Alexandre Drouin

ALEXANDRE DROUIN

Ph.D Student


CAREER RESUME

My main fields of interest are machine learning and computational biology. I enjoy analyzing biological problems and designing algorithmic solutions to address them. In the context of my PhD, I am developing machine learning approaches to predict phenotypes from multiomics data (genomics, transcriptomics, metabolomic, etc.). I am particularly interested in making the most out of the vast amount of publicly available data through supervised and semi-supervised machine learning. Recently, I have been working on predicting antibiotic resistance in bacteria, a major public health concern. 

 
FINANCING
 
2014-2017       NSERC Alexander Graham Bell Canada Graduate Scholarship – Doctoral
2013-2014       FRQ Graduate Masters Scholarship (B1)
2012-2013       NSERC Alexander Graham Bell Canada Graduate Scholarship – Master’s     
2012-2012       NSERC Undergraduate Student Research Award
 
 
PUBLICATIONS
  • Drouin, A., Giguère, S., Déraspe, M., Marchand, M., Tyers, M., Loo, V.G., Bourgault, A.-M., Laviolette, F. & Corbeil, J. (2016). Predictive Computational Phenotyping and Biomarker Discovery Using Reference-Free Genome Comparisons. Submitted. [ pdf ]
  • Drouin, A., Giguère, S., Déraspe, M., Laviolette, F., Marchand, M. & Corbeil, J. (2015, July). Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance. Greed is Great Workshop, ICML, Lille, France. [ pdf ]
  • Drouin, A., Giguère, S., Sagatovich, V., Déraspe, M., Laviolette, F., Marchand, M. & Corbeil, J. (2014, December). Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine. Machine Learning in Computational Biology Workshop, NIPS, Montréal, Canada. [ pdf ]
  • Giguère, S., Drouin, A., Lacoste, A., Marchand, M., Corbeil, J., & Laviolette, F. (2013). MHC-NP: Predicting peptides naturally processed by the MHC. Journal of immunological methods, 400, 30-36. [ pdf ]
  • Latulippe, M., Drouin, A., Giguère, P., & Laviolette, F. (2013, August). Accelerated robust point cloud registration in natural environments through positive and unlabeled learning. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence (pp. 2480-2487). AAAI Press. [ pdf ]
  • Giguère, S., Marchand, M., Laviolette, F., Drouin, A., & Corbeil, J. (2013). Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC bioinformatics, 14(1), 82. [pdf ]

FORMATION

2014 –      Ph.D degree in computer science

Université Laval, Québec, Canada

Director : Dr François Laviolette

Project title : L’apprentissage automatique et les sciences “omiques”

2012-2014      Master’s degree in computer science

Université Laval, Québec, Canada

Director : Dr François Laviolette

Project title : Algorithmes d’apprentissage automatique à grande échelle pour l’élaboration de nouveaux composés pharmaceutiques

2010–2012      Bachelor’s degree in computer science

Université Laval, Québec, Canada