Using Topological Data Analysis on your BigData

Topological Data Analysis (TDA) is a framework for data analysis and machine learning and represents a breakthrough in how to effectively use geometric and topological information to solve ‘Big Data’ problems. TDA provides meaningful summaries (in a technical sense to be described) and insights into complex data problems. In this talk, Anthony will begin with an overview of TDA and describe the core algorithm that is utilized. This talk will include both the theory and real world problems that have been solved using TDA. After this talk, attendees will understand how the underlying TDA algorithm works and how it improves on existing “classical” data analysis techniques as well as how it provides a framework for many machine learning algorithms and tasks.

Anthony Bak, Senior Data Scientist, Ayasdi (
Prior to coming to Ayasdi, Anthony was at Stanford University where he did a postdoc with Ayasdi co-founder Gunnar Carlsson, working on new methods and applications of Topological Data Analysis. He completed his Ph.D. work in algebraic geometry with applications to string theory at the University of Pennsylvania and ,along the way, he worked at the Max Planck Institute in Germany, Mount Holyoke College in Germany, and the American Institute of Mathematics in California.

Thanks to our Sponsors
Microsoft [ ] for providing awesome venue for the event.

Ayasdi [ ] for providing the food/drinks.

cognizeus [ ] for hosting the event and providing books to give away as raffle.

Originally posted at AnalyticsWeek