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The Research Project


Producing Data Science research papers

We conduct Data Science research and present our findings in the form of academic papers. This entails multifaceted approaches to the problem, including managing a extremely large dataset, building complex machine learning models, performing statistical validation, and analyzing the outcomes.

The Kaggle Project

Competing globally in applied machine learning

Kaggle hosts international machine learning competitions. Thousands of teams compete in each challenge, which can range from predicting housing prices to detecting objects in the sea.

We develop state-of-the-art machine learning algorithms on these applied problems. Members gain valuable hands-on experience with applying theory to practical challenges.


The Algorithmic Trading Project


Applying data-driven approaches to finance

The Algorithmic Trading Project strives to develop algorithmic trading strategies. We want to find a portfolio of stocks and algorithmically buy/sell them in such a way that we maximize our profit while minimizing our risk. We are applying statistical techniques to determine which equities to trade and machine learning techniques to determine when it is appropriate to enter or exit a position.



Reddit Recommendation Engine

Created an engine to generate a set of recommended posts for users of the social website Reddit given prior voting history, previous posts, and comments.


EEG Signal Classification with SVMs

Using data science tools to categorize each stage in the sleep cycle, this team analyzed brain waves emitted between neurons at each stage. By using EEG and electroencephalography, signals were captured and passed through machine learning algorithms for classification.