The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R

About Colleen M. FarrellyYae Ulrich Gaba

Colleen M. Farrelly is a senior data scientist whose academic and industry research has focused on topological data analysis quantum machine learning geometry-based machine learning network science hierarchical modeling and natural language processing. Since graduating from the University of Miami with an MS in biostatistics Colleen has worked as a data scientist in a vari- ety of industries including healthcare consumer packaged goods biotech nuclear engineering marketing and education. Colleen often speaks at tech conferences including PyData SAS Global WiDS Data Science Africa and DataScience SALON. When not working Colleen can be found writing haibun/haiga or swimming.Yae Ulrich Gaba completed his doctoral studies at the University of Cape Town (UCT South Africa) with a specialization in topology and is currently a research associate at Quantum Leap Africa (QLA Rwanda). His research interests are computational geometry applied algebraic topology (topologi- cal data analysis) and geometric machine learning (graph and point-cloud representation learning). His current focus lies in geometric methods in data analysis and his work seeks to develop effective and theoretically justified algorithms for data and shape analysis using geometric and topological ideas and methods.