About Me
A picture of me

I am a graduate student in the Computer Science department, doing my Master's degree with a specialization in Data Science.

Within the field of Data Science, I am particularly interested in predictive analytics, basketball analytics, and social media analytics.

In my free time, I enjoy playing basketball, listening to music, and playing (mainly indie) video games.

Coursework

Fall 2014

CSCI 561: Artificial Intelligence

Studied searching algorithms, game-playing, propositional and first order logic, planning, Bayesian networks, neural networks, decision trees, and SVMs.

CSCI 570: Analysis of Algorithms

Studied greedy algorithms, divide and conquer, dynamic programming, graph algorithms, network flow, and NP completeness.

CSCI 585: Database Systems

Studied SQL, database design, spatial databases, query optimization, business intelligence, XML databases, and big data.

Spring 2015

CSCI 571: Web Technologies

To be determined

CSCI 572: Information Retrieval and Web Search Engines

To be determined

INF 552: Machine Learning for Data Informatics

To be determined

Projects

Sports Team Ranking

Using a modified form of Google's PageRank algorithm, a rudimentary power ranking system was created. At the start of the season, all teams have the same number of "fans". When team A beats team B, fans of team B "bandwagon" to team A with a given probability. The team with the most fans at the end is the highest ranked team. Ranking was dependent on the probability chosen.

NBA Player Performance Prediction

Using previous performance data and a player similarity metric, future performance prediction was attempted. Predictions were based on the performance of players similar to the one being looked at. K-means clustering and the cosine distance metric were used to determine player similarity. A number of basic statistics like points and rebounds per game were predicted.

Wine Website

Worked in group of 10 to create a wine inventory and recommendation website for software engineering class. The website was primarily built using Python, Flask, HTML, Javascript, and CSS. Recommendations were made based on wines in the user's inventory and wishlist. The website was voted as the best project in the class, out of 20 total projects.