Where biological questions meet computational logic
My research has always been guided by a central curiosity: how microbes manage stress, survive disruption, and reorganize their function when environments become unstable. As an undergraduate in genetic engineering, I explored microbial toxicity responses, secondary metabolite production, and functional annotations in survival pathways. This deepened during my master’s in computational biology, where I studied secretion systems and stress-regulatory mechanisms across pathogenic bacteria. It was here that I learned how to translate biological questions into data-driven workflows, and how molecular adaptation is rarely about isolated genes; it is about how systems rewire under challenge.
Now, as a PhD candidate in Human Toxicology, I focus on understanding how microbial gene function and regulation shift during disease, using the oral microbiome as a model system. I study functional changes in the microbial communities associated with dental caries through large-scale analysis of metagenomic, metatranscriptomic, and metaproteomic data. My research centers on identifying stress-response systems, such as toxin-antitoxin modules, that may play roles in microbial persistence and ecological reprogramming. I build reproducible computational pipelines tailored for high-performance computing environments, capable of handling multi-organism, multi-layered datasets. These workflows are designed not only for accuracy and speed but also for statistical rigor and biological interpretability.
Caries offers a uniquely tractable system to study microbial adaptation at the functional level. It is a slow, microbially mediated transition, rather than an acute event. I model this shift not as a taxonomic replacement, but as a reorganization of activity across microbial populations. My approach emphasizes the collective dynamics of function: how groups of genes across multiple organisms adjust their expression and regulatory behavior under prolonged stress. The workflows I build are shaped by this systems perspective, with a focus on integrating multiple molecular layers while retaining biological meaning and methodological transparency.
Looking ahead, I aim to develop computational resources that advance a One Health perspective, linking microbial function, environmental exposures, and human health. My long-term goal is to build frameworks that connect exposomics, microbiome science, and disease modeling through the integration of multi-omics. I want to understand how everyday exposures shape microbial communities and how those communities, in turn, influence the trajectory of chronic diseases. By designing tools that allow us to ask these questions at functional resolution, I hope to make microbial systems visible, contextual, and essential to how we understand and intervene in complex health outcomes.