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 semi-supervised and/or multiple-instance learning.

I am part of the Methods Beyond Supervised Learning and Probabilistic Machine and Deep Learning research groups, as well as the Translational Immunoinformatics research group at the Institute of Computational Biology, Helmholtz Zentrum.

Research Interests

Teaching

Awards

You Can Find me on

Publications

  1. Dorigatti E, Bischl B, Rügamer D (2023) Frequentist Uncertainty Quantification in Semi-Structured Neural Networks International Conference on Artificial Intelligence and Statistics, PMLR.
  2. Dorigatti E, Bischl B, Schubert B (2022) Improved proteasomal cleavage prediction with positive-unlabeled learning. Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual abs/2209.07527.
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  3. Ziegler I, Ma B, Nie E et al. (2022) What cleaves? Is proteasomal cleavage prediction reaching a ceiling? Extended Abstract presented at the NeurIPS Learning Meaningful Representations of Life (LMRL) workshop 2022.
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  4. Dorigatti E, Goschenhofer J, Schubert B, Rezaei M, Bischl B (2021) Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. arXiv preprint arXiv:2201.13192.
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  5. Rezaei M, Dorigatti E, Rugamer D, Bernd Bischl and (2022) Joint Debiased Representation Learning and Imbalanced Data Clustering IEEE International Conference on Data Mining (ICDM) Deep Learning and Clustering (DLC) Workshop,
  6. Boniolo F, Dorigatti E, Ohnmacht AJ, Saur D, Schubert B, Menden MP (2021) Artificial intelligence in early drug discovery enabling precision medicine. Expert Opinion on Drug Discovery, 1–17.
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  7. Fritz C, Dorigatti E, Rügamer D (2021) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. arXiv:2101.00661 [cs, stat].
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  8. Dorigatti E, Schubert B (2020) Graph-theoretical formulation of the generalized epitope-based vaccine design problem (RD Kouyos, Ed.). PLOS Computational Biology 16, e1008237.
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  9. Dorigatti E, Schubert B (2020) Joint epitope selection and spacer design for string-of-beads vaccines. Bioinformatics 36, i643–i650.
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  10. Kahn J, Dorigatti E, Lieret K, Lindner A, Kuhr T (2020) Selective background Monte Carlo simulation at Belle II. EPJ Web Conf. 245, 02028.
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  11. 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.
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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