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 Institute. 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.

Mari is joining the DSGE Lab as a joint post doc with the Daly lab at FIMM. She’ll be working on integrating genome-wide association studies with single cell gene expression data, taking a particular focus on neuropsychiatric and cognitive traits. For this work, we are also interested in the aspect of gene expression patterns during development.

 Mari completed her PhD at the Wellcome Sanger Institute (UK), in affiliation with University of Cambridge. Her work there explored how combined effects from multiple variants across the genome (polygenic effects) were contributing to presumed single-gene disorders of neurodevelopment. She also investigated the joint role of polygenic and rare variant effects on cognition in the healthy population.

Aoxing completed her Ph.D. in genetics at Aarhus University, Denmark. Her main work there was to explore the genetic architectures of lifetime reproduction in dairy cattle by using large scale of genomic and phenotypic data. She worked intensively in estimation of heritability and genetic correlation, genome-wide association studies, prediction of genetic merit, detection of genotype by environment interaction, and genotype imputation.

After joining the DSGE Lab as a postdoc, she’ll explore the lifetime reproductive success across multiple diseases using Scandinavian health registries. Combing with the results from genome-wide association studies, she will further explore potential signatures of selection.

Sakari is an MD-PhD from the University of Helsinki, who completed a masters in Health Data Science at Harvard T.H. Chan School of Public Health as a Fulbright grantee. He is interested in using large-scale observational data for causal inference and in developing applications of machine learning and clinical informatics for healthcare.  His PhD involved using twin cohorts to examine the metabolic consequences of obesity. In addition to working as a primary care physician and an anesthesiology resident, he has also worked on several clinical research projects related to critical care (including health economic analyses).

At the DSGE Lab he is working with nationwide Finnish healthcare registries to create an atlas of disease associations and to build a framework for discovery-driven causal inference. In addition Sakari is working on Mendelian Randomization studies utilizing FinnGen GWAS data.

Vincent has graduated as an engineer from INSA Lyon with a master's degree in bioinformatics.

After his studies, Vincent worked as software developer and system administrator. He has extensive expertises in cloud computing and database design. He worked at CCIN2P3 data center, helping to migrate CERN computing to the cloud, as part of a h2020-founded project.

He is currently working at FIMM building the Risteys web portal to explore nation-wide disease trajectories and allow exploration of Finngen longitudinal data.

Mattia has graduated in Bioengineering from University of Pavia. For his master’s thesis, he has been a visiting student at Bioinformatics Lab of Prof. Blaž Zupan, University of Ljubljana, working on the development of deep learning models for molecular fingerprinting.

After his graduation, he worked as a research fellow at IRCSS Policlinico San Matteo Foundation, Pavia, focusing on leveraging and integrating hospital data for epidemiology, in particular for the surveillance and prediction of nosocomial infections outbreaks.

He is currently working on applying AI methods to model health trajectories and exploring their interplays with polygenic risk scores.