I am a postdoctoral researcher the High-Dimensional Statistical Modeling Team, led by Makoto Yamada.
I am interested in bridging the gap between advancements in computer science, most prominently machine learning, and research in biology. In particular, but not exclusively, I study the discordance between the genetic causes of a disease, and the small number of genetic factors we can actually associate with it (aka missing heritability). Complex diseases like familial breast cancer or Alzheimer’s disease are studied through genome-wide association studies. I develop procedures that complement data from these studies with prior knowledge of the disease, with the goal of boosting discovery and selecting more robust features. Ultimately, my objective is to recover interpretable markers of disease which are useful for diagnosis, treatment, and to better understand the biology of the disease.
PhD in Bioinformatics, 2020
Paris Sciences & Lettres
MSc in Bioinformatics for Health Sciences, 2014
Pompeu Fabra University
BSc in Biochemistry, 2013
Autonomous University of Barcelona
BSc in Biotechnology, 2012
Autonomous University of Barcelona
CBIO is a joint research team between Mines ParisTech and Institut Curie, devoted to applying machine learning to diagnosis and treatment of cancer. I was supervised by Chloé-Agathe Azencott.
In my thesis, Network-guided genome-wide association studies, I explore existing machine learning algorithms, and develop novel procedures, to better understand the genetics of cancer susceptibility. All those methods leverage on prior biological knowledge, modeled as a graph, to boost discovery.