Catheter-associated urinary tract infections (CAUTI) are one of the most common nosocomial infections worldwide and represents a significant healthcare burden. CAUTI is often a polymicrobial disease, where a patient is infected with multiple uropathogens. Thus, my research focuses primarily on investigations into the polymicrobial interactions occurring in CAUTI.

Cystic Fibrosis (CF) is a recessive genetic disorder which causes abnormally thick mucus in essentially all mucosal organ systems. Historically, people with CF had a greatly reduced lifespan due to lethal respiratory infections resulting from an inability to clear microbes from the respiratory tract. Thankfully, in recent years the advent of chemical therapies has greatly extended the average lifespan of people with CF. However, there is now a growing awareness of morbidity and mortality caused by the effects of CF on other organ systems, particularly the gastrointestinal tract. Of note, people with CF have a rate of colorectal cancer (CRC) 5-10 times higher than the general population.

My research focuses on the application of deep learning techniques to cancer prognosis through the integration of multi-omics and multi-region sequencing data. I am particularly interested in developing deep learning models that explicitly account for intratumor heterogeneity, a defining feature of cancer progression characterized by the coexistence of genetically distinct subclones within the same tumor. These subclones are major drivers of disease recurrence, metastasis, and treatment resistance, yet they are often overlooked in conventional prognostic models.

Cryptococcus neoformans is a haploid budding yeast that causes cryptococcal meningoencephalitis in immunocompromised individuals, leading to approximately 120,000 deaths worldwide each year. My research investigates how this environmental fungal pathogen adapts to host-induced stress conditions, by examining the pathways involved in translatome reprogramming and virulence regulation.

Eventually, I aim to identify key regulatory networks that can be targeted for the development of novel antifungal strategies.

My research asks how bacteria survive the conditions we design to eliminate them. Chronic and device-associated infections are difficult to treat because they adapt to the host, to one another, and to antibiotics over the course of a single infection, and that adaptation is a major driver of persistence and antimicrobial resistance. I study these dynamics to understand infection as an evolving process, so that treatment can be aimed at an adapting bacterial population. I work at the interface of molecular microbiology and computational genomics, using whole-genome and metagenomic sequencing to trace how uropathogens evolve inside a single host. My primary model is catheter-associated urinary tract infection (CAUTI). Working with a longitudinal cohort of chronically catheterized patients, I have shown that symptomatic infection often develops from existing persistent bacteria rather than from newly acquired strains. In one persistent Proteus mirabilis lineage, I traced a single mutation that converted the population into a hypermutator, accelerating its diversification and reshaping how it behaved in the host. Findings like these reframe chronic infection as a within-host evolutionary process and point toward more precise antibiotic stewardship, along with host-adaptive bacterial changes that could serve as candidate targets for improved diagnostics and infection-risk stratification.

As a postdoctoral associate in the Armbruster Lab at the University at Buffalo, I serve as the primary bioinformatics lead for the laboratory and the broader department. My role involves hybrid wet-lab and computational analysis with end-to-end analytical support spanning comparative genomics, metagenomics, RNA-seq, variant analysis, proteomics, and custom pipeline development. Looking ahead, I want to extend this within-host framework across the pathogens and polymicrobial communities that drive chronic urinary infections. My longer-term goal is to turn these genomic observations into tools that clinicians can use, from markers that flag when colonization is likely to progress to disease, to evidence that sharpens how and when antibiotics are administered.