Emilio Dorigatti
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
Teaching
Awards
- Best poster at RECOMB2020
You Can Find me on
Publications
- Dorigatti E, Schubert B (2020) Joint epitope selection and spacer design for string-of-beads vaccines. bioRxiv.
link | pdf. - Dorigatti E, Schubert B (2020) Graph-theoretical formulation of the generalized epitope-based vaccine design problem (RD Kouyos, Ed.). PLOS Computational Biology 16, e1008237.
link | pdf. - Fossati M, Dorigatti E, Giuliano C (2018) N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation (PE Cimiano, Ed.). Semantic Web 9, 413–439.
link | pdf.
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