Applying topological data analysis and geometry-based ML

 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 UsMargot Gerritsen on LinkedInListen and Subscribe to the WiDS Podcast on Apple Podcasts,Google Podcasts,Spotify,Stitcher