2025 SUNY GREAT Award Recipients

The laser tattoo removal industry is estimated to have a value in the hundreds of millions to billions of dollars range, yet we do not understand how light causes a tattoo to fade. I propose to address this knowledge gap about tattoo photodegradation by 1) developing generalized methods to characterize the photodegradation pathways and products of tattoo pigments in biologically relevant conditions, 2) understanding the photodecomposition of particularly problematic azo pigments, and 3) elucidating the photomechanical process of tattoo breakdown in cadaverous human tissue. There is increasing interest in the use of tattoos for medical diagnostics, much of which relies on the design of stimuli-responsive pigments; thus this work will help those efforts by highlighting how to design photostable pigments. Finally, the Swierk group maintains a website called “whatsinmyink.com” that provides scientifically validated compositions of commercial tattoo ink to the public.

I aim to deepen my understanding of how episodic memory and other cognitive processes evolve across the adult lifespan. My research focuses on unraveling the complex interplay between environmental and lifestyle factors that influence both physical and brain health, often in a bidirectional manner. I explore how these factors contribute to either safeguarding or increasing risk during healthy aging and in neurocognitive disorders like Alzheimer's disease. Specifically, I am interested in addressing health disparities, particularly vascular disease, to better understand its role in cognitive aging and memory processes. By advancing our knowledge in these areas, I hope this research will inform interventions and policies that promote equitable brain health, ultimately improving quality of life and reducing the global burden of cognitive decline.

My research focuses on the effects of maternal diet and early-life feeding exposures on adult taste/flavor preferences using rodent models. There is ample human research that demonstrates that offspring are exposed to maternal diets via amniotic fluid and lactation and these exposures can influence food and flavor preferences in children. However, little research has focused on the long-term implications of these exposures. In other words, can a maternal diet or early feeding exposures influence feeding behaviors into adulthood? I expect the collected data will increase awareness regarding the impact feeding practices during gestation and early childhood can impact long-term feeding practices and health.

As a biological anthropologist, I'm generally interested in how the environments and experiences of an individual become embodied and impact health. For my master's thesis, I am researching what factors impact the mental health of mothers in The Gambia. Understanding how various sociocultural and demographic factors affect mental health and other biological outcomes is crucial to determining public health strategies and driving policy decisions that improve health outcomes globally. 

My research deals with the Combined Sewer Systems, this is a sewer system that conveys both sewage, and stormwater in one pipe network. During heavy rain events the combined sewer system is designed to allow a portion of this water mixture to overflow into local waterways. I am researching how green infrastructure can be used to reduce the amount of water overflowing, as well as improve the water quality of the water being discharged during overflow events. This research could be useful to improve water quality and reduce combined sewer overflows, while minimizing the cost of improving the sewer infrastructure.  

My research mainly focuses on deep learning and model explainability. My work aims to make complex models more interpretable and transparent, addressing critical challenges in trust and accountability for AI systems. Emphasizing robustness and innovative applications, my work strives to bridge the gap between theoretical models and real-world problems, enabling AI to solve complex global challenges. The primary goal of my research is to foster greater trust in complex machine learning models, empowering decision-makers with actionable, transparent insights.

My research focuses on characterizing host cell intrinsic responses to retroviruses, such as Human Immunodeficiency Virus 1 (HIV-1). Recently, a family of E3 Ubiquitin Ligases known as Membrane Associated RING-CH (MARCH) proteins have been shown to decrease HIV-1 infectivity by targeting the HIV-1 glycoprotein responsible for viral entry. My work aims to characterize further the mechanism in which MARCH proteins target HIV-1 glycoproteins and block HIV-1 infection. This work will help further characterize the complex web of host-pathogen interactions and uncover potential novel therapeutic targets for HIV-1.

Lung cancer (LC) is the leading cause of cancer death, making up nearly 25% of all cancer deaths. Although some advances in personalized treatments have improved survival rates, there is an ongoing clinical need to develop novel targeted therapies against LC subtypes with particularly poor prognosis. However, our ability to develop these treatments is currently limited by a lack of compelling actionable targets. Publicly available screening data, along with a multitude of prior studies and data from our lab, have validated nuclear factor erythroid 2-related factor 2 (NRF2) as a strong and selective dependency in lung cancer. The proposed research explores the potential of exploiting this dependency on NRF2 in lung cancer by biochemically characterizing its functional binding partners, which could serve as actionable targets to develop novel targeted therapies. To overcome the inherent challenges of targeting NRF2, we used a high-throughput reporter screen and biochemical assays to identify PRMT5 as a putative modulator of NRF2 activity. We hypothesize that the activity of NRF2 in KEAP1-mutant LC is negatively regulated by a PRMT5-NRF2 interaction, and that loss of this interaction leads to intolerable levels of NRF2 activity that promote tumor growth. Our current efforts are focused on characterizing the PRMT5-mediated repression of NRF2 transcriptional activity and evaluating the impact of methylosome perturbations on KEAP1-mutant LC growth through functional genetic and epigenetic assays. Furthermore, we also look to follow up on other putative regulators of NRF2 activity that play a functional role in sustaining LC growth. The information gained from this project will provide a solid foundation for the advancement of future drug development efforts focused on targeting NRF2.

My research is focused on the molecular and cellular basis of disease resistance in the eastern oyster Crassostrea virginica. I am interested in using a variety of techniques, with a particular focus on functional gene annotation, to better understand how innate immunity and other defense systems work in this model bivalve. Currently I am focused on characterizing triploid and diploid oysters on a cellular level to better understand how ploidy impacts vulnerability to disease and other environmental stressors. Soon I will transition to identifying the proteins and other factors mediating the interaction between oyster cells and the roseovarius oyster disease bacteria. I believe this research will not only contribute to the generation of oyster stocks less susceptible to these stressors and resultant mortality events, but also advance our knowledge on the nature and evolution of innate immunity in marine invertebrates. 

My research is focused on the sensory processing of taste and how it is modulated by expectation. Using a combination of behavioral, electrophysiological, and computational techniques, I explore how the brain is able to encode and transform the information conveyed by sensory cues, such as smell, to predict taste information and guide eating behaviors. This research will give insight into how individuals make food choices and how those choices can be influenced by the environment.

My doctoral research studies Cryptococcus neoformans, a fungal pathogen that causes deadly meningitis in people who are immunocompromised. My work focuses on understanding how this pathogen is able to adapt to the environment of the human body, specifically looking at fungal signal transduction proteins that regulate adaptation to stressors encountered in the host environment. Through this work, I hope to establish the fungal stress response as an attractive drug target, and identify pathways that can be targeted to prevent diseases such as cryptococcosis.

My research is focused on improving observations and numerical modeling of air quality. My research group led by Scott Miller, added a low-cost air quality sensor package to 38 New York State Mesonet sites in NYC and surrounding areas. My first task has been to calibrate the low-cost sensors, in which I've used machine learning models. This work has recently been completed and the results will be compiled into a paper that we hope to submit by February of this year. The next steps, and the primary goal of my PhD work, is to analyze the air quality observations we have taken to understand and identify local sources in NYC.  Along with this work, I will be working with my other advisor Sarah Lu to use the air quality observations to improve air quality models and forecasts. Through my work, I hope to demonstrate that low-cost sensors can be a useful means of air quality information. As the name implies, they are significantly cheaper than traditional means of observing air quality. I hope that the results of my research motivate more networks of low-cost sensors and expands their use in observational and modeling studies. Since my work is focused on NYC, I hope that my work can be used by policymakers and other relevant stakeholders to promote health and environmental equity.

I currently work on studying molecular dynamics at ultrafast timescales. I seek to enable a better understanding of the short-timescale biological and chemical photoprocesses that govern our lives.

My research uses a multi-method approach to examine risk factors for depression in adolescents. Specifically, I aim to understand how environmental and biological processes interact to confer risk for core features of depression, such as anhedonia. Towards this goal, my NRSA project is examining whether neural measures of reward processing predict real-world experiences of depressive symptoms (e.g., low mood and anhedonia) in adolescents at heightened risk for depression. My hope is that this research will help identify youth at particularly high risk of developing depression and can aid in preventative intervention efforts. 

My research focuses on understanding intrinsically disordered proteins, which are dynamic proteins that play a major role in various vital cellular processes. Intrinsically disordered proteins (IDPs) do not have a fixed three-dimensional structure. They are dynamic and fluctuate through diverse conformations that allow them to play widespread roles in biological functions. The biophysical investigation of disordered proteins has remained elusive for decades due to the difficulty in working with them. I focus on Abl kinase Interactor 1 (ABI1), which controls the progression of prostate tumors and acts as a prostate tumor suppressor. Through investigation of ABI1 and its interactions with nuclear hormone receptors like the androgen receptor (AR) and DNA, we will set the stage to develop novel inhibitors for prostate cancer. This work will have a positive impact by providing a model for determining the mechanism of the interactions of ABI1 and other nuclear hormone receptors, such as the estrogen receptor, a major driver of breast cancer.

I analyze the gravitational wave (GW) signals detected by LIGO-Virgo-KAGRA (LVK) from binary black hole (BH) mergers to study properties of BHs and to test Einstein's theory of General Relativity (GR). When two BHs merge, the resulting remnant briefly oscillates, or rings, down to a stable spinning BH. This ringing corresponds to what is called the ringdown portion of the GW signal from the merger. The ringing of the remnant BH in the ringdown phase consists of damped sinusoid modes of various frequencies and decay times. By GR, this ringdown mode structure directly determines the mass and spin of the remnant BH, which are very important properties to communities that study BH formation and populations. I use Bayesian inference techniques to fit the mode content in ringdown signals and measure remnant BH masses and spins; by comparing to those measured from full GW signal analyses, we can test the validity of GR. I also study ringdowns from BH binary mergers that exhibited particular initial conditions‚ how we can learn more about GW properties from these signals, and how badly incorrect assumptions about the initial conditions can bias the masses and spins we measure in our ringdown analyses. I am actively analyzing the ringdowns from mergers that have been detected in the current ongoing LVK observing run, as well as assisting with the development and testing of improved ringdown analysis softwares. Ringdown is a rich data conditioning and analysis problem that encourages the development of novel computational methods. The usefulness of the techniques employed in ringdown studies extends far beyond astrophysics, to any field that is centered in big data analytics. 

Frontotemporal dementia (FTD) is a leading cause of presenile dementia, and treatments are limited as little is known about the underlying neural mechanisms.  My research focuses on delineating the neurophysiological mechanisms underlying loss of empathy in FTD, one of the most distressing symptoms, as well as testing pharmacological treatments to restore empathy.  This work will uncover the circuit, neural, and molecular mechanisms underlying the loss of empathy in FTD and will identify targets for therapies aimed at improving quality of life for FTD patients and their family and friends.  

Powassan virus (family Flaviviridae) is a reemerging tickborne virus endemic in North America and Russia. In 1997, a POWV-like agent was isolated from Ixodes scapularis in New England and determined to be genetically distinct from the original human isolate. This revealed the existence of two lineages: lineage I, POWV (POWV L1) and lineage II, deer tick virus (POWV L2). POWV L1 is maintained between I. cookei and woodchucks and I. marxi and arboreal squirrels, while POWV L2 is maintained in I. scapularis and small mammalian hosts. This distinction suggests an evolutionary progression of either POWV L1 or L2 into unique transmission cycles and subsequent adaption to these new hosts. Tick, mammalian, and human isolates from New York State (NYS) are typically identified as POWV L2, but for the first time in 45 years three POWV L1 isolates were detected including isolations from I. cookei and the first known isolation of POWV L1 from I. scapularis. Additionally, within the same timeframe in NYS, POWV L1 was identified in Dermacentor variabilis and POWV L2 in Amblyomma americanum. With few available isolates, especially POWV L1, little work has been done to elucidate genetic correlates that confer tick host specificity or the role of tick host responses in driving host-specific adaptation. Therefore, the goal of my work is to utilize our emergent POWV isolates to understand genetic correlates of tick host specificity and to investigate the potential for further adaptation to distinct tick hosts. This previous work suggests introduction of POWV L1 into new tick hosts like I. scapularis facilitates viral diversification and emergence of host-driven adaptations in proteins involved in immune evasion and replication. Therefore, introduction of POWV L1 into distinct tick species leads to emergence of tick-specific substitutions that may contribute to increased viral fitness (1). Additionally, these changes could be tick specific with distinct adaptation of POWV occurring dependent on tick species (2).These studies will help further our understanding of POWV adaptation to novel hosts and host-specific, genetic correlates that influence viral fitness. Additionally, these studies will contribute to the poorly understood tick antiviral responses to POWV infection. Overall, this work may act as a model for tickborne virus spread and establishment in new transmission cycles.

95% of the universe is believed to be comprised of so-called dark matter and dark energy, which have only ever been observed indirectly. One way of studying the fundamental properties of these dark components is through statistical measurements on large cosmic surveys of billions of galaxies. My research involves measuring and correcting for systematic biases in the newest such survey, the Rubin Observatory LSST, which will begin observing the sky in 2025. Accounting for these biases will contribute to the field of precision observational cosmology, which will provide a test of general relativity and enhance our understanding of the universe.

Periodontitis is a chronic inflammatory condition affecting about 47% of the population in the United States and is a potential risk factor for systemic co-morbidities as individuals with poor oral hygiene are 30% more likely to develop adverse cardiovascular events. Periodontitis is characterized by a dysbiotic subgingival microbial community with a high abundance of spirochetes, including Treponema denticola, together with local gingival inflammation, allowing for the transmigration of bacterial products and inflammatory mediators to the circulation. Inflammation-driven endothelial cell (EC) dysfunction leading to vascular permeability is a critical factor in initial cardiovascular disease (CVD) pathogenesis. Emerging evidence has implicated neutrophil infiltration in the early stages of endothelial cell dysfunction and progression of CVD. T. denticola has been detected in human atheromas, along with active propagation to the aortic tissue with neutrophil-rich immune cell recruitment in murine models of oral T. denticola infection. T. denticola and T. denticola-produced outer-membrane vesicles (OMVs) dysregulate neutrophil function along with promoting distinct neutrophil cytokine profiles characterized by the secretion of the understudied IL-6 family cytokine, Oncostatin M (OSM). OSM is elevated locally in the periodontal pocket and systemically during PD. Furthermore, OSM has also been implicated in cardiovascular pathologies and may support endothelial changes in atheroprone environments. Effective cellular junctions and functional signaling are required to maintain vascular endothelial integrity. Disruption of these molecular complexes and cellular activation are early hallmarks of endothelial dysfunction leading to atheroma development. A gap in knowledge remains in the mechanistic understanding of how neutrophil cytokine signaling and spirochete interactions orchestrate initial endothelial changes to promote pathology. This project aims to characterize oral bacteria and inflammatory cytokine signaling processes that promote aortic endothelial cell dysfunction. We believe that Td-mediated interactions and neutrophil-derived OSM signaling support endothelial junctional integrity and inflammatory signaling pathways to promote a pro-atheroma environment. I will test our hypothesis using different methods. These include immunological assays, in vitro models, microscopy, flow cytometry, animal models, and microbiological techniques. Completing this project will provide valuable insight into the interactions of spirochetes, inflammatory cytokine signaling, and neutrophils to promote EC dysfunction, closing the gap of knowledge in PD association with CVDs, and may give an insight into which population may be at risk of developing CVD during PD.

Estrogen loss during aging is an inflammatory process that affects tissues throughout the body. Prebiotic dietary fibers are a promising treatment that act via the gut-immune connection. However, there is a gap in our understanding of how estrogen and prebiotics affect these systems, both separately and together. My research uses computational models to capture these processes and their effects on gut inflammation, an effort that is otherwise intractable by experimental research alone. Insights from these models will guide ongoing experimental efforts and improve our understanding of the complex processes of inflammation and aging.

After completing her master's degree in linguistics at the University at Buffalo, Sarah joined the psychology department (cognitive area). Her current research includes topics of memory and semantic categorization, as well as investigations into how various linguistic features spread across communities. Central to her research is the fact that language is both a social act and a cognitive process, and she strives to account for those factors in her work.

Mitochondrial diseases are common inherited metabolic diseases affecting an estimated 1 in 5,000 people. The Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes (MELAS) syndrome is the most prevalent mitochondrial disease; its cardinal symptom is stroke-like episodes (SLE). Currently there are no effective therapies for SLE. My research focus is on mitochondrial transfer between cells and how we can harness Extracellular Vesicles (EVs) to donate mitochondria to vascular cells. EVs are nanosized carriers that are constantly synthesized and circulate within our bodies, sort of like our very own nanobots! My advisor's lab studies mitochondrial biology using benchtop vascular models that replicate the human environment. Our study will bridge benchtop to clinical practice by applying our in vitro findings on EV-mediated mitochondrial transfer towards understanding SLE pathogenesis, potentially suggesting a new therapeutic strategy for MELAS patients.

My research focuses on understanding how mitochondrial protein import stress influences the activation of the myokines, or key signaling molecule involved in skeletal muscle function and metabolic regulation. By utilizing both animal and cellular models, I aim to uncover the molecular mechanisms linking mitochondrial dysfunction to myokine signaling and its downstream effects. These findings will provide critical insights into the cellular stress responses associated with mitochondrial disorders and their systemic impacts on muscle health and overall metabolism. Ultimately, these insights could pave the way for interventions that improve muscle function, prevent disease progression, and enhance the quality of life for patients affected by mitochondrial dysfunction.

I am a mechanical engineer who spent several years working in design and analysis for the manufacturing, aircraft, space, and defense industries before entering the University at Buffalo Engineering Education PhD program. My research interests include early career engineers working in industry and their experiences with developmental relationships such as mentorship. I plan to develop tools from my research that can be used to teach students nearing graduation or early career engineers working in industry how to be intentional in their developmental relationship selection and cultivation for successful, satisfying careers. As a research assistant, I have also had the opportunity to work on projects that study the influences of affect, or emotions, on the engineering identity of first- and second-year students and engineering judgment during open ended modeling problems (OEMPs).

I research the physical properties of transcription. Specifically, I am interested in how the liquid-liquid phase separation of transcription associated proteins can be promoted or modulated. Liquid-liquid phase separation is a process much like what we observe when we mix oil and water. The oil separates into droplets within the water and forms distinct oil droplets. This process also occurs in your cells, and is the driving force behind many biological processes and disease. My research aims to provide a fundamental basis for the process of liquid-liquid phase separation and transcription. Understanding this process could lead to the development of drugs or other intervention strategies to treat cancers, neurological diseases, or age related complications.

Sepsis is a complex, heterogenous disease that results in a dysregulated immune response. It is associated with hyperinflammation, known as "cytokine storm", causing multiple organ failure and death, as well as simultaneous compensatory anti-inflammatory responses, leading to immune suppression by abundant anti- inflammatory cytokine secretion and immune cell death. Sepsis survivors may suffer chronic critical illness, known as Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PIICS), leading to serious complications and later death. In sepsis triggered by Gram negative bacterial infection, lipopolysaccharide (LPS), an endotoxin, promotes uncontrolled inflammation and cytokine storm through TLR4 activation. It can also trigger significant cell-lytic death of immune cells, known as inflammasome-mediated pyroptosis, which promotes further immune response for pathogen clearance. However, excessive pyroptosis during hyperinflammation results in prolonged immunosuppression and increased organ dysfunction, contributing to the development of PIICS. Currently, there are no available therapies to directly target both excessive cytokine production and immune cell pyroptosis in sepsis, a gap we will fill with our novel immune modulating itaconate- loaded telodendrimer nanoparticles (ITA:TD NP). In Luo lab, we have developed a series of bioactive immune modulating TD nanodrugs, which mimic the molecular pattern of LPS (multivalent charge and fatty acid tails). As a result, the optimized TD nanodrug attenuates LPS-induced inflammation via competitive binding with TLR4. Itaconate, a metabolite produced by activated macrophages, is known inhibit immune cell pyroptosis. Unfortunately, the in vivo application of ITA is limited by the unfavorable pharmacokinetic properties and cytotoxicity. Our LPS-antagonizing TD nanodrug can form a nanocarrier for efficient encapsulation of itaconate, thus to synergize the in vivo application and immune modulation. We hypothesize that concurrent inhibition of LPS signals, inflammasome activation, and membrane pore formation will effectively control both early phase hyperinflammation and pyroptosis-mediated later stage PIICS for improved sepsis treatment. Preliminary results indicate a survival benefit with ITA:TD NP treatment in septic mice induced by LPS and effective inhibition of both inflammation and pyroptosis. Thus, further studies to evaluate ITA:TD NP mechanism of action and efficacy to prevent both acute and late death in sepsis are needed. The aims of this study are: 1) investigate ITA:TD NP-mediated inhibition of hyperinflammation in sepsis, and 2) elucidate the protective effect of ITA:TD NP to prevent PIICS-associated morbidity and mortality. Results from these studies will provide insight to the mechanism of action of ITA:TD NP and its therapeutic potential for concurrent treatment of hyperinflammation and prevention of PIICS in sepsis to reduce mortality and fill this critical gap in patient care.

Alzheimer's disease (AD), the most common form of dementia, is diagnosable based on a combination of clinical and biological indicators that generally exhibit consistency across patients, such as cognitive decline and memory impairment; however, substantial variability in the onset, progression, and manifestation of the disease among individuals poses significant challenges for early detection and management of AD. My proposed project, "Advancing Alzheimer's Diagnosis: MRI-Based Predictive Modeling of Alzheimer's Disease Molecular Subtypes," aims to establish a computational model that can accurately identify distinct subgroups among AD patients based on gene activity patterns in the brain, all achieved non-invasively by neuroimaging techniques.

Immunotherapies can improve survival outcomes across multiple solid cancer types. However, in several cancers, such as triple-negative breast cancer (TNBC), immunotherapies either achieve modest benefits or are effective in only some subsets of patients. Therefore, there are many barriers which limit therapeutic efficacy, including the immune suppressive tumor microenvironment (TME). One prominent suppressive cell population of the TME includes myeloid-derived suppressor cells (MDSCs). MDSCs are immature immune cells that inhibit the effector functions of T cells, which are responsible for targeting cancer cells. To overcome the burden of MDSCs, our laboratory developed a novel approach to target MDSC development in the bone marrow and bolster therapeutic activity. We identified an intracellular pathway, specifically nucleotide synthesis, which is upregulated in MDSCs. When we inhibit nucleotide production, MDSC suppressive function is significantly reduced. Additionally, we observed improved therapeutic efficacy through a reduction in both TNBC primary tumor growth and tumor progression to other organs. Therefore, our interest lies in the central idea that nucleotide blockade reprograms MDSCs by lowering their suppressive activity and that MDSC mitigation sensitizes TNBC to novel immunotherapy regimens. New advances gained from this research have the potential to inform the clinical design of novel combination immunotherapies and enhance standard-of-care treatment.

Mesenchymal epithelial transition factor (c-MET/MET), a receptor tyrosine kinase, is an oncogenic driver in numerous tumor types, notably non-small cell lung cancer (NSCLC) and melanomas. On-target mutations drive resistance against small-molecule MET inhibitors. I aim to comprehensively profile the selectivity of clinically approved MET inhibitors against MET mutants, and determine combinations of inhibitors that maximize on-target activity and reduce off-target effects. This project serves as a model for the rational design of drug combinations to overcome on-target resistance mutations in human cancers. 

A quarter of the global population is infected with Mycobacterium tuberculosis, the causative agent of tuberculosis (TB). Mycobacteria are intrinsically resistant to antibiotic, immune, and environmental stressors, in part due to the unique structure and composition of the mycobacterial cell envelope. Studies on lipid extracts, membrane fragments, and model membranes have led to a dominant model in which the mycobacterial cell envelope has exceptionally low fluidity, which hinders the entry of antibiotics and other stressors. However, there are few reports on membrane fluidity in the complex context of the intact cell envelope, despite the fact that (1) membrane fluidity affects essential cellular processes, and (2) all living organisms adapt to changes in their environment to maintain their membrane fluidity. To address this gap in knowledge, we optimized an imaging-based method for measuring cell envelope fluidity directly in live mycobacteria using an environmentally-sensitive fluorescent membrane probe. Our approach labels the cell envelope of diverse mycobacterial species, including M. smegmatis, M. abscessus, and M. tuberculosis. By characterizing patterns of fluidity we found that cell envelope fluidity varies greatly within and between cells. Mycobacteria growth asymmetrically to yield daughter cells with different sizes and growth rates, and the resulting heterogeneity is thought to promote survival, e.g., in response to antibiotics. Consistent with this asymmetry, we observed that cell envelope fluidity is not equal in daughter cells upon division, and this heterogeneity is reduced in a mutant with decreased asymmetric polar growth. We also found that membrane fluidity differs at the cell poles vs. sidewalls, and this difference is altered by chemical and genetic perturbations. In ongoing work, we are continuing to characterize cell envelope fluidity as a function of such perturbations, towards assessing how membrane properties correlate with antibiotic susceptibility and clinical outcomes. Ultimately, understanding the biophysical characteristics of the cell envelope and the changes that occur after perturbation may uncover mechanisms underlying antibiotic susceptibility and essential biological processes.

The CRISPR/Cas system is well-known within the scientific community as a gene editing tool. One of its other uses, however, is as a biosensor which operates with single-nucleotide specificity. My research focuses on developing a novel class of sensors that combine optical sensors with CRISPR/Cas technology to detect pathogens and small-molecule contaminants rapidly, sensitively, and with low cost and minimal instrumentation. In doing this, they hope to create a sensing option which can be deployed rapidly and with high confidence to assess contaminant exposure risks and environmental transport, especially in resource-limited areas.

Linguistics is driven by two central questions: what is the character of linguistic knowledge, and how is it acquired from limited data? My research focuses on expanding our understanding of learning-theoretic elements of natural language by: 1) designing learning algorithms for linguistically relevant formal language classes which can learn from small data, 2) creating efficient software implementations of these algorithms which can be run on naturalistic datasets, and 3) investigating the impact of representational choices and abductive principles to ultimately refine these algorithms and hone in on learning approaches which resemble human behavior. Finding algorithms that can learn linguistic structures will impact many kinds of language technologies. Although neural approaches have had great success recently in many natural language applications, these models require massive amounts of data to train. Because of this, they often fail to extend to lower-resource languages where such volumes of data are not available. By developing learning algorithms which are more human-like and less resource-intensive, many possibilities will open up for extending language technologies to these under-resourced languages. Explicit learning algorithms also have the advantage that they are entirely explainable and have provable learning outcomes, reducing the kinds of hidden biases which often plague neural models.

Urinary tract infections are among the most common healthcare associated infection, of which up to 80% are due to a urinary catheter. Catheter associated urinary tract infections (CAUTIs) are linked to an increased risk of recurrent infection, renal damage, and secondary bacteremia. These types of infections have been found to be largely polymicrobial, with 3 or more species of bacteria present during both asymptomatic colonization and active infection. We and others have shown that the most common partners are Proteus mirabilis (Pm), Escherichia  coli (Ec), and Enterococcus faecalis (Ef),.all of which form robust catheter biofilms that contribute to colonization of the catheterized bladder, antibiotic resistance, and pathogenesis. My work aims to identity key interactions that occur during polymicrobial CAUTI that can be used as possible targets in treating these type of infections. 

My current research aims to evaluate how herpetofauna are exposed to anthropogenic pollutants (mercury and PFAS) and how within-species trait variation affects exposure. To address this goal I am conducting two research projects. The first project investigates how habitat characteristics and biological parameters impact mercury exposure in common snapping turtles and easter painted turtles in Central New York. My second project investigates PFAS exposure and what drives PFAS exposure in wild amphibian populations in the Adirondack Mountains.  This research is important because reptiles and amphibians play integral parts in food webs and contaminants can easily be transferred between organisms via diet. Evaluating contaminant exposure in these species can help inform us of the risks that these contaminants pose on food webs. 

Understanding the underlying mechanisms of psychological problems can assist in the identification and treatment of mental health conditions. I utilize neuroscience methods to identify neural markers of developmental trajectories of psychopathology risk and emergence. Further, I am interested in understanding neurobiological convergence and divergence across psychiatric disorders.

Researchers tend to conduct studies using participant samples that are homogenous and not representative of the general public. This lack of participant diversity can cause scientists to question whether research results can generalize to other populations. However, it has not yet been studied whether members of the general public consider whether research findings generalize to themselves. My research is developing a novel measurement tool to evaluate how lay people perceive research as applying to their own lives. Understanding this phenomenon may help explain why some members of underrepresented populations may be less likely to follow research-based health recommendations, which may contribute to health disparities.

My research will be investigating how various environmental conditions are driving differences in the two main color morphs of the Eastern red-backed salamander (Plethodon cinereus). My research will involve morphology, diet, and coloration as key aspects of divergence between these two color morphs. This research will add to the ever growing body of literature aimed at figuring out what drives these morph differences, as well as potentially provide insight in to how environmental disturbance may be affecting salamander populations in the Northeast.