Curriculum Vitae

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Ruggero Cortini, PhD

Data Scientist · Computational Biology & Bioinformatics · NLP for Life Sciences

ruggero.cortini@gmail.com · +34 652 02 65 67 · Barcelona, Spain linkedin.com/in/ruggerocortini · github.com/rcortini

Profile

Data scientist and consultant with 5+ years of experience applying NLP, machine learning, and advanced analytics to life sciences and biomedical research challenges. Built and delivered scalable pipelines for mining scientific literature, patents, and clinical trial data at scale. Deep domain expertise in computational biology and biophysics (PhD from Imperial College London, postdoctoral research at CRG Barcelona), with hands-on experience analysing omics datasets (single-cell RNA-seq, ChIP-seq), molecular simulations, and heterogeneous biomedical data sources. Proven track record of end-to-end consulting project delivery for international clients, translating complex scientific data into actionable insights through dashboards, visualisations, and evidence-based reports. Experienced in client engagement, cross-functional team leadership, and mentoring.

Experience

Data Scientist & Consultant — SIRIS Academic

Oct 2019 – Apr 2025 Barcelona-based consulting firm delivering data-driven strategy for research institutions, public bodies, and industry partners

  • Led 10+ cross-functional consulting projects for international clients (Google DeepMind, Sorbonne Université, Generalitat de Catalunya), developing NLP and AI-powered decision-support tools; scoped projects, developed analytical strategies, and delivered evidence-based recommendations.
  • Designed and deployed scalable Python pipelines for NLP analysis of biomedical and scientific corpora (tens of millions of documents: publications, patents, clinical trials), performing entity extraction, document classification, topic modelling, and semantic search to map research strengths and emerging therapeutic areas.
  • Integrated heterogeneous data sources (publications, patents, clinical trials, project databases) into coherent analytical frameworks using ETL pipelines, data normalisation, and deduplication techniques, producing interactive dashboards and reports.
  • Applied ML and statistical methods (classification, clustering, topic modelling, dimensionality reduction) in Python and R to validate results, assess model robustness, and generate predictive insights across biomedical, environmental, and technological domains.
  • Coached and mentored junior data scientists in NLP workflows, code quality, and reproducible analytics.

Marie Curie Post-doctoral Fellow — Centre for Genomic Regulation (CRG)

Jan 2016 – Sep 2019 World-class research centre in genomics and molecular biology, Barcelona

  • Developed computational models of DNA–protein interactions and chromatin dynamics, combining analytical theory with large-scale molecular simulations on HPC clusters (CPUs and GPUs); results published in Nature Communications.
  • Analysed multi-omics experimental data: single-cell RNA-seq (gene expression quantification, clustering, differential expression), ChIP-seq (peak calling, genome alignment), and DNA sequence data to establish correlations between chromatin structure and gene regulation.
  • Built reproducible Python analysis pipelines to process hundreds of thousands of simulations and experimental datasets, with strong attention to scalability, version control, and documentation.

Post-doctoral Fellow — Sorbonne Université / ORNL

Jul 2013 – Dec 2015 Paris, France; visiting researcher at Oak Ridge National Laboratory, USA

  • Mathematical modelling of DNA mechanical properties using Molecular Dynamics, Monte Carlo simulations, and analytical theory; performed large-scale all-atom molecular dynamics simulations on HPC infrastructure at Oak Ridge National Laboratory.

Education

PhD in Physics (DNA Biophysics) — Imperial College London

Nov 2009 – Jul 2013 Theoretical and computational study of electrostatic interactions in DNA systems

MSc in Condensed Matter Physics — Università di Napoli “Federico II”

2006 – 2009 · 110/110 cum laude

BSc in Physics — Università di Napoli “Federico II”

2003 – 2006 · 110/110 cum laude

Skills

Category Details
Programming Python (primary) · R · SQL · C/C++ · Bash
AI, NLP & ML LLMs (OpenAI, HuggingFace) · BERTopic · TF-IDF · spaCy · NLTK · semantic search · classification · clustering · random forests · SVMs
Life Sciences & Bioinformatics scRNA-seq · ChIP-seq · genome alignment · molecular simulations · omics data integration · biomedical text mining
Data Engineering & DevOps ETL pipelines · PostgreSQL · Mage AI · Docker · GitHub Actions · Git · HPC (SLURM, PBS)
Visualisation & Reporting Matplotlib · Plotly · ggplot2 · Apache Superset · Google Looker Studio

Selected publications

  • Theoretical principles of transcription factor traffic on folded chromatin, Nature Communications (2018)
  • The Physics of Epigenetics, Reviews of Modern Physics (2016)
  • Theory and Simulations of Toroidal and Rod-Like Structures in DNA Condensation, J. Chem. Phys. (2015)

Selected projects

  • sc_hiv (R, Python) — End-to-end analysis of single-cell RNA-seq data from cell lines expressing HIV proteins: gene expression quantification, clustering, differential expression, and correlation with HIV integration locus.
  • VocTagger (Python) — High-performance NLP library for entity extraction and thematic mapping in large scientific corpora (spaCy, multiprocessing); used to classify 30,000+ Horizon 2020 projects against UN SDGs.
  • sbs_tracers (Python) — Scalable simulation and analysis pipeline for coarse-grained models of DNA–protein interactions on HPC clusters; published in Nature Communications.

Additional

Languages: Italian (native) · English, Spanish, Catalan, French (full professional proficiency)

Training: Professional trainer at The Paper Mill (2018–2019) — workshops on scientific communication for PhD students at ICTA and IDIBAPS, Barcelona