A list of college level courses I have taken in reverse chronological order.
(TA) denotes courses for which I was a teaching assistant or grader.
Courses taken as a graduate student:
Fall 2015 (Carnegie Mellon University)
- 36-755 Advanced Statistical Theory I - Jiashun Jin
- 36-758 Advanced Data Analysis II - Ann Lee
- 36-788 Topics in High Dimensional Statistics I - Alessandro Rinaldo
- 36-789 Topics in High Dimensional Statistics II - Alessandro Rinaldo
- 10/36-702 Statistical Machine Learning - Larry Wasserman & Ryan Tibshirani
- 10-704 Information Processing and Learning - Akshay Krishnamurthy & Aarti Singh
- 36-757 Advanced Data Analysis I - Valerie Ventura
- 11-745 Foundations of Machine Learning - Yiming Yang
- 36-662 Data Mining (TA) - Max G'Sell
- 10/36-705 Intermediate Statistics - Larry Wasserman
- 10-715 Advanced Introduction to Machine Learning - Eric Xing & Barnabás Póczos
- 11-745 New Methods in Large-Scale Structured Learning - Yiming Yang
- 36-309/749 Experimental Design (TA) - Howard Seltman
Courses taken as an undergraduate:
(*) denotes honors or graduate level courses.
Spring 2014 (Carnegie Mellon University)
- 15-440 Distributed Systems - Dave Andersen & Srinivasan Seshan
- 21-470 Calculus of Variations - Bill Hrusa
- 21-721 Probability* - Dmitry Kramkov
- 21-901 Masters Degree Research* - Advised by Barnabás Póczos
- 10-725/36-725 Convex Optimization*- Barnabás Póczos & Ryan Tibshirani
- 21-740 Functional Analysis II* - Bill Hrusa
- 21-759 Differential Geometry* - Dejan Slepčev
- 21-901 Masters Degree Research* - Advised by Barnabás Póczos
- 82-171 Elementary Japanese I - Angel Huang
- 85-211 Cognitive Psychology - Charles Kemp
- 21-490 Senior Problem Seminar
- 21-373 Algebraic Structures (TA) - Richard Statman
- 15-221 Technical Communication for Computer Scientists - Tom Keating
- 15-423 Digital Signal Processing for Computer Science - Bhiksha Raj
- 15-859 Information Theory and Applications in Theoretical Computer Science* - Venkat Guruswami
- 21-630 Ordinary Differential Equations* - Jack Schaeffer
- 21-640 Introduction to Functional Analysis* - Bill Hrusa
- 36-226 Introduction to Statistical Inference - Jessi Cisewski
- 10-601 Machine Learning* - Tom Mitchell & Ziv Bar-Joseph
- 15-213 Introduction to Computer Systems - Greg Kesden
- 21-621 Introduction to Lebesgure Integration* - David Kinderlehrer
- 21-651 General Topology* - Dejan Slepčev
- 86-595 Neural Data Analysis* - Steve Chase
- 86-712 Computational Neuroscience of Vision* - Tai Sing Lee
- 21-201 Undergraduate Colloquium
- 21-260 Differential Equations - Chris Potter
- 15-359 Probability and Computing - Mor Harchol-Balter & Klaus Sutner
- 15-451 Algorithm Design and Analysis - Gary Miller & Victor Adamchik
- 21-236 Honors Real Analysis II* - Giovanni Leoni
- 21-484 Graph Theory
- 15-251 Great Theoretical Ideas in Computer Science (TA) - Ryan O'Donnell & Danny Sleator
- 15-150 Principles of Functional Programming - Dan Licata
- 21-355 Principles of Real Analysis I - Giovanni Leoni
- 21-373 Honors Algebraic Structures* - Luc Tartar
- 21-600 Mathematical Logic I* - Peter Andrews
- 21-295 Putnam Seminar - Po-Shen Loh
- 21-201 Undergraduate Colloquium
- 15-211 Fundamental Data Structures and Algorithms (TA) - James Morris & Chris Langmead
- 15-123 Effective Programming in C and UNIX - Tim Hoffman
- 15-211 Fundamental Data Structures and Algorithms - Danny Sleator & Margaret Reid-Miller
- 15-251 Great Theoretical Ideas in Computer Science - Luis von Ahn
- 21-132 Honors Analysis II* - Michael Klipper
- 21-301 Combinatorics - Andrzej Dudek
- 18-100 Introduction to Electrical and Computer Engineering - Tom Sullivan
- 76-101 Interpretation and Argument
- 21-127 Concepts of Mathematics - Dana Mihai
- 21-131 Honors Analysis I* - Michael Klipper
- 15-121 Introduction to Data Structures - Dave Feinberg
- 21-295 Putnam Seminar - Po-Shen Loh
- 03-121 Modern Biology - Frederick Lanni
- 73-100 Principles of Economics - Steven Klepper
- 99-102 Computing at Carnegie Mellon