Frank D'Agostino
I am a Harvard University undergraduate student studying Applied Mathematics with a focus on Data Science and a secondary in Computer Science. I have previous Data Science experience at places like Meta (formerly Facebook) and Georgia Tech, as well as research experience with Harvard School of Arts and Sciences, Harvard Medical School, Harvard School of Engineering and Applied Sciences, UMass Amherst Medical School, and Northeastern University. I am a hardworking and motivated individual who is committed to learning new things and contributing to things I care about. Research is an important part of my life, and I hope my website allows you to learn more about me and my experiences. You can click on the buttons or pictures below to learn more about these selected projects/experiences. Furthermore, you can navigate to learn more about me by using the buttons at the top right hand corner.
Significant Experiences
Citadel Data Open Eastern Regional Datathon
Placed 1st place at the Citadel Eastern Regional Datathon. Competed against 15 teams to analyze a diabetes dataset containing different patients and their health outcomes over several doctor visits. My team and I are going on to compete in the Data Open Championships in December 2022. Our project involved creating a hierarchical linear model to predict heart failure based on different drug characteristics that patients were prescribed. The team and I also applied a synthetic control model using a matching algorithm among all the patients, controlling for other drugs they may have been taking. We were able to show that glipizide, a common diabetes medication, had a causal link to increasing the risk of heart failure.
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Publication: Computational Genomics
Higgins, D. P., Weisman, C. M., Lui, D. S., D’Agostino, F., & Walker, A. K. (2022). Defining characteristics and conservation of poorly annotated genes in Caenorhabditis elegans using WormCat 2.0. Genetics.
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Publication: Neurology & Clinical Trials
Ai, M., Morris, T. P., Ordway, C., Quinoñez, E., D'Agostino, F., … Geddes, M. (2021). The daily activity study of health (DASH): A pilot randomized controlled trial to enhance physical activity in sedentary older adults. Contemporary Clinical Trials, 106405.
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Citadel Data Open Summer Invitational Datathon
Three teammates and I competed at the Citadel Summer Invitational Datathon. As competitors, we faced teams of other top CS/data science students in a week long competition, creating an insightful report and analyses on the topic of Airbnb rental data in the United States Southern region. Using hypothesis testing, time series analysis, feature engineering, exploratory factor analysis, and machine learning, we made several insights into the differences in communities and its impact on Airbnb rental home prices. We also revealed possible confounding variables of pricing such as the racial and socioeconomic demographics of an area.
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Harvard College iGEM Project 2020
In this project, the Harvard College iGEM Team worked on MOTbox, a machine learning and DNA origami SARS-CoV-2 therapeutic. Using molecular dynamics and machine learning algorithms, we computationally modeled and characterized a platform technology for expeditious antibody therapeutics for viruses in a pandemic scenario. Our project was able to achieve a Gold Medal and to be nominated for "Best Model' at the annual iGEM competition with over 150+ institutions worldwide.
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Predicting and Modeling Sea Level Rise in the Barnegat Bay
In this project, I utilized Python and Aquaveo SMS in order to predict sea level rise. First, I created a 3-dimensional mesh using elevation data points from the USGS. Using that model, I simulated water circulation patterns and the effects of sea level rise on erosion, water velocity, and flooding. I was able to place 1st at the Jersey Shore Science Fair, 1st at the Delaware Valley Science Fair, and 1st at the NJDEP Geographic Information System (GIS) conference.
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Birthweight Data Analysis
For the final for my Mathematics for Statistics, Computation, and Data Science course, I worked with another class member to analyze a birthweight dataset from the CDC and seek out statistical relationships using the programming language R. Using a series of permutation tests, t-tests, and visualizations, we were able to draw several conclusions regarding the dataset. For example, we found that mothers who smoked during pregnancy had a significantly raised chance of birthing a child that had a low birthweight.
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Monmouth University Summer Research Program
During the summer of 2018, I had the opportunity to work as a Research Associate for Professor Katie Gatto at Monmouth University as part of the School of Science Summer Research Program. Funded by Johnson and Johnson and the Department of Computer Science and Software Engineering, I worked with four other students to create a virtual reality (VR) simulation of eutrophication in the Barnegat Bay. The simulation was played on the Oculus Rift and Google Daydream.
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NJ Key Club District Governor
Key Club is one of the largest student-led service organizations in the world. During the annual District Convention, I was elected to serve as District Governor of the state of New Jersey for the 2018 to 2019 service year. As Governor, I was responsible for leading the Executive Board and 22 lieutenant governors and representing New Jersey on the Key Club International Council. I planned and ran several events, such as Fall Rally which had over 1,500 people.
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FIRST Robotics
During the 2018 to 2019 competition season, I was the lead programmer for the Marine Academy of Technology and Environmental Science (MATES) FIRST Robotics team. We competed at six competitions across the season and made our way to states. Coding the robot using Java and an Android phone for the motor controller, the team utilized advanced circuitry, engineering, and programming techniques to participate in the advanced competition.
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