Glioblastoma multiforme (GBM) is the most common malignant brain tumor and remains highly lethal due to its invasive nature and resistance to treatment. Its development of oxygen-poor regions reduces the effectiveness of radiation, limiting current therapeutic outcomes. We propose a new treatment strategy that delivers oxygen to tumor cells to enhance their sensitivity to radiation and improve patient prognosis.

Acute Kidney Injury (AKI) is a sudden loss of renal filtration capacity that leads to toxin and fluid accumulation. Patients who survive AKI-related hospitalizations are frequently readmitted, contributing to poor outcomes and healthcare strain. However, existing readmission risk tools are often based on small, single-center cohorts and lack generalizability.

To address this, we linked the Johns Hopkins Precision Medicine Analytics Platform with Kaiser Permanente Mid-Atlantic EHR data to construct a large, multi-center post-AKI cohort of over 60,000 admissions, integrating demographics, comorbidities, lab trends, vital signs, and other physiologic data. We trained a stacked gradient boosting ensemble that achieved an AUROC of ~0.65 for predicting 90-day all-cause readmission—outperforming logistic regression and single-model baselines.

Systematic feature analysis identified dynamic vital signs and bedside physiologic measurements as key predictors, supporting the development of a concise, clinically actionable risk score to identify high-risk patients and guide targeted post-discharge interventions.

DnATA is a DNA-based data storage platform designed to address the growing need for sustainable and durable data archiving. Our system uses a computational pipeline to convert digital files into DNA sequences, which are then stored and replicated inside bacterial cells. Leveraging natural bacterial replication reduces reliance on energy-intensive, short-lived conventional storage devices. By combining principles from synthetic biology, DnATA explores a novel biological approach to long-term archival data storage. While still in early stages, our work demonstrates the feasibility of encoding, storing, and retrieving digital information from cells, thereby opening new possibilities for secure, low-maintenance archival solutions.

Human trafficking remains one of the most prevalent humanitarian issues of our time, with millions of individuals trafficked across state, country, and continental lines each year. One of the main issues with tackling trafficking is the lack of data available to us that can help shed light into where and when trafficking occurs.

Our project builds upon the foundation provided by the largest anonymized dataset of human trafficking data available–the global synthetic dataset from the Counter Trafficking Data Collaborative. We apply statistical methods to impute missing values in this data, and then conduct statistical analysis to find correlations between different data trends over time. Finally, we utilize a large language model to provide these trends with the appropriate historical / geopolitical context. Through this effort, we hope to identify and enhance our understanding of the factors that drive trafficking in order to help combat it more effectively.