Samuele my webpage
Between 2009 and 2014 I have been accomplishing my BSc and MSc in applied mathematics at the University of Genoa, Italy, where I defended a thesis on Hidden Markov Model in Robotics. This was done in collaboration with the Italian Institute of Technology.
I started my PhD in 2015 at the section for statistic of the University of Copenaghen as part of the interdisciplinary project "Genomic History of Denmark" and the group of Population Genetics at the Biocenter.
PhD Student in Statistics, University of Copenhagen (2015 - defense 03/2018)
Interests & Work
I am interested in the mathematical analysis and implementation of statistical methods to analyze different types of data, especially genome data.
As part of my PhD program I have been working on methods and mathematical analysis of 2nd Generation Sequencing Data. I have implemented an extended version of the four-population test that works with low-coverage NGS data and multiple individuals per population (SOFTWARE --- PUBLICATION).
I have been ph.d guest at Imperial College London supervised by Dr. Matteo Fumagalli. Here I worked on the implementation of an HMM to infer ploidy levels.
Powerful Inference with the D-Statistic on Low-Coverage Whole-Genome Data. Samuele Soraggi, Carsten Wiuf and Anders Albrechtsen. G3: GENES, GENOMES, GENETICS.
Selected Talks and Posters
- Probabilistic modeling in genetics. (2015, Cold Spring Harbor, USA). Poster and software presentation: Extended D-statistic and 4-population Test.
- introduction to Hidden Markov Models with discrete states. (2015, Graz Technical University, Graz, Austria). Presentation and tutorial.
- Second Danish Bioinformatics Conference. (2016, Odense, DK). Presentation: Extended D-statistic and 4-population Test.
- Mathematical and Statistical Aspects of Molecular Biology. (2017, Vienna, Austria). Presentation: Extended D-statistic and 4-population Test.
- Third Danish Bioinformatics Conference. (2017, Odense, DK). Presentation and poster: Inference of ploidy levels from sequencing depth and genotype likelihoods.
- Probabilistic modeling in Genetics. (2017, AArhus, DK). Poster: Inference of ploidy levels from sequencing depth and genotype likelihoods.
- Teaching assistant for programming in the Hidden Markov Models course (2015).
- Teaching assistant for programming in the Computational Statistics course (2016 and 2017).
- Statistical inference for partially observed stochastic processes. Modern inferential methods for partially observed stochastic processes, with emphasis on state-space models (also known as Hidden Markov Models).
- State Space Models and particle methods. Sequential algorithms for statistical inference. Emphasis on inference for state space models.
- Analysis of Next Generation Sequencing Data. Sequencing technologies and analysis of the available tools. Final 3-weeks project.
- Washington University Summer School Biology/Statistics. Summer courses on regression models and graphs inference.
- Stochastic Models for Genetic Data. Key points about models used in genetics, comparison to "traditional statistics" and discussion of some methods for inference.