About

I have a Bachelor's in Computer Science and a double Master's degree (from the Technical University of Eindhoven and from the Royal Institute of Technology, Stockholm) in Data Science obtained through the EIT Digital Master's School, as well as a minor degree in Innovation & Entrepreneurship.

My PhD is about incorporating uncertainty in the design of personalized vaccines for cancer, done in collaboration with the Institute of Computational Biology at the Helmholtz Zentrum. It revolves around two central topics: (1) design vaccines through discrete optimization, in particular mixed integer linear (stochastic) programming, and (2) improve the predictions of pivotal quantities, with a particular focus on uncertainty, needed to design vaccines, mainly through deep learning.

I am part of the Deep Learning research group.

Research Interests

• Bayesian Inference
• Uncertainty Quantification
• Bayesian Deep Learning
• Discrete Optimization
• Linear Programming

You Can Find me on

• ResearchGate
• Google Scholar
• ORCID
• GitHub
• LinkedIn
• Twitter

Publications

• E Dorigatti, B Schubert, Joint epitope selection and spacer design for string-of-beads vaccines, (under review) (link)
• J Kahn, E Dorigatti, K Lieret, A Linder, T Kuhr, Selective background Monte Carlo simulation at Belle II, 24th International Conference on Computing in High Energy & Nuclear Physics (CHEP) (proceedings will be published soon)
• E Dorigatti, B Schubert, Graph-Theoretical Formulation of the Generalized Epitope-based Vaccine Design Problem (under review) (link)
• M Fossati, E Dorigatti, C Giuliano, N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation, Semantic Web 9 (4), 413-439 (link)

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 139, first floor

Phone: +49 911 58061 9595

Emilio.Dorigatti [at] stat.uni-muenchen.de