Scholar
Alejandro Guerrero-López
Google Scholar ID: z1Cqb6AAAAAJ
University of Zurich
Citations & Impact
Citations
127
H-index
7
i10-index
5
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Released the largest dataset of MALDI-TOF MS of clinically relevant microorganisms, containing 207,950 samples from Hospital General Universitario Gregorio Marañón
- Participated as a professor in the ESCMID Artificial Intelligence and Machine Learning in Medical Microbiology Diagnostics course in Zurich, Switzerland
- Published a preprint performing a systematic review of how ML and DL are used to solve microbiological problems with MALDI-TOF data
Research Experience
- Postdoctoral Researcher at the Institute of Medical Microbiology (IMM), University of Zurich (UZH), in Prof. Dr. Adrian Egli’s group, July 2025 - present
- Visiting Professor at Signal Theory and Communications, UC3M, Madrid, Spain, 2024 - June 2025
- Postdoctoral Researcher at the Bioengineering and Optoelectronics Group, UPM, Madrid, Spain, 2023-2024
- Ph.D. Candidate and Predoctoral Researcher at the Signal Processing Group, UC3M, and Gregorio Marañón Health Research Institute, Madrid, Spain, 2020-2023
- Research Assistant at UGIVIA, UIB, Mallorca, Spain, 2018-2019
Education
- Ph.D. with honors (cum laude) from Universidad Carlos III de Madrid (UC3M), supervised by Prof. Pablo M. Olmos and Prof. Vanessa Gómez-Verdejo, with microbiology guidance from Dr. Belén Rodríguez-Sánchez. Research focus: Bayesian Machine Learning applied to Clinical Microbiology.
- M.Sc. in Information Health Engineering at UC3M, 2019-2020
- B.Sc. in Telematics Engineering at UIB, 2015-2019
Background
- Research Interests: Machine Learning, Bayesian Models, Explainable AI (XAI)
- Fields of Expertise: Application of Machine Learning to Biomedical Problems
- About: Currently a postdoctoral researcher at the Institute of Medical Microbiology (IMM), University of Zurich (UZH), focusing on advancing the field of Machine Learning by developing and understanding novel probabilistic models that address real-world biomedical problems.
Miscellany
- Personal interests and hobbies not mentioned
Co-authors