Kristina holds a BSc in Biomedical Sciences from King’s College London and is currently working on her PhD at University College London with Alan Lowe and Guillaume Charras. Her PhD research utilizes deep learning to track single cell heterogeneity within non-cancer and cancer cell lines. Kristina’s work in this area was recently accepted to Frontiers in Computer Science: www.frontiersin.org/articles/10.3389/fcomp.2021.734559/abstract
She also recently completed an internship at Microsoft Research Cambridge as an AI research scientist and Forbes Slovensko shortlisted her as a 2021’s under-30 laureate.
In this presentation, Kristina covers the tools she has developed to automatically detect cell nuclei using deep learning, track each nucleus over time, and visualize the complete cell lineage tree for individual cells. These tools allow her capture the cellular state over multiple generations and to examine whether cell cycle durations are inheritable.
More about her work: https://github.com/KristinaUlicna
- Using multiple deep learning models and bTrack algorithms cell segmentation, cell state classification and single cell tracking can be fully automated
- Full single cell lineage tracing is possible and allows questions regarding heritable traits can be examined
- Wild-type MDCK cells have a large range of cell cycle durations and that this range may be maintained across lineages