I am a Postdoctoral Fellow in the Ray and Stephanie Lane Computational Biology Department at Carnegie Mellon University. I currently work on developing computational and machine learning methods, as well as software, for analyzing and understanding single-cell epigenomics.
I obtained my Ph.D. degree in the Department of Computational Mathematics, Science & Engineering (CMSE) at Michigan State University. My training focused on network biology, graph representation learning, spectral graph theory, and machine learning.
- Stay tuned! Something exciting is going online soon!
- obnb [paper]: a Python toolkit for setting up benchmarking datasets using publicly available biomedical networks and gene annotation resources. A comprehensive benchmarking study with various graph neural networks and graph embedding methods is presented in obnbench.
- DANCE [paper]: an extensive toolkit for deep learning with single-cell (multi-)omics data.
- PecanPy [paper]: a memory efficient and Numba accelerated Python implementation of node2vec with an improved version node2vec+ [paper] for weighted graphs.
- PyGenePlexus [paper]: a network-based gene classification service using machine learning and gene interaction network features.
- GTaxoGym [paper]: a taxonomic study of benchmarking graph datasets from various domains based on the GNN model sensitivity to a collection of graph perturbations.
- βοΈ I share my passion about network biology and machine learning via blog posts on Medium
- π I create mathematical and algorithmic visualizations using Manim, which was first developed and used by my favorite math YouTube channel 3Blue1Brown.
- π€ I contribute to open source projects in various ways
- Implemented patches and fixes: Pytorch Geometric
- Feature discussion: Hydra
- Bug report and question posting: mypy, mydisease.info, ndex2-client
- π¦ I work on several small packages on the side to help improve my production workflow and exercise my dev workflow
conda create -n remylau python=3.11 -y && conda activate remylau
pip install -e .
conda clean --all -y