- 🎓 Ph.D. Candidate in Bioinformatics at Université de Montréal
Affiliations: Mila – Quebec AI Institute, Montreal Heart Institute (MHI), and IRIC
Thesis: On the Visualization of Genetic Data - 🧠 Research Interests: population genetics, manifold learning, causal inference, deep learning for genomics
- 🧳 Experience: Machine Learning Engineer (insitro, Huawei Noah’s Ark Lab), Research Assistant (MHI), Visiting Scholar (Yale, Dept. of Genetics)
- 📚 Publications: Bioinformatics Advances (2023), CSBJ (2025), Behavior Research Methods (2018)
- 🎤 Talks: How to Visualize a Biobank, Lessons from the All of Us UMAP Scandal (MHI, 2024–25), What Every Biologist Should Know About Manifold Learning (IRIC, 2023)
- 🏆 Awards: NSERC CGS-D, Michael Smith Foreign Study Supplement, Canadian Bioinformatics Hub Training Award, Ontario Graduate Scholarship
- 💻 Skills: Python, Bash, LaTeX, SLURM, Nextflow, PLINK, Git, AWS
- 🌐 Links: LinkedIn · Google Scholar
- 🙂 Pronouns: he/him
- A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction bioRxiv (2025) doi
- Predicting pathogen evolution and immune evasion in the age of AI, CSBJ (2025) doi
- Towards computing attributions for dimensionality reduction techniques, Bioinformatics Advances (2023) doi
- A modern take on the bias-variance tradeoff in neural networks ArXiv (2018) doi
- Argus: Automated quantification of zebrafish behavior, Behavior Research Methods (2018) doi
✨ Maintainer & contributor to: PHATE, scprep, graphtools
📢 (Previously) Organizer: Bio+AI Reading Group at Mila, Machine Learning on Molecules (MoML 2023) conference