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), gradingTopics: 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, gradingTopics: 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, gradingTopics: 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 |