Collaboration

FIRE Capital One Machine Learning welcomes opportunities to collaboratively work with academic, industry, or government partners on various projects that require the use of machine learning. This program is intended for potential partners seeking FIRE Capital One Machine Learning students to work on a prospective research project. The goal of the collaboration aims to foster research that is mutually beneficial and provides the opportunity for broader impact.

Why partner with FIRE Capital One Machine Learning

The FIRE Capital One Machine Learning research stream was founded with the mission to foster collaboration in prominent research and study areas include: Artificial Intelligence, Big Data, Bioinformatics and Computational Biology, Computer Vision, Data Science, Natural Language Processing, and Cybersecurity. FIRE Capital One Machine Learning research stream provides the unique advantage of having a cohort of more than 30 undergrad students in machine learning that can be utilized for research projects during the Spring, Summer and Fall semesters. Our students do not engage in coursework experiences or demonstrations. Rather, they spearhead authentic research problems, dealing with complex issues using contemporary techniques that are current and used in cutting-edge research groups across the world.

Requirements for Successful Collaboration

An ideal project proposal should:

  • Identify a question or problem
  • Require machine learning development process or approach to answer or solve it
  • Engage students and educators with open, honest, and respectful communication
  • Have the data resources ready for utilization
  • Lead to dissemination of the knowledge or product created

What is the potential timeline of collaboration

A project should ideally span between 2-3 semesters during the summer and fall semesters. Our students receive training during the Spring semester and then transition from training to immersive research during the Spring, Summer, and Fall semesters. A project proposal should ideally be ready before the end of Spring semester.

Image of potential timeline of collaboration

How to collaborate with FIRE Capital One Machine Learning

Please drop Dr. Raymond Tu a line with a short intro of yourself and the project that you would like to collaborate on.