Applying topological data analysis and geometry-based ML
Margot Gerittsen speaks with Colleen Farrelly, Mathematician at Post Urban Ventures and Author of The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R. Colleen is known for network science, topological data analysis, and geometry-based machine learning. This podcast covers the challenges and rewards of interdisciplinary work and her journey in the discipline of data science.
Women in Data Science Worldwide · Colleen Farrelly, Professor Margot Gerritsen
Audio is streamed directly from the publisher (dts.podtrac.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
Highlights:
- 00:02:25 - Colleen’s motivation for writing a book, interdisciplinary collaborations, and explaining advanced mathematical tools in accessible ways.
- 00:08:44 - Journey from biology and social sciences to data science, and the integration of different mathematical tools in solving data problems.
- 00:14:13 - Overcoming imposter syndrome and the value of exploring beyond one's field.
- 00:15:02 - The importance of mentorship.
- 00:23:40 - Coping strategies for setbacks in academia and industry.
About the Guest:
Colleen Farrelly is an author and senior data scientist. Her research has focused on network science, topological data analysis, and geometry-based machine learning. She has a master's from the University of Miami and has experience in many fields, including healthcare, biotechnology, nuclear engineering, marketing, and education. Colleen wrote the book, The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R.
Mentions:
Connect with Colleen Farrelly on LinkedIn
Related Links:
The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R
Connect with Us
Margot Gerritsen on LinkedIn
Listen and Subscribe to the WiDS Podcast on Apple Podcasts,Google Podcasts,Spotify,Stitcher