Professional with 10 years of experience in data-driven AI/ML applications in both industry and academia. Proficient in the in the entire digital value chain, and familiar with the entire data science workflow, from problem definition, data acquisition and cleaning, feature engineering, exploratory data analysis and visualization, model selection, training, evaluation, and interpretation, until model deployment, monitoring, and maintenance. Passionate about computer science and mathematics, eager to work on cutting-edge AI applications in the pharmaceutical industry, keen to continuously grow, learn and share.

LinkedIn / Google Scholar / PDF

Selected Professional Experience:

  • Postdoctoral Scientist, Boehringer Ingelheim, Ingelheim, Germany, 10/2023 – Now
  • Machine Learning Research Scientist, Helmholtz Zentrum, Munich, Germany, 07/2019 – 09/2023
  • Data Scientist (working student), BrightCape B.V., Eindhoven, the Netherlands, 03/2017 – 06/2017
  • Machine Learning Engineer (working student), Wikidata, Trento, Italy, 02/2016 – 06/2016
  • Data Engineer, SpazioDati Srl, Trento, Italy, 04/2014 – 06/2016

Education:

  • Ph.D. in Statistics, Ludwig Maximilians Universität, München, Germany, 07/2019 – 09/2023
    • Munich School for Data Science
  • Double Master’s Degree in Data Science, European Institute of Innovation and Technology, 08/2016 – 06/2018
    • Specialization in Distributed Systems and Data Mining, minor Degree in Innovation & Entrepreneurship
    • Computer Science and Engineering, Eindhoven Technical University, Eindhoven, the Netherlands
    • Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, Sweden
  • Bachelor’s Degree in Computer Science, University of Trento, Trento, Italy, 09/2013 – 07/2016

Awards:

  • Merck Research and Innovation Cup 2023
  • IFI – International Research Scholarship awarded by the DAAD (Deutscher Akademischer Austauschdienst)
  • Best Business Plan award at the European Institute of Innovation and Technology (EIT) Digital Summer School
  • Best Pitch & Business Case at the Siemens AI@Industry hackathon
  • Winner of the Siemens Tech for Sustainability hackathon

Publication Highlights:

  • Drost et al., 2024, Predicting T cell receptor functionality against mutant epitopes, Cell Genomics
  • Dorigatti et al. 2023, Frequentist uncertainty quantification in semi-structured neural networks, AISTATS
  • Boniolo et al. 2021, Artificial Intelligence in Early Drug Development enabling Precision Medicine, Expert Opinion on Drug Discovery
  • Fritz et al. 2021, Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany, Nature Scientific Reports
  • Dorigatti et al. 2020, Joint epitope selection and spacer design for string-of-beads vaccines, ECCB (best poster award)