Research

My research integrates plant pathology, genomics, computational biology, and high-throughput phenotyping to understand the genetic basis of plant disease resistance and develop tools for improving disease-resistant crops.

Disease Resistance and Susceptibility Genes

A major focus of my work is understanding host genes that influence susceptibility to disease. I am particularly interested in the Mildew Locus O (MLO) gene family, whose members function as susceptibility factors for powdery mildew in many plant species.

My current work examines MLO diversity across grapevine germplasm and genomic resources. I use comparative genomics, sequence analysis, protein-level prediction, and phenotype-associated genetic variation to identify alleles and functional regions that may be useful for developing powdery mildew resistance.

An important goal of this work is to identify resistance-associated modifications that reduce susceptibility while retaining normal plant development and minimizing pleiotropic effects.

Comparative Genomics and Pangenomics

Plant germplasm contains considerably more genetic diversity than can be represented by a single reference genome.

I use haplotype-resolved genome assemblies, comparative genomics, pangenome approaches, and population-scale sequence data to investigate conservation and diversity within genes involved in disease resistance.

Current questions include:

• How conserved are powdery mildew susceptibility genes across Vitis?
• Which alleles occur naturally within wild and cultivated germplasm?
• Which protein regions tolerate natural variation?
• Are potentially useful loss-of-function or hypomorphic alleles already present in breeding collections?
• How can natural variation inform gene-editing targets?

Computer Vision and High-Throughput Phenotyping

Plant phenotyping is often limited by the speed and subjectivity of manual measurement. I develop image-analysis and computer-vision approaches for converting plant images into reproducible quantitative traits.

My current work focuses on automated phenotyping of grapevine nursery populations, including approaches for detecting and quantifying foliar traits from large image collections.

Previous projects have included:

• automated microscopic quantification of powdery and downy mildew
• computer-vision detection of two-spotted spider mite life stages
• automated fungicide, miticide, and antibiosis efficacy assessment

My broader research interest is in integrating genetic data with imaging, spatial information, machine learning, and experimental metadata to uncover genetic insights and make plant phenotyping more objective, scalable, and quantitative.

Genome Editing and Functional Validation

Computational predictions are most useful when they can ultimately be tested experimentally.

My doctoral research used CRISPR-mediated mutagenesis and functional genomics to investigate MLO susceptibility genes in hop. This work included developing transformation and editing workflows and evaluating candidate mutations for powdery mildew resistance.

I am interested in using natural genetic diversity, comparative genomics, protein structure, and evolutionary conservation to prioritize targeted mutations before functional validation.

Reproducible Research and Scientific Automation

I also develop tools that make experimental and computational workflows more efficient and reproducible.

These projects have included:

• high-throughput, massively parallel computer vision and omic pipelines
• specialized multi-agent and multimodal AI systems to tackle complex biological tasks
• automated IoT sensor devices
• high-performance computing pipelines

Whenever possible, I favor reusable and open approaches that allow methods to be adapted to new biological questions.