Experience 1: Big IDEAS Lab, Duke University
Dates: January 2022 – present (500+ hours)
Advisors: Dr. Jessilyn Dunn, Mr. Mobashir Shandhi, Mr. Bill Chen
Since my freshman year, I have worked on a variety of projects as part of the Big IDEAS lab. During the Fall 2023 semester, I collaborated with other graduate students in using longitudinal EEG recordings to predict optimal or poor neurological outcomes (particularly Cerebral Performance Category scores) in comatose patients after cardiac arrest. To optimize the classifier, I used various machine learning models to accurately predict these neurological outcomes from clinical parameters in patient data (such as return of spontaneous circulation and out-of-hospital cardiac arrest). I was a co-author of an abstract and associated manuscript of a poster presentation at 2023 Computing in Cardiology Conference (https://cinc.org/2023/Program/accepted/434.html).
I have also used bash scripting, Python, and RStudio to investigate reporting of demographic factors of patients enrolled in nearly two hundred studies categorized into various clinical areas of interest (such as cardiovascular studies or neuroelectric studies) on the bio-signals database, PhysioNet. I also performed analyses on the geographical locations where various biosignal studies were conducted. I am a co-author on the drafting on this manuscript (which is in preparation to submit for publication).
I was also involved in the Duke Health Listens Study, where I used Python and machine learning models to investigate the assessment of ownership of smart devices and the acceptability of digital health data sharing based on survey responses from patients. I am a co-author of the manuscript in submission for publication (link to preprint: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4546780).
I am currently continuing my research with the Big IDEAS lab through the Pratt Research Fellowship (4 semesters of Independent Study). My current project aims to improve accuracy in direct detection and classification of cardiac arrhythmias using novel rest and stress ECG data from Project Baseline, a clinical study spearheaded by Verily (a Google Lifesciences Company). Link: https://drive.google.com/file/d/1uIwssvXQBYZ1P3JPnDR-Mw-PH9iWVphW/view?usp=sharing
Experience 2: American College of Cardiology Young Scholars Program
Dates: February 2022 – October 2023 (200 hours)
I was selected as a Young Scholar (from over 220 applicants) for the American College of Cardiology (ACC) yearlong fellowship. I was invited to lectures by distinguished cardiologists from teaching hospitals (such as Brigham and Women’s Hospital) in various fields (interventional cardiology, electrophysiology, and cardio-obstetrics). I independently developed machine learning and deep learning models to predict death events resulting from heart failure (HF) and utilized bioinformatics to identify differentially-expressed genes and potential therapeutic targets in HF in independent project I was the sole author of abstract accepted for poster presentation at the American College of Cardiology 2023 Conference in New Orleans (Link: https://docs.google.com/presentation/d/172nIvI_MGZntcLE5pqX7J2HnVMukJ8c7/edit?usp=sharing&ouid=108768834133255288738&rtpof=true&sd=true)
