, NIPS 2012.
Extended version on arXiv. PNOPT package homepage.
Analysis of Inexact Proximal Newton-type Methods
Jason D Lee, Yuekai Sun, and Michael Saunders, NIPS 2012 Optimization and Machine Learning Workshop.
Practical Large Scale Optimization for Max-norm Regularization
Jason D Lee, Benjamin Recht, Ruslan Salakhutdinov, Nati Srebro, and Joel Tropp, NIPS 2010.
Multiscale Dynamic Graphs
Jason D Lee and Mauro Maggioni, Sampling Theory and its Applications 2011.
Multiscale Analysis of Graph Time Series
Jason D Lee and Mauro Maggioni, In Preparation.
Generalized DCell Structure for Load-Balanced Data Center Networks
Markus Kliegl, Jason D Lee, Jun Li, Xinchao Zhang, Chuanxiong Guo, and David Rincon, IEEE Infocom 2010.
Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale SVD
Anna V. Little, Jason D Lee, and Mauro Maggioni, IEEE Statistical Signal Processing Workshop 2009.
Multiscale Estimation of Intrinsic Dimensionality of Point Cloud Data and Multiscale Analysis of Dynamic Graphs
Jason D Lee, Advisor: Mauro Maggioni, Awarded Graduation with High Distinction.
The Generalized DCell Network Structures and Their Graph Properties
Markus Kliegl, Jason Lee, Jun Li, Xinchao Zhang, Chuanxiong Guo, and David Rincon, Microsoft Research Technical Report, October 2009.
Existence of Asymptotic Solutions to Semilinear Partial Difference Equations on Graphs
Jason D Lee and John Neuberger, AMS-MAA Joint Mathematics Meetings 2008.
PhD, Computational and Mathematical Engineering, Stanford University, September 2010-Present.
BS with High Distinction (Magna Cum Laude), Mathematics, Duke University, May 2010.
Lynbrook High School, San Jose, California, June 2006.
Intern at Machine Learning Department, Microsoft Research Redmond, June-September 2012.
Mentor: Ran Gilad-Bachrach
Project: Provable methods for clustering.
Intern at Microsoft Research Redmond, Internet Services Research Center, June-September 2011.
Mentor: Emre Kiciman
Project: Distributed optimization for matrix factorizations.
Intern at Toyota Technology Institute- Chicago, May-September 2010.
Mentor: Nati Srebro
Project: Large-scale optimization for max-norm regularization with applications to collaborative filtering, matrix completion, clustering and inference in Markov random fields.
PRUV Fellow and Senior Thesis Research, Duke University, June 2009-May 2010.
Project: Multiscale estimation of intrinsic dimensionality of point cloud data and multiscale analysis of dynamic graphs.
Intern at Microsoft Research Asia Wireless and Networking Group, May-August 2009.
Mentors:Chuanxiong Guo and David Rincon
Project: Properties of generalized DCell data center network.