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Teaching & Supervision

Continuing education courses

2025: QSAR Demystified: From Principles to Practice in Regulatory Toxicology

2024: Navigating chemical space in association with experimental and predicted toxicity data to identify relevant analogues for hazard and risk assessment

Teaching

2015–2016: Molecular visualization using PyMOL

2012–2015: Ph.D. program teaching session (Monitorat)

  • Probability, combinational statistics — practice sessions, Bachelor 1st year (15%)
  • Multivariate analysis and statistics — lecture, practice sessions, Master 2nd year (80%)
    • R programming — practice sessions and tutored projects
    • Statistical test theory — lectures
    • Multivariate visualization methods: PCA, AFC, MDS, …
    • Machine learning tuning and performance:
      • Linear regression (PLS), linear discriminant analysis (LDA), decision trees (CART), support vector machine (SVM), random forest (RF), neural network type singleton (NN)
      • Applicability domains, model limitations and evaluations
  • Build a professional network — lecture, Master 2nd year (5%)

Student supervision

2019: Ph.D. visiting student — Riu Zheng

2019: Master student, mentor award program — Andy Mendoza

2015: Master student — Ivan Toussies

2015: Bachelor Bioinformatics student — Hélène Borges

2014: Master Bioinformatics student — David Brandao