PhD Advisees (for whom I was primary advisor)
 Julian Aronowitz
Graduated USC 2020. Thesis: Finite sample bounds in group sequential analysis via Stein's method.
First position: Statistician at Palo Alto Networks
 Xinrui He
Graduated USC 2019. Thesis: Asymptotically optimal sequential multiple testing with (or without) prior information on the number of signals.
First position: Statistician at Acumen LLC/The SPHERE Institute
Current position: Data Scientist at Google
 Mike Hankin
Graduated USC 2017. Thesis: Sequential testing of multiple hypotheses with FDR control.
First position: Statistical Data Scientist at Google
 Jinlin Song
Graduated USC 2013. Thesis: Sequential testing of multiple hypotheses.
First position: Statistician at The Analysis Group
My/our math genealogy can be found here.
Some Recent
Classes Taught at USC
 Fall 2018

 Math 541B  Mathematical Statistics
Second semester of the core graduate mathematical statistics sequence.
Topics: Hypothesis tests and their optimality theory, the
NeymanPearson lemma, generalized likelihood ratios, confidence regions,
asymptotic theory,
jackknife & bootstrap methods, the EM algorithm, and Monte
Carlo simulation methods including Markov chain Monte
Carlo.
 Math 595  Practicum in Teaching the Liberal
Arts: Mathematics
Practical principles for the longterm development of effective teaching
within college disciplines.
 Spring 2017

 Math 542L  Analysis of Variance &
Regression
A graduatelevel introduction to regression and ANOVA models with
statistical computing laboratory. Topics:
Basics of multivariate statistics, ordinary and generalized least
squares estimation in the
linear model, the Ftest, multiple comparisons and confidence intervals,
equivariance and invariance, ridge regression and the lasso, analysis of
variance, random effects models, and applications with statistical
computing.
 Math 500  Graduate Colloquium
 Fall 2016

 Math 307  Statistical Inference and Data Analysis
I
Math 307 (and its companion course, Math 308) provide instruction in
both the mathematical basis for modern statistical techniques as well
as their latest and most important applications. These classes are
also the core of the Statistics Minor offered by the Mathematics
Department. Topics: Probability, counting, independence,
distributions, random variables, simulation, expectation, variance,
covariance, transformations, law of large numbers, Central limit
theorem, estimation, efficiency, maximum likelihood, CramerRao bound,
the bootstrap. Also, statistical computing using the software package R is
featured prominently throughout the course.
 Math 595  Practicum in Teaching the Liberal
Arts: Mathematics
Practical principles for the longterm development of effective teaching
within college disciplines.
 Spring 2016

 Math 308  Statistical Inference and Data Analysis
II
Math 308 (and its companion course, Math 307) provide instruction in
both the mathematical basis for modern statistical techniques as well
as their latest and most important applications. These classes are
also the core of the Statistics Minor offered by the Mathematics
Department. Topics: Confidence intervals, hypothesis testing, pvalues, likelihood ratio, nonparametrics, descriptive statistics, regression, multiple linear regression, experimental design, analysis of variance, categorical data, chisquared tests, Bayesian statistics. Also, statistical computing using the software package R is
featured prominently throughout the course.
 Math 500  Graduate Colloquium
 Fall 2015

 Math 307  Statistical Inference and Data Analysis
I
 Math 595  Practicum in Teaching the Liberal
Arts: Mathematics
 Spring 2015

 Math 542L  Analysis of Variance &
Regression
 Math 545L  Introduction to Time Series
A graduatelevel introduction to the theory and methods of the
analysis of time series data. Topics: Stationary and nonstationary
stochastic processes, trend removal, the multivariate normal distribution,
Hilbert
spaces and the projection theorem, linear regression, autoregressive
moving average (ARMA) models, estimation of mean and autocorrelation
functions via maximum likelihood and least squares, and applications with statistical
computing in R.
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