Dima is a Master's student in Data Science at the University of Amsterdam with a quantitative background in Econometrics. In collaboration with Amsterdam UMC, she is developing an advanced Computer Vision pipeline to decode drug-induced changes in mitochondrial morphology via the Cell Painting assay—a critical frontier for identifying next-generation oncology treatments.

Applying advanced machine learning techniques, her research stands at the intersection of supervised, self-supervised, and unsupervised learning. She develops multi-paradigm models to extract and cluster drug phenotypes from high-dimensional latent spaces, enabling the discovery of subtle biological patterns.

By leveraging her background in complex quantitative analysis, Dima transforms massive imaging datasets into actionable insights, aiming to revolutionize traditional drug discovery through scalable, data-driven AI frameworks that accelerate the discovery of novel therapeutic mechanisms.