API Designer & Network Analysis Expert
I specialize in statistical network analysis and develop tools that make complex network science accessible to researchers and practitioners. Creator of PhyNetPy, bringing advanced network algorithms to the Python ecosystem.
Showcasing my work in network analysis, data science, and open-source development
A comprehensive Python library for math and statistics-inspired network analysis. Features advanced algorithms for Network topology comparison and simulations, Bayesian inference, and machine learning approaches to network science.
Interactive web application for visualizing and analyzing large-scale networks in real-time. Key features include:
BioForge is a GPU-accelerated Python library for generating synthetic DNA, RNA, and protein sequences with known ground truth. Designed for training and benchmarking ML models in computational biology. It includes evolutionary models (substitution, indels, recombination), population genetics simulations, sequencing error models, and ML-ready outputs (one-hot encoding, PyTorch datasets) with full reproducibility through configuration tracking.
Bringing advanced optimization and engineering tactics to network analysis methods
Ultra-Optimized implementations of network algorithms with parallel processing support and gpu vectorization capabilities for large-scale networks.
PhyNetPy comes prepackaged with a variety of network space search algorithms as well as a variety of network alteration moves.
Future updates to PhyNetPy will include support for running machine learning models on quartet networks and sequence data.
I'm passionate about applying mathematically backed methods to solve complex network problems-- many of these are NP-hard problems that require careful optimization. With a background in software design and algorithm creation, I focus on developing accessible tools that bridge the gap between theoretical network science and practical applications.
When I'm not coding, you can find me gardening, cooking, playing tennis/guitar/golf, or going on a walk with my lovely fiancée. I believe in open-source collaboration and am always excited to connect with fellow researchers and developers working on network science challenges.
I'm always interested in collaborations, research opportunities, and exciting network science challenges. Feel free to reach out!