On 13th March 2020, the Heilbronn Institute welcomes Gunnar Carlsson to the University of Bristol to deliver a Heilbronn Colloquium.
Gunnar Carlsson is an exceptionally distinguished algebraic topologist. He has worked at UCSD (1978-1986), Princeton (1986-1991) and Stanford (1991-present). Professor Carlsson has made a number of important contributions, including, for example, his proof of Segal’s Burnside Conjecture and his proof of Sullivan’s fixed point conjecture. He is also renowned for his work in Topological Data Analysis and Machine Learning, including founding the predictive technology company Ayasdi.
Topology for Machine Learning
Abstract: In recent years, methods of topology have been adapted to the study of large and complex data sets. Topology can provide many useful summaries that describe the shape of the data, or in other cases the shape of individual data points. There are numerous applications within health care, life sciences, finance, etc. In addition, topological methods are particularly suitable for producing “explainable” machine learning and artificial intelligence. I will survey the methods while presenting numerous examples.