Education

• Stanford University (2006-2010)
    Ph.D. in Statistics

• Williams College (2002-2006)
    B.A. in Mathematics and Economics
    Thesis written with Prof. Bernhard Klingenberg on simultaneous confidence interval estimation

• California Institute of Technology (2004-2005)
    1-year Dean’s Exchange Student

Course Highlight

Machine Learning

• STATS 306B: Unsupervised learning (topics)

  • Unsupervised vs supervised learning

  • Clustering methods

  • K-means clustering, K-medoid clustering; choosing the number of clusters. The gap statistic. Silhouette statistic. Prediction strength

  • Agglomerative hierarchical clustering. Application to DNA microarrays

  • Vector quantization, tree-structured VQ

  • Hybrid clustering

  • Gaussian mixtures; the EM algorithm. Model-based clustering

  • Unsupervised problem cast as a supervised problem

  • Self-organizing maps

  • Principal components; principal surfaces

  • Factor analysis. Independent components analysis

  • Multidimensional scaling, ISOMAP, local linear embedding

• STATS 315A: Supervised learning (topics)

  • Gaussian discriminant analysis

  • Naive Bayes

  • Support vector machines

  • Model selection and feature selection

  • Least angle regression and the Lasso

  • SVM path algorithms

  • cross-validation, bootstrap

  • Basic expansions and regularization

  • Fitting curves to data

  • Generalized additive models

• STATS 315B: Tree-based learning methods and ensemble methods (topics)

  • Classification & regression trees (CART)

  • Multivariate adaptive regression splines (MARS)

  • Bagging

  • Boosting and additive trees (MART)

  • Neural networks

  • Prototype & near-neighbor methods

• STATS 315C: Learning from matrix valued data (topics)

  • Biplots and heatmaps

  • Anova models, Rasch models, correspondence analysis

  • Clustering, biclustering, spectral clustering

  • SVD, non-negative matrix factorization, and generalizations

  • PageRank, TrustRank and generalizations

  • Prediction on graphs

  • Tensor methods for three way data

  • Matrix resampling and downsampling

  • Random matrix theory and Tracy-Widom laws

  • Graph based algorithms

• CS 221: Artificial intelligence (topics)

  • Robotics and motion planning

  • Search

  • Constraint satisfaction problems

  • Supervised learning

  • Decision trees

  • Reinforcement learning

  • Computer Vision

  • Bayesian networks

  • Hidden Markov Models

• CS 369M: Algorithms for modern massive data set analysis (topics)

  • Randomized algorithms for matrix problems

  • Data analysis and machine learning uses of matrix computations

  • Algorithmic approaches to graph partitioning problems

  • Novel data-motivated matrix factorizations

  • Relationship to numerical, statistical, large-scale computational issues

Other coursework

• EE 364A: Convex optimization

• STATS 305: Intro to Statistical modeling

• STATS 306A: Discrete data modeling (topics)

  • Discrete distributions: Bernoulli, Binomial, Poisson, Multinomial

  • Related continuous distributions: Beta, Dirichlet

  • Chisquare tests

  • Logistic regression

  • Loglinear models for contingency tables

  • Generalized linear models

  • Bradley-Terry and related models

  • Rasch and related models

  • Predicting ordered and unordered categorical values

• STATS 324: Multivariate analysis

• STATS 362: Monte Carlo sampling

• STATS 352: Spatial Statistics

• Statistical theory (STATS 300A, B, C); Probability theory (STATS 310A, B, C)