Hi, I’m Elisa
Welcome to my corner of the internet! đź‘‹
I’m interested in applying machine learning methods to solve challenges in the biomedical realm.
My background includes a blend of machine (and deep) learning modelling & bioinformatics, and I develop statistical and ML methods to identify blood-based molecular markers of Parkinson’s disease diagnosis and prognosis for my doctoral studies. I was recently awarded a fellowship to do a research visit in Cambridge, where I focused on exciting graph representation learning models (aka graph ML or geometric DL) with Pietro Liò (Cambridge Univeristy) and Petar VeliÄŤković (Google DeepMind).
Beyond research, I’m a fellow of the 2023 MIT Catalyst fellowhip program, where I explore the space of un-met needs in health(-tech) and I proudly serve as a global shaper recognized by the World Economic Forum of Geneva.
At work and in life, I thrive in collaborative, engaging atmospheres and enjoy sports, from padel tennis to running, with a special fondness for outdoor activities. While at Cambridge, I raced with the triathlon club the Varsity duathlon against Oxford - quite an experience! In my downtime, I enjoy indulging in a good book and the occasional slice of carrot cake with a glass of white wine.
Disclaimer: this site is currently under (active) construction!
Download my resumé.
- Machine learning
- Graphs
- Omics data
- Drug discovery
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PhD in Biomedical Data Science, 2024
University of Luxembourg
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MSc in Bioinformatics, 2018
Universitat Autonoma de Barcelona
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BSc in Biotechnology (computational), 2017
Universidad Politécnica de Madrid
Skills
Experience
I’m part of the Biomedical Data Science group. Integrative machine learning methods for the joint analysis of different types of omics, clinical and imaging data.
Responsibilities include:
- State of the Art research and definition of experiments and project strategy
- Scripting and modelling
- Validation of results
Applied statistics and ML models in a wide range of industries and projects. Forecasting sales, computer vision OCR, statistics for fraud detection, visualization.
Responsibilities include:
- Extraction, preprocessing and analysing data from different formats
- Modelling
- Preparing scripts and models for deployment
Use of SAS to access, manage, analyze and present data. Daily tasks related to SAS developer role, consultant and a migration of data from different database versions (data warehouse).
Responsibilities include:
- Data extraction on SAS
- Database migration
- Scripting on SAS/SAS-SQL
Heuristics for computing hydrophobic properties based on quantum mechanics calculations applied to virtual screening and the alignment of molecules in drug discovery. Built a pipeline x30 times faster without compromising accuracy.
Responsibilities include:
- Research in virtual screening techniques
- Parametrization of hydrophobic descriptors
- Build and validate the parametrized pipeline on external data
AccomplishÂments
Recent Posts
Projects
Sleep-PTSD
Objective sleep data to guide treatment of sleep disturbance in PTSD. Project from MIT-Catalyst program.
Farmelody
Data-driven ‘farm-tech’, our secret weapon is the analysis of microbiome data. Prototype under construction!