Data Science and ML Experience

In my journey through Data Science and Machine Learning, each experience has been an exploration of a world where data transcends its informational role, evolving into a potent agent of transformative change. My path in this dynamic field has been intricately woven with my interests in medical and research applications, strategically integrating data analysis and machine learning to advance patient care, diagnostics, and treatment methodologies. This integration seamlessly aligns with my broader mission, where data-driven precision intersects with medical and research contexts to fuel innovative advancements in healthcare and scientific exploration.

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Independent Data Analysis

Seamlessly intertwining my medical and research expertise, I offer data analysis and programming as services where I leverage my skills to deliver insightful solutions and uncover meaningful insights from data. Whether it's developing algorithms to extract patterns, conducting statistical analyses to extract meaningful trends, or creating visualizations that bring data to life, my proficiency in data analysis and programming enables me to contribute effectively to projects that require rigorous analysis and interpretation.

January 2023 – Present

Political Data Coordinator at The Pivot Group - Remote | Washington, DC

As a Political Data Coordinator at The Pivot Group, I analyzed demographic and voting trends within various communities across the United States. This data was then used to guide effective campaign strategies for candidates participating in the 2022 Midterm Elections. In particular, I utilized tools such as Excel and IBM SPSS to store, preprocess, and statistically evaluate extensive lists of potential voters, strategically optimizing voting turnout for client candidates. In addition, I contributed to the assessment of the prediction accuracy of several machine learning models, including boosted decision trees and random forests, with regard to voters’ propensity and partisanship scores. My responsibilities extended beyond data analysis, embracing the realm of coordination as I employed a practical yet innovative approach by implementing a Python algorithm alongside a user-friendly Graphical User Interface. This dynamic solution streamlined the coordination of over 500 tasks, enhancing the efficiency and collaboration between different teams within the company. On a weekly basis, I also queried USPS databases to track over 10 million mail letters through printing facilities and monitor their reception by households. In tandem, I conducted several statistical analyses that unveiled vivid visualizations of demographics and voting history at the state, county, and district levels, empowering our clients with strategic insights that galvanized campaigns' directions.

June 2022 - November 2022

Research Assistant and Data Analyst in the Thakar Lab at the University of Rochester Medical Center - Rochester, NY

In my position at the Thakar lab, I built upon the work that I started in 2021 during a summer undergraduate research fellowship. Particularly, I led an independent research project where I constructed computational models of certain gene regulatory networks in B cells and investigated their response at the single-cell level in disease and health. This work entailed sourcing network topologies from scientific databases, procuring gene annotations via the Ensembl genome database project's Perl API, and utilizing R and Python to process intricate single-cell RNA expression datasets. Employing techniques like Principal Component Analysis (PCA) and clustering methods such as K-means, t-SNE, and UMAP, I studied gene expression patterns. I also used a genetic algorithm to simulate immune cell activation in various patient conditions including HIV, breast cancer, lung cancer, and COVID-19. Moreover, to validate the significance of my findings, I employed statistical tests such as Chi-squared, t-test, and ANOVA. My engagement extended beyond research as I actively participated in weekly lab meetings, where I honed my skills in analyzing scientific literature, exchanged research progress updates, and incorporated insights from fellow lab members' projects.

August 2021 - August 2022

Research Assistant at the University of Rochester Center for Advanced Brain Imaging and Neurophysiology - Rochester, NY

Under the guidance of Dr. Edward G. Freedman and a Ph.D. mentor, I engaged in hands-on research focused on identifying how the brain allocates cognitive resources and on finding biomarkers for Parkinson’s disease and attention-deficit/hyperactivity disorder. This experience encompassed a range of responsibilities, from meticulously preparing research sessions – involving the setup and synchronization of EEG equipment, OptiTrack motion capture systems, and a Pupil Labs eye tracker – to prepping participants for experiments by applying conductive gel, placing EEG electrodes, and arranging motion capture markers. Vigilantly monitoring EEG signals, I also swiftly addressed technical challenges to maintain data quality throughout the experiments' sessions. My role at the lab extended beyond data collection as I also performed data archiving and data analysis using MATLAB, Python, and C++. Equipped with a variety of computational skills, I set forth to independently integrate a wearable eye tracker into the lab and trained several classifiers including random forests, support vector machines, and recurrent neural networks to optimize the detection of different types of eye movements recorded. In addition, I wrote a coding pipeline that used this eye tracker and independent component analysis to accurately filter out eye movements and muscle contractions artifacts from EEG recordings. On a weekly basis, I participated in a journal club where I actively contributed to the lab's academic discourse as we critically evaluated recent scientific literature.

February 2020 - May 2022