Causal and predictive regulatory genomics · Interpretable AI/ML · Single-cell multi-omics · Plant environmental adaptation
I am an Assistant Professor—Fixed Term in the MSU-DOE Plant Research Laboratory and Department of Plant Biology at Michigan State University, and an affiliate of the Great Lakes Bioenergy Research Center .
My research seeks to understand how gene regulatory networks translate environmental change into adaptive molecular states and plant performance. I combine regulatory genomics, single-cell and multi-omics technologies, functional genetics, and interpretable AI/ML to move from descriptive regulatory maps toward experimentally supported and predictive biological models.
My work spans Arabidopsis, maize, Brachypodium, and sorghum, with current emphasis on environmental stress responses in sorghum. At MSU, I lead independent DOE Joint Genome Institute user projects and an MSU Project GREEEN project while contributing to collaborative GLBRC research.
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.
Modern genomics can reveal where transcription factors bind, which chromatin regions are accessible, and which genes respond to environmental change. My research asks the next question: which regulatory relationships are functionally important, and can experimentally supported regulatory rules predict plant responses beyond the conditions in which they were discovered?
Central question: How do cell-type-specific regulatory networks translate environmental change into adaptive states, and can experimentally supported regulatory rules predict resilience across genotypes and environments?
Integrate transcriptomic, chromatin, TF-binding, sequence, and cell-type information to identify regulatory relationships associated with plant responses to environmental change.
Experimentally evaluate prioritized transcription factors and cis-regulatory elements to distinguish functional mechanisms from correlative associations.
Build experimentally informed models that test whether regulatory principles generalize across genetic backgrounds, environmental conditions, and biological contexts.
The program builds on datasets, methods, and biological systems that I have already established.
Time-resolved transcriptomic resources across drought, heat, salinity, and related stress contexts provide temporal and tissue-level views of plant regulatory responses.
DAP-seq profiles define millions of candidate TF–DNA interactions and provide a genome-scale foundation for regulatory-network reconstruction.
Root snMultiome profiling links transcriptional states with chromatin accessibility to resolve cell-type-specific regulatory responses.
NEEDLE and related network approaches combine computational prioritization with experimental validation so that models generate testable biological hypotheses rather than serving as endpoints.
Circadian and transcriptional regulation underlying biomass heterosis in maize.
Regulatory networks controlling proteotoxic stress adaptation, recovery, and cell fate in Arabidopsis.
Integrative modeling and validation workflows for prioritizing plant transcriptional regulators.
Cell-type-resolved, experimentally grounded models of plant environmental adaptation.
Long-term direction: My goal is to identify transferable regulatory principles that explain how plants adapt to changing environments and to use those principles to guide mechanistic discovery and crop resilience research across plant systems.
Representative publications spanning AI-enabled regulator prediction, network modeling, multi-omics, functional genomics, and plant stress biology. For the full publication record, see Google Scholar .
My current research is supported through competitive DOE user-facility awards, an independent MSU award, and collaborative GLBRC research. Together, these projects have generated the experimental and computational resources that underpin my regulatory-genomics program.
Principal Investigator on three competitive projects awarded in 2021, 2023, and 2025, supporting sorghum stress transcriptomics, genome-scale TF–DNA binding maps, and follow-up studies of dynamic environmental responses.
Principal Investigator on an MSU Project GREEEN award 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.