Education: I am a PhD candidate in the Department of Statistics at Carnegie Mellon University (expected graduation date: May 2015). I received a Master’s in Statistics from Carnegie Mellon in May 2011. Before that, I received a Bachelor’s degree from Carnegie Mellon in May 2010, double-majoring in Computational Finance and Statistics.
Research: Below are some research projects I am currently working on or have worked on in the past:
- Large-Scale Classification and Clustering Approaches for Record Linkage
- Clustering with Distributions of Distances
- Identifying Unique Casualties Resulting from the Syrian Civil War with Record Linkage
- Improving NHL Player Ability Ratings with Regularization and Hazard Function Models for Goal Scoring and Prevention
- Predicting Game-by-Game Athlete Performances
- Predicting NBA Game Outcomes
Teaching: I recently taught the courses 36-225 Introduction to Probability and 36-217 Probability Theory and Random Processes. These courses provide an introduction to probability theory and are designed for students in Electrical and Computer Engineering. Topics include elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, limit theorems, Poisson processes, and Markov chains.
For more information about my teaching experiences, please see my CV.
Contact: Please see my CV for contact information.