Teaching

Below are a few of the more recent courses at Brown with which I have been involved in a teaching role. I especially enjoy teaching in the areas of probability, statistics, and their applications (including random algorithms, data science and machine learning, and probabilistic modeling), though I also have experience working one-on-one with students on other common introductory topics (usually calculus and ODEs) through my time at Brown's Math Resource Center. I also had the opportunity to serve as one of the instructors for the 2015 Brown-ICERM-Kobe Simulation Summer School, a crash course in applications of high-performance computing aimed at first- and second-year graduate students. In addition to my teaching experience, I have served as a mentor to two undergraduate students (Kenneth Peluso and Thabo Samakhoana, both graduating in 2018) through our department's graduate-undergraduate mentorship program.

1650 is a particularly notable course due to its constantly-growing enrollment (now 300+ students per semester, offered both semesters). Because it is the university's most rigorous introductory statistics course, it hosts students from a broad range of backgrounds and disciplines, many of whom have little to no experience in formal mathematics. Problem sessions, held multiple times per week to increase accessibility, are a notable feature of the course design; working directly with students and guiding their thought process is critical to developing independent rather than formulaic problem solvers. In my final two semesters as head TA of 1650, I helped to organize and plan the course for two first-time professors. You can find my most recent student evaluations here.



APMA1650 - Statistical Inference I
(TA Fall 2015 - Spring 2016, HTA Fall 2016 - Spring 2017)

Responsibilities: some lectures, course planning and organization, review sessions, problem sessions, office hours, writing homeworks and exams (and solutions thereof), grading

Topics: probability spaces; conditional probability and independence; discrete and continuous random variables; multivariate distributions, marginals, and conditionals; parameter estimation, bias, and variance; central limit theorem; confidence intervals; hypothesis testing (normal and t distributions)



APMA1690 - Computational Probability and Statistics
(TA Fall 2013)

Responsibilities: review sessions, office hours, writing homework and exam solutions, grading

Topics: random number generation; laws of large numbers; Monte Carlo methods; central limit theorem and convolutions; random walks, recurrence, exit probabilities, and their continuous limit application to PDEs; graphical models, including Gibbs random fields and Bayesian networks; graphical conditionals, marginals, and dynamic programming; dimensionality reduction (including principal and independent component analysis)



APMA2610 - Recent Applications of Probability and Statistics (graduate course)
(TA Spring 2014)

Responsibilities: some lectures, review sessions, office hours, writing homework and exam solutions, grading

Topics: Markov chains and their applications to MCMC computing and hidden Markov models; dependency graphs and Bayesian networks; parameter estimation and the EM algorithm; Kalman and particle filtering; nonparametric statistics ("learning theory"), including consistency, bias/variance tradeoff, and regularization; the Bayesian approach to nonparametrics, including the Dirichlet and other conjugate priors; principle and independent component analysis; Gibbs distributions, maximum entropy, and their connections to large deviations