My Story

As an undergrad, I studied both music and biomedical engineering as part of a double degree program. This is also where I had my first research experience in the Johns Hopkins University Applied Physics Lab (JHUAPL) CIRCUIT program. At JHU and APL, I had the opportunity to work in a variety of spaces ranging from nanoscale connectomics and precision medicine to neuromorphic computing and robotics, where I fell in love with research, machine learning, and neuroscience, and, most importantly, it’s where I developed a passion for mentorship and outreach. I began as a student and quickly transitioned to a leadership role, mentoring students and overseeing the first iteration of the high school version of CIRCUIT.

In August of 2021, I moved to Boston to pursue my PhD, supported by the Biomedical Informatics and Data Science Research Training (BIRT) fellowship. Here, I pursued rotations in labs in both the nanoscale connectomics and healthcare AI spaces ( Jeff Lichtman , Pranav Rajpurkar , Raj Manrai , Isaac Kohane ), where I worked on a variety of projects and even published a few papers. Ultimately, I chose to join the Manrai lab and focus on kidney function estimation, but I am grateful for the opportunities I've had to learn and grow in my undergraduate and rotation research experiences.

Previous Research Projects

Connectomics Annotation Metadata Standardization for Increased Accessibility and Queryability

In an effort to better understand structural organization and anatomy of nervous systems at nanoscale spatial resolution, researchers have collected increasingly large, even petascale, connectomics datasets. These datasets have the potential to form the basis for the next generation of brain atlases at submicron resolution. However, variability in data collection, annotation, and storage approaches limits effective comparative and secondary analysis. This project aims to provide design considerations for community-adopted standardized annotation format, develop an example database and API for storing and accessing the first nanoscale human connectomics dataset, and create a user-friendly web app for exploring and querying the dataset. Read More.

Predicting Physiological Deterioration and Mortality in Mechanically Ventilated ICU Patients

Previous work has demonstrated that the severity of lung damage in experimental models of ventilator-induced lung injury depend on the mechanical power of the ventilator. In this project, we build machine learning models (using the Phillips eICU Database) that leverage patient data (including demographics, ventilator settings, lab values, etc.) to predict whether a ventilated patient will deteriorate or expire. Such a model could be used to inform decisions about patient-specific ventilator settings or alert clinicians to deteriorating patients. Read More.

  • JHU Dept. of Medicine & Whiting School of Engineering (WSE) Research Retreat 2021
    WSE Research Award Finalist
  • American College of Chest Physicians Annual CHEST Conference 2021

Examining Plaque Toxicity to Synaptic Activity in an Alzheimer’s Disease Mouse Model

Alzheimer's is a disease that causes patients to progressively lose memory and cognitive function, typically living only 4-7 years after diagnosis due to limited treatment options. Biochemically, Alzheimer's is known to be associated with extracellular deposits of Amyloid-Beta peptide, or plaques, but causality and mechanism are not fully understood. Therefore, as part of a rotation in Jeff Lichtman's lab at Harvard, I studied the relationship between proximity to plaque material and synapse density in an effort to better understand whether plaque may be contributing to cognitive deficits through toxicity to synaptic activity. To do this, we ran synapse, membrane, and plaque detection models on an electron microscopy volume to obtain noisy annotations, filtered those annotations automatically, and estimated synapse distributions close to and far from a plaque of interest.

A Review of Recent Randomized Controlled Trials for Human-AI Collaboration in Healthcare

In recent years, the application of machine learning to healthcare has become an increasingly popular research area. Perhaps more exciting, however, is the recent upsurge in prospective studies, even randomized controlled trials, for AI-supported health tools, the lack of which has previously held research back from translation. In this project we aim to systematically determine the current state of the field through review of recent randomized controlled trials. This is a team project completed as part of a rotation project in Pranav Rajpurkar's lab.