Welcome to our BostonML Meetup for July hosted by Indigo. We're pleased to have Brandy Freitas speaking with us about how we can leverage graphs and graph technology in industrial ML applications.
Where: Inidigo, 500 Rutherford Avenue. Charlestown, MA 02129
When: 6:00 (Doors), 6:30 (talk)
- Indigo is located on the second floor of the North Building in Hood Business Park at 500 Rutherford Ave, Charlestown, MA.
- There is plenty of free parking in the front lot outside of the building (attendees will not be towed; no parking pass or car identification is needed), and we are a 5 minute walk from the Sullivan Square T Stop.
- The space capacity is 300. We will, as usual, admit on a first-come, first-serve basis until full
Graph databases have become much more widely popularized in the recent year. By representing highly connected data in a graph, we have access to a host of graph native algorithms that depend on and exploit the relationships between our data. Computing and storing graph metrics can add strong new features to nodes, creating innovative predictors for machine learning. Using algorithms designed for path finding, centrality, community detection, and graph pattern matching, we can begin to rely less on inflexible, subject-driven feature engineering.
In this session, Brandy Freitas will cover the interplay between graph analytics and machine learning, improved feature engineering with graph native algorithms, and outline the current use of graph technology in industry.
Brandy Freitas is a principle data scientist at Pitney Bowes, where she works with clients in a wide variety of industries to develop analytical solutions for their business needs. She is a research physicist-turned-data scientist based in Boston, MA. Her academic research focused primarily on protein structure determination, applying machine learning techniques to single-particle cryoelectron microscopy data. Brandy is a National Science Foundation Graduate Research Fellow and a James Mills Pierce Fellow. She holds undergraduate degrees in physics and chemistry from the Rochester Institute of Technology and did her graduate work in biophysics and computational statistics at Harvard University.