Unsupervised Learning methods in R

Sofie Van Gassen received her M.S. degree in Computer Science from Ghent University in 2013 and her PhD in Computer Science Engineering from Ghent University in 2017. During her PhD she developed machine learning techniques for flow and mass cytometry data. This included the FlowSOM algorithm, a well-known clustering tool for flow cytometry data, which has recently been incorporated in the FlowJo software. She also participated in the FlowCAP IV challenge, where the FloReMi pipeline got the best results in predicting progression time to AIDS for HIV patients. Since 2018, she is an ISAC Marylou Ingram Scholar and as a postdoc she is further extending and improving machine learning techniques for single cell data as a postdoc in the DaMBi group (Center for Inflammation Research).