Who I am
Hello ! I’m Evan, a 24-year-old freshly graduated life sciences engineer. I am really interested in topics related to personalized and data-driven medicine and would be enthusiastic to bring my personal skills and knowledge gained during my studies to an innovative company.
Education
EPFL Master’s degree in Life Sciences Engineering and minor in Data Science with a 5.62/6 average. The master’s thesis was conducted at NeuroPoly (Canada) and resulted in the highest grade and the submission of a scientific paper. The courses and projects were focused on the application of data/computer science to life sciences topics (oncology, cell & molecular biology, genetics & genomics and neurology).
- Master’s thesis project on using deep learning technique for multimodal spinal cord MRI registration.
- Industry internship in a start-up doing data-driven precision medicine.
- Semester projects to implement neural networks for gene expression prediction and for instance segmentation of cells.
EPFL Bachelor’s degree in Life Sciences Engineering with an average higher than 5/6. Polytechnic education based on multidisciplinary skills built on a solid theoretical training in basic and life sciences. Interest developed in molecular medicine, genetics/genomics and data/computer science fields.
Master Thesis
Implementation of a fully automatized deep learning pipeline for multimodal registration of brain and spinal cord MRI images for the NeuroPoly lab led by Prof. J. Cohen-Adad at Polytechnique Montreal. The models and pipeline are publicly available on GitHub. The project was realized in Python and Shell and resulted in the submission of a scientific article to the journal Aperture Neuro.
Experiences
Development of neural networks for the prediction of diagnosis and treatment outcomes of cardiometabolic pathologies using historical patient data (diagnosis, factors, medications). Analyses performed in Python and neural networks developed with Keras/TensorFlow.
- Gap analysis
- Transfer learning
- Neural networks interpretation and features explanation using Shapley values
Internship in the data analytics and interpretation platform of the Health 2030 Genome Center in Geneva. Analysis of RNAseq, proteomics and clinical data in a project around a genetic disease. Analyses performed in R.
Extra scholar personal tutor in maths, physics, biology and chemistry for high school students.
Projects
Below is a description of the various data/computer science projects, primarily applied to the life sciences, completed during my master's degree, along with a link to the submitted reports.
Implementation of a neural network for the Laboratory of Computational and Systems Biology led by Prof. F. Naef at EPFL to predict gene expression data from ChIP-seq data and explore the biological meaning of the network. The network has been developed to be biologically interpretable. The project was realized in Python and the networks were developed with Keras/TensorFlow (report available here).
Implementation of a convolutional neural network for the Laboratory of the Physics of Biological Systems (LPBS) led by Prof. S. Rahi at EPFL to do instance segmentation of yeast cells on microscope images. Neural network implemented: Mask R-CNN, using Python and Keras/TensorFlow (report available here).
Use of image analysis and pattern recognition to build a pipeline (OOP) able to track a robot and recognize patterns (segmentation, description, classification). The presentation of the project is available here.
Determine the number of cell types present in a dataset and the marker genes characterizing the different cell types. Identification of the cell types present and the amount of genes expressed per cell and per cell type. The project was realized in R (report available here).
Determine three major molecular profiles across 22 distinct nuclei of the thalamus through unsupervised hierarchical clustering of the most differentially expressed genes. The project was realized in R (report available here).
Genome wide association analysis for identification of single-nucleotide polymorphisms correlated to coronary artery disease predisposition using HDL cholesterol concentration data. The project was realized in R (report available here).