Advances in data collection technologies in neuroscience has resulted in a deluge of high-quality data that needs to be analyzed, and presented to the experimentalist in a meaningful way. Usually the “data analysis and visualization”-pipeline is built from scratch for each new experiment resulting in a significant amount of code duplication and wasted effort in rebuilding the analysis tools. There is a growing need for a unified system to automate much of the repetitive tasks and aid biologists in understanding their data more efficiently.

The aim of this project is to design such a software system for exploratory data analysis of neuroscience data. The system will include several machine learning based data analysis tools such as clustering and visualization for data exploration. Techniques from anomaly detection and metric learning will be used to identify noisy data and features more effectively.

This is an UNPAID research project.

Faculty Advisor

  • Professor: Nakul Verma
  • Department/School: Computer Science
  • Location: CEPSR 726

Project Timeline

  • Earliest starting date: 3/1/2020
  • End date: 8/15/2020
  • Number of hours per week of research expected during Spring 2020: ~10
  • Number of hours per week of research expected during Summer 2020: ~20

Candidate requirements

  • Skill sets: Strong programming skills in Matlab, good knowledge of Machine Learning
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible