Web-seminar series in Biostatistics - Università di Milano-Bicocca

Titolo del seminario: Machine learning and high-throughput epidemiological research
Presentatore: Dr. Andrea Ganna Finnish Institute of Public Health, Finland and Broad Institute, USA
Quando: 22 Ottobre 2020, 14:30-15:30
Dove: Zoom, Meeting ID 645 1958 0905, Password:803296
[si prega di spegnere video e microfono quando si accede al meeting. In caso sia necessario potrete attivarli per interagire durante il seminario].

Link to zoom meeting: https://ki-se.zoom.us/j/64519580905?pwd=OHNPWDFmSG1OR1dQTG1RL0c1Qk0zUT09

Abstract: Andrea is an EMBL-group leader at FIMM and an instructor at Harvard Medical School and Massachusetts General Hospital. Previously he did his post-doc at the Analytical and Translation Genetic Unit at Massachusetts General Hospital/Harvard Medical School/Broad Institute and his PhD at Karolinska Institutet. His research interests lie on the intersection between epidemiology, genetics and statistics. Andrea has authored and co-authored both methodological and applied papers focused on leveraging large scale epidemiological datasets to identify novel socio-
demographic, metabolic and genetic markers of common complex diseases. He has extensive expertise in statistical genetics and has been working with large-scale exome and genome sequencing data, focusing on ultra-rare variants in coding and non-coding regions. His research vision is to integrate genetic data and information from electronic health record/national health registries to enhance early detection of common diseases and public health interventions. He will be describing diff erent projects ongoing in his team at the intersection between genetic, machine learning
and epidemiology. Speci fically, he will show 1) how human genetics can be used to infer epidemiological biases beyond what can be done using simply measured variables from questionnaires and 2) how epidemiology can learn from human genetic research to improve data sharing, scalability and move beyond a publication-centered science. Students are invited to ask questions about doing a PhD abroad and research opportunities in genetic epidemiology and machine learning.