Meet Tyler Marrs

Tyler Marrs

What is your personal & professional background?

I started out creating websites at the young age of 12 and started working as a software engineer after earning my bachelor's degree. Falling into genomics research by accident led me down the path of obtaining a master's degree in software engineering and starting a doctoral degree in informatics. However, the doctoral degree was not for me, so I dropped the program and started working as a data scientist. My practical experience working in genomics made it a pretty smooth transition as I was already familiar with distributed algorithms, big data, and statistical programming.

How did you get connected with the Matrix Profile and MPF?

The Knowledge Discovery and Data Mining (KDD) conference in 2017 is where I first learned about the Matrix Profile; Eamonn Keogh and Abdullah Muenn presented. The talk seemed interesting, as the research problems I worked on were mostly time-series specific. At the time, no public repositories existed for the algorithms, so I started implementing my own with intentions on creating an open-source library. However, Andrew beat me to creating a public repository. So instead of creating a new library and competing with Andrew, I started to contribute to his repository instead. We eventually started frequently chatting online and decided to see if other matrix profile related libraries wanted to help establish the "Matrix Profile Foundation." Franz, Andrew, Austin, and I all saw great potential in this venture.

What do you do at MPF?

I'm the primary contributor to the Python library "matrixprofile." Additionally, I oversee technical decisions. We do our best to simplify concepts and keep the library aligned with our mission statement of accessibility. If you want to give me a label, "CTO" would be the correct one to use.

What excites you about the future of Matrix Profile and the MPF?

There is a lot of untapped potentials out there, and I feel that we are just opening the door to everyone. The biggest challenge with very dense research is making it accessible and easily understandable to everyone. I think that we have done a pretty good job at closing that gap.