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I am scientist interested in engineering effective cancer immunotherapies. I enjoy most working on important and challenging problems, where data are plentiful but where the amount of data makes it difficult to know where to start. I address this challenge by formulating hypotheses, turning these hypotheses into mathematical equations, and implementing these equations into new data science approaches that make best use of the data to solve the problem at hand.

I initially trained in bioinformatics and data science for Masters and PhD, then as a mathematical theorist and finally as a cancer experimentalist during my postdoc, before starting my lab at Karolinska Institute.

Main research achievements include finding mathematical order in the complexity of the fundamentals of gene regulation, and in the coordination of gene programs in tumors. I have attained these achievements by integrating bioinformatics and data science, mathematical modeling, and experimental approaches. I have published 30+ articles which have been cited more than 10000 times, and acquired 4 million euros in research funding.

The lab researches mathematical rules in the molecular tricks that cancer cells use to escape destruction by immune cells. We seek to articulate the molecular chat between immune and cancer cells into equations, to serve as the foundation to engineer personalized cancer immunotherapy. We combine single-cell and spatial tumor profiling experiments, AI & data science, and physics-style mathematical modeling.

• Mathematical modeling in biomedicine. Karolinska Institute (2022-)
• Systems biology. Teaching assistant in Uri Alon's class at Weizmann
  Institute (2014)
• General biology. Teaching assistant in Michael Hall's class at Uni Basel
  (2010)