Senior machine learning research scientist at Imprint Labs, building generative and unsupervised models of the adaptive immune system.
I develop unsupervised and generative learning methods to model immune receptor repertoires and to characterize how the adaptive immune system recognizes pathogens.
Previously a Postdoctoral Scientist at UC Berkeley and a Humboldt Research Fellow at the University of Leipzig, where I studied how bacterial and viral evolution drives immune evasion. I obtained my PhD jointly from ENS Paris and the Max Planck Institute for Dynamics and Self-Organization, Göttingen, on generative modeling of immune receptor repertoires.
How the principle of maximum entropy connects unsupervised learning and density-ratio estimation, and its surprising link to logistic regression.
Read →Calibrating model predictions to data by matching quantiles with boosted decision trees, extended to the multidimensional case.
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