While an normal semen sample contains hundreds of millions of sperm, men with azospermia have no sperm seen and azospermia represents an important cause of infertility. Men with azospermia typically have to use donor sperm in order to have a child. However, extended manual search of semen samples in men with azospermia reveal single-digit numbers of sperm in ~80% of cases. These few sperm are sufficient to fertility eggs in the IVF lab and allow the men to be biological fathers. Unfortunately, the manual search for sperm is only performed in a couple of sites in the world because of the enormous cost associated with having a specially trained andrologist search a sperm sample for roughly 8 hours. In this project, we aim to apply the semen sample to the custom designed microfluidics chip at a fixed flow rate. Next, several thousands of images will be captured at frame rate ranging between 4,000-40,000 fps using a high definition microscope camera as the sample flows through the microfluidics device. We will then build a training dataset of images with and without the rare cells of interest. These training datasets will be used to build an artificial intelligence (AI) model with high accuracy to detect rare cells of interest. The model will be tested for its accuracy on the ‘true’ sample containing rare cells. The Data Science Institute Scholar is expected to have basic programming skills (preferably in Python). Thus, if successful, there will literally be countless babies born as a result of this project.

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We performed a prospective, non-randomized trial of patients undergoing surgical management of gastrointestinal, gynecologic, and urologic malignancies between 2017 and 2021 . Participants were instructed to take immunonutrition formula three times daily for five days before and after surgery once cleared by the surgeon for clear liquid diet. The dietician tracked their adherence. We are looking to perform statistical analysis to look at the impact of immunonutrition intake on unadjusted and adjusted postoperative surgical outcomes.

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The Center for Behavioral Cardiovascular Health has been at the forefront of developing virtual interventions (e.g., home BP telemonitoring, iHeart enhanced depression screening app, and COVID remote care program) to improve the management of hypertension, depression, and COVID. We are looking for motivated mentees to help us organize and analyze a wide-breadth of patient data from the Columbia-New York Presbyterian data warehouse to understand the impact of the interventions on patient outcomes. The mentee would work alongside highly experienced biostatisticians and our Center’s professionalized data team managers. The mentee will develop expertise in analyzing health system data and will inform decisions to modify, sustain, or de-implement existing programs.

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The aims of this project are two-fold. We seek to: 1) better understand the associations of discrimination with sleep and cardiovascular health among LGBTQ+ adults, and 2) compare various unsupervised learning techniques on their performance on clustering sleep patterns using actigraphy data. We have multiple datasets for conducting our analyses to delineate the pattern of associations between sleep and other variables. We will also explore various modeling techniques to quantify the within- and between-person variability in sleep patterns. We aim to complete multiple analyses in the Fall and Spring semesters to develop conference abstracts and manuscripts to be submitted in Spring 2022. Since our DSI seed grant is still ongoing, during the Fall semester the Scholar will work on data wrangling and analyses, in addition to data management from existing data collected from our Precision in Symptom Self-Management pilot studies. In January 2022, we will begin analyzing data from our DSI seed grant. We hope to collaborate with a student who is interested in machine learning, sleep, and/or health disparities. The Scholar will have the opportunity to contribute to all of these aspects of the project. The Scholar will also be able to propose and lead additional analyses.

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This project is a collaboration between Columbia scholars and students and leadership at the Malcolm X & Dr. Betty Shabazz Center. The goal is to create programming and intellectual resources inspired by the legacy of Dr. Shabazz and Malcolm X through public events, workshops and data science activities. Part of this work will involve using computational methods to analyze existing publications around Malcolm X’s work, as well as develop a corpus of research materials by effectively digitizing the contents of the publicly-available FBI files about his life.

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The project will include conducting analyses on lifestyle behaviors, namely diet and sleep, in relation to cardiovascular health. Analyses will be completed within NHANES (National Health and Nutrition Examination Surveys). Analyses will also be completed using data from a 24/7 sleep and activity tracker in relation to blood pressure. The student will contribute to data management and statistical analyses and will be a co-author on manuscripts submitted for publication.

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We study how competing groups of regulators regulate the COI disclosure using the setting of the fairness opinion (“FO”) providers in mergers and acquisitions (“M&A”). In this setting, there are three types of regulators with cross-sectional variation in enforcement: the SEC, FINRA, and state courts. Though each group regulates the same disclosure, they all have authority over different regulatees: The SEC has authority over the managers who create the filings; FINRA has authority over the FO providers; and state courts have authority over the board of directors. We examine the effectiveness of each group’s enforcement and the effect of their interactions on COI disclosures. Our paper contributes to the literature on regulatory design for financial reporting disclosures, especially when the different regulatees need to coordinate information or when disclosures have negative implications.

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