This person is a member of Sanger Institute Alumni.

I study the processes that cause mutations in DNA of normal and cancerous cells. Most fascinating questions are why cancer cells acquire excess of mutations, and how these processes are specific to tissue types. This knowledge will allow us to interpret cancer genomes of individual patients. I bring skills in data science, visualisation and machine learning.

Mutatiations in DNA of cancerous and normal cells are uncovered by next-generation sequencing. I apply data science algorithms and statistical programming to extract the knowledge about causes of the mutations.

I am involved in several projects, all of which involve computational analysis of cancer genomes

  • In SUPPRESSTEM project, I identify the mutations driving colorectal tumours. We will then use this information to predict which patients are likely to respond to a novel therapy.
  • Sequencing whole cancer genomes uncovers the whole new landscape of structural variation. I am studying chromosomal rearrangements, which are another evidence of the mutational processes that operated on the way to breast cancer. Link to BASIS project (
  • In the INSIGNIA project we experimentally try to re-create the patterns of mutations observed in patients’ tumours. I will relate the genomes of the patient tumours to the patterns of experimentally induced mutations.

In my work I enjoy the opportunity to answer one of the fundamental questions about cancer. I am keen to see how data science and machine learning will help us interpret the patients’ tumour. I enjoy public speaking, and explaining our science to the public.

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A team from NYU has worked on an AI project for COVID-19 deterioration prediction for several months. We just released our paper “An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department”