Research work before product: machine learning for multi-omics data at IBM, and experimental therapeutics for lymphoma at Columbia. This is where the biology and the computational side of the later work come from.
IBM Center for Advanced Studies
Graduate Machine Learning Research Intern · Amsterdam · 2016 – 2017
Implemented and benchmarked machine-learning frameworks for identifying disease-causing features in multi-omics data, and evaluated dimensionality reduction techniques, subspace projections in particular, for visualizing single-cell patient data.
Master's Thesis
The research became my MSc thesis in Computational Science at the University of Amsterdam, written in the IBM Center for Advanced Studies under Dr. Shi Yu: Diffuse Intrinsic Pontine Glioma and the Application of Multi-Modal Data Fusion Techniques. It examines methods that fuse DNA and RNA modalities in a data-dependent way, motivated by rare diseases like DIPG where samples are scarce and every modality has to count, and applies them to glioblastoma multiforme and DIPG.
Columbia University Medical Center
Senior Technician, Experimental Therapeutics for Lymphoma · New York · 2013 – 2015
Studied mechanisms of acquired drug resistance in lymphoma using experimental and computational approaches, characterizing via RNA-seq how cell lines escaped HDAC6 inhibition and proteasome blockade. The work contributed to four publications, in Clinical Cancer Research and Blood.