MIT EECS | Hudson River Trading Undergraduate Research and Innovation Scholar
High Performance Subgraph Computations for Anomaly Detection
- Artificial Intelligence and Machine Learning
Efficient and accurate clustering algorithms have a wide range of applications, such as financial time series analysis. In this project, I will implement a framework for dynamically clustering time series data. This project started over the summer, where I improved the performance of a static clustering algorithm, and will continue through this year. I will implement heuristics to extend the static algorithm to dynamic time series and apply the algorithm to real-world data. Additionally, I compare other algorithms in the dynamic framework, and I will attempt to modify the static algorithm to increase performance in a dynamic setting.
I am interested in doing this UROP because I want to gain experience in constructing large projects and getting results. I want to get a better understanding of how to create and implement algorithms.