Causal and predictive regulatory genomics · Interpretable AI/ML · Single-cell multi-omics · Crop stress resilience
I am an Assistant Professor — Fixed Term in the Plant Research Laboratory , the Department of Plant Biology , and the Great Lakes Bioenergy Research Center (GLBRC) at Michigan State University.
My research program develops causal and predictive regulatory-genomics approaches to determine how cell-type-specific gene regulatory networks encode crop responses to environmental stress and whether those regulatory rules can predict resilience across genotypes. I integrate time-resolved transcriptomics, single-nucleus multiome profiling, transcription factor–DNA binding maps, functional genetics, and interpretable AI/ML to generate experimentally testable predictions.
At MSU, I lead independent projects in sorghum stress genomics, regulatory-network inference, and network-enabled gene discovery. I serve as Principal Investigator on three competitive DOE Joint Genome Institute user projects and an MSU Project GREEEN award. My broader research is also supported through GLBRC within the Brandizzi Lab.
2026 Yaoping Zhang Bioenergy Research Award
Great Lakes Bioenergy Research Center
Recognized for contributions to systems-level gene discovery, regulatory genomics, and research advancing the understanding of plant resilience in bioenergy crops.
Genomic and single-cell technologies can describe transcription factor activity, chromatin accessibility, and gene expression at unprecedented resolution, but most regulatory maps remain correlative. My program uses interpretable modeling and focused experiments to distinguish functional regulatory relationships from context-dependent associations and to determine whether those relationships explain variation in crop performance.
Central question: How do cell-type-specific regulatory networks encode environmental stress responses, and can those regulatory rules predict resilience across genotypes?
Integrate time-resolved transcriptomes, DNA sequence and motifs, DAP-seq binding maps, chromatin accessibility, and gene expression to rank TF–cis-regulatory element–target relationships in defined root cell types, stresses, and treatment stages.
Test prioritized transcription factors through targeted pooled CRISPR perturbation in root-derived sorghum protoplasts, coupled with single-cell transcriptomic readouts and focused cis-regulatory element assays.
Determine whether experimentally informed regulatory rules and natural variation in TFs and cis-regulatory elements predict molecular responses, root traits, growth, and stress performance across genetically diverse sorghum.
Root and shoot RNA-seq resources define shared, tissue-specific, and stress-specific transcriptional responses and provide temporal evidence for candidate regulatory programs.
Sorghum DAP-seq profiles spanning approximately 31% of the genome define a focused candidate space of TF–DNA interactions for cell-type-aware modeling and causal testing.
Control and salinity-stressed sorghum roots reveal cell-type-specific transcriptional and chromatin-accessibility states, including distinct responses in cortex and pericycle populations.
NEEDLE, GENIE3, coexpression modeling, promoter analysis, and functional assays establish a practical workflow in which computational models generate focused biological hypotheses rather than serving as endpoints.
After establishing this core framework in sorghum roots, I will extend it selectively to stress recovery, additional crop systems, and—through collaboration—the influence of root regulatory states on root chemistry and microbial recruitment.
Selected publications spanning AI-enabled regulator prediction, network modeling, multi-omics, functional genomics, and crop stress biology.
A complete publication list is available in my CV and on Google Scholar .
My current research is supported through competitive user-facility awards, an independent MSU award, and collaborative GLBRC research. These projects have generated the multimodal resources that underpin my causal and predictive regulatory-genomics program.
Principal Investigator on three competitive projects awarded in 2021, 2023, and 2025. These projects support large-scale sorghum stress transcriptomics, DAP-seq profiling of 142 stress-responsive transcription factors, and follow-up time-resolved stress experiments.
Principal Investigator on a $30,000 project examining how abiotic stress alters regulatory DNA activity in sorghum at single-cell resolution.
Research supported through the Great Lakes Bioenergy Research Center within the Brandizzi Lab, contributing regulatory-genomics and systems-biology approaches to bioenergy crop resilience.
I teach students to connect biological mechanisms with quantitative evidence. My teaching emphasizes conceptual integration, experimental design, reproducible data analysis, responsible interpretation of computational models, and clear scientific communication.
I use active learning, structured discussion, primary literature, and real biological datasets to help students move from descriptive observations to testable models and evidence-based conclusions.
I mentor undergraduate researchers, research assistants, and early-career scientists through scaffolded training, explicit expectations, regular feedback, reproducible research practices, and meaningful ownership of scientific questions.
My goal is to help trainees become confident scientific thinkers who can work across experimental biology, computation, and agriculture.
My current CV is available here: Download CV .