Metagenome-Assembled Genomes from the Morrow Plots
Recovering the first metagenome-assembled genomes from the historic Morrow Plots, revealing management-associated dominance of archaeal ammonia oxidizers in long-term agricultural soils.
Research
My research spans computational biology, high-performance computing, and machine learning privacy, with a focus on scalable methods for analyzing complex biological data.
Current projects
Privacy-Preserving Machine Learning for Biomedical Data
Investigating membership and property inference attacks against machine learning models trained on biomedical data, and developing privacy-preserving techniques that keep accuracy while protecting sensitive information.
Anomaly Detection in Industrial Images
Deep learning approaches for detecting anomalies in industrial images with minimal supervision, using wavelet-based feature extraction and self-supervised learning.
Previous projects
De Novo Haplotype-Level Assembly of Hybrid Yeast
A chromosome-level, subgenome-aware de novo assembly of the Saccharomyces bayanus genome, revealing new insights into subgenome divergence after hybridization.
GPU-Accelerated CT Image Analysis
Optimized CUDA implementations for CT image preprocessing and quality assessment, with substantial speedups while maintaining accuracy.
Metagenomic Analysis & Sample Origin Prediction
Computational methods for analyzing low-biomass microbial samples and predicting their geographic origins from taxonomic profiles.
Research impact
Interdisciplinary approach
Bridging computational methods with biological insight.
Performance optimization
Cutting computation time from days to hours through GPU acceleration.
Privacy protection
Advancing secure machine learning for sensitive biomedical data.