Trainees

Joe Cristiano

Advisor: Erin Sparks

Research Topic: Brace root mechanics in cereal crops

My research aims to understand how corn brace roots develop and function at the molecular, morphological, and mechanical levels. To achieve this, I will be developing new tools that can be applied broadly across living systems. I will use a combination of transcriptomic analysis, automated phenotyping, image analysis and mechanized testing methods. I am passionate about my research topic and its potential applications for improving living systems.

Amirah Ellis-Gilliam

Advisor: Jennifer Horney

Research Topic: Social Determinants of Health Impact on Long COVID

I am currently working on my dissertation and will be exploring the N3C (National COVID Cohort Collaborative) data enclave to study the impact of social determinants on individuals with long COVID. Since enrolling in the Bioinformatics Data Science PhD program, I have also served as principal investigator and completed a study centered on documenting long-term experiences with COVID-19. I went through the process of submitting documents and a protocol to the IRB, once complete I built a survey in RedCap adapted from the All of Us Research Program Participant Experience (COPE) Survey and The Basics Survey. I distributed the survey via RedCap to members of COVID Survivors for Change and the COVID Institute: Long COVID Treatments & Success Stories Facebook group and documented the results.

Julie Nguyen

Advisor: Xin Lu

Research Topic: Mechanisms of resveratrol’s chondroprotective function in osteoarthritis initiation and progression

My research project aims to understand the molecular mechanisms of resveratrol’s’ chondroprotective function and improve its effectiveness in OA treatment. Osteoarthritis (OA) is a disease characterized by the degradation of articular cartilage extracellular matrix (ECM) involving metalloproteinases (MPs). ECM degradation is initiated and exacerbated by joint inflammation. Resveratrol, a natural compound found in berries and red grapes, has shown potential in protecting cartilage in animal models of OA due to its anti-inflammatory properties.

Nicholas Rafailidis

Advisor: Jason Gleghorn

Research Topic: Computational protein design using protein language models for improved protein annotation

My research focuses on enhancing protein language models by experimenting with fine-tuning methods, such as Low-Rank Adaptation (LoRA), and designing novel pre-training tasks. The goal is to improve model performance in predicting protein function, cellular localization, and gene ontology annotations, which are crucial for understanding biological processes and disease mechanisms. This work addresses current limitations in computational biology by providing more accurate tools for protein analysis, potentially accelerating drug discovery and advancing personalized medicine. Since beginning my Ph.D. studies, I have contributed as a co author to a paper: L Hallee, N Rafailidis, C Horger, D Hong, JP Gleghorn (2024) Annotation Vocabulary (Might Be) All You Need, BioRxiv DOI 2024.07.30.605924.

Apurv Srivastav

Advisor: Abhyudai Singh

Research Topic: Examining the lineages/barcodes of cells and find the probability of cells being in different states after a specific time period

I have begun working on research with Dr. Singh, primarily on 2 different projects. One involves finding the noise from gene expression levels in cells across different lineages while the other is a benchmarking project involving the probability of a cell transitioning into different states after a certain amount of time. I plan to take a few more courses next semester and delve even further into the research I am working on with Dr. Singh.

Esther Weyer

Advisor: Vincenzo Ellis

Research Topic: Avian malaria phylogenetics

My planned research focuses on avian malaria phylogenetics and genetics. Avian malaria consists of several lineages, infecting birds all over the world. Some of these lineages have a very narrow host range while others are super generalists with >150 host species. It is a particular conservation concern for previously unexposed host populations, such as the native bird species in Hawaii. By analyzing sequencing data from samples across a diverse geographic range, I aim to determine a species tree for the lineages and test for introgression or rearrangement events between them. I am interested in identifying genomic regions that differ between closely related lineages with alternative host specificity strategies. I also hope to utilize the isolation of lineages discovered in Hawaii to test for selective pressures and potentially dating analysis to estimate the evolutionary age of malaria. This year, I had a publication on modeling host population dynamics in a fluctuating selective pressure environment (https://doi.org/10.1073/pnas.2322371121). During my time as a student at UD, I have served as both treasurer –helping to organize and assist with hosting a Genomics Data Carpentry workshop – and continue to serve as the vice president for the Bioinformatics Student Association.

Yasaman Moghadamnia

Advisor: Ryan Zurakowski & Jason Gleghorn

Research Topic: Mathematical modeling of antiretroviral drugs’ diffusion into the lymph nodes

The motivation for my research is modeling the drug distribution in HIV-infected tissues. Many studies have shown that the antiretroviral drugs distribute heterogeneously in lymph nodes (LNs) and cannot penetrate sufficiently to suppress the virus.

Kyle Regan

Advisor: Gonzalo Arce

Research Topic: Biological threat detection and spectral enhancement of aerosol MALDI-ToF mass spectrometry data

My research is focused on Matrix Assisted Laser Desorption and Ionization Time-of-Flight Mass Spectrometry (MALDI-ToF-MS) specifically for the portable single particle aerosol mass spectrometer developed by Zeteo Tech Inc. The motivation behind the portable mass spectrometer is the ability to deploy it in highly populated areas to detect potential biological threats in the air. In the future, I plan to develop machine learning models, specifically generative models, to generate synthetic data.

Anthony Shepherd

Advisor: Thomas Hanson

Research Topic: Leveraging UAV Hyper-spectral imaging and microbial networks to predict toxic CyanoHABs

Under the guidance of Dr. Thomas Hanson, my PhD research is focused on the prevalent issue of toxic Cyanobacterial Harmful Algal Blooms (CyanoHABs) in aquatic systems, aiming to proactively manage their occurrences. By utilizing advanced technologies like UAV hyper-spectral imaging combined with environmental metrics and microbial interactions, my research aims to develop an integrated predictive model to forecast shifts in microbial community compositions. This model will answer critical questions about the specific triggers and patterns of CyanoHABs in selected Delaware sites, including the Delaware River at Battery Park, Lums Pond, Silver Lake, Coursey Pond, and Trap Pond.

Patrick Dopler

Advisor: Melinda Duncan

Research Topic: Bioinformatic predictions of cataract mechanisms

My research broadly focuses transcriptomic analysis of the lens with various disease and injury states resulting in cataracts. Primarily, this focuses on the fibrotic response in lens epithelial cells (LECs) and how it alters ECM expression causing structural deformation (posterior capsular opacification (PCO) and Soemmering’s ring formation) after traumatic stress (post cataract surgery).

Jonathan Hicks

Advisor: Mona Batish & Robert Akins

Research Topic: Machine learning of medical diagnoses

My research focuses on machine learning algorithms in medicine. Primarily, I have worked on cerebral palsy and epilepsy. These two diseases are interrelated, and current literature is pointing to a potential set of biomarkers in the methylome of individuals afflicted by these diseases.

Rachel Keown

Advisor: Shawn Polson

Research Topic: Viral replication protein biochemistry: genotype to phenotype

My current research aims to investigate the link between protein biochemistry and viral phenotypes. Environmental bacteriophage (phage) are highly abundant and diverse, making them a genetic goldmine in aquatic environments. The working hypothesis driving my work is that a single amino acid substitution in the DNA polymerase I protein can be indicative of the replication speed, and by extension the lifecycle characteristics of a phage.

Joel Turk

Advisor: Joshua Neunuebel

Research Topic: Unsupervised machine learning approach to segment mouse social behavior

Throughout the animal kingdom, diverse repertoires of behaviors are exhibited. Identifying these behaviors is crucial for deciphering the relationship between social behavior and the neural circuits that encode this information, which is a main area of focus for our lab. My focus is on developing an unsupervised machine-learning approach that applies a Self-Organizing Map to group similar patterns in movement and social interaction.

Active Trainees, Currently Appointed