Brief summary of my teaching experiences

Current Course GSI

Fall 2007:

Past Courses

I have been a GSI for five of the semesters I have spent in Berkeley. Please see below for the courses I have TA'd for. Along with the name of the course I have attached a brief verbal description of the nature of the course and any relevant links.

Stat 21: Summer 2004

Introductory Probability and Statistics for Business -- Statistics
Instructor: Prof Rodney Wong
Verbal Description: Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.
Personal Comments: First course I ever GSI'd for!! Was amazed at the sheer drive of the students to do well.

Stat 205A: Fall 2004

Graduate Course in Probability
Instructor: Prof David Aldous
Verbal Description: Some knowledge of real analysis and metric spaces, including compactness, Riemann integral. Knowledge of Lebesgue integral and/or elementary probability is helpful, but not essential, given otherwise strong mathematical background. Measure theory concepts needed for probability. Expectation, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations; martingales and theory convergence. Markov chains. Stationary processes. Also listed as Mathematics C218A.
Personal Comments: Made me realize how important it is to have a clear mental picture in your mind before you can actually explain things to students.

Also made me realize that the truly great academicians can put a lot of effort into teaching and research at the same time. It will probably take me a long while to master the art of time management but it is always encouraging to know that it can be done!!

Course Webpage	205 A- Fall 04
TA Webpage	205 A- Fall 04

Stat 204A: Fall 2005 and Fall 2006

Graduate Course: Probability for Applications
Instructor: Prof David Aldous
Verbal Description: A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion.
Personal Comments: Why the ability to explain the main idea behind a particular technique in words, is so important.

Stat 2: Spring 2005

Lower Division Course:
Instructor: Prof. Ani Adhikari
Verbal Description: Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.
Personal Comments: This was a lower division course in Statistics. Since students came from diverse backgrounds it was crucial to somehow explain the concepts to everybody and to try to make the Sections interesting. It took me about a week before I finally realized how important class participation is for students to truly learn

Also taught under an amazing Professor. It is difficult to be understanding yet firm, organized yet spontaneous and she managed to do all of the above with aplomb.