We’re continuing to feature our 1st year doctoral students! We hope everyone is able to get to know this talented and diverse new group of students.
Biostatisticians work at the interface of statistics with public health and solve important design and analysis questions that are critical to the success of scientific research. This exciting field, which can confront modern day diseases by using quantitative skills and by turning data into knowledge, led me to complete a Master of Science in Biostatistics at Yale School of Public Health after receiving a Bachelor’s degree with a double major in mathematics and statistics.
While working on several projects, I especially enjoyed and appreciated an active interdisciplinary and collaborative research environment which encourages people with different areas of expertise to analyze the same problem with unique perspectives and to form creative solutions. At the National Institute of Child Health and Human Development (NICHD), I investigated the effect of maternal and parental serum concentrations of persistent organic pollutants on birth size of offspring. At Yale School of Medicine, I worked on Dirichlet Process Gaussian Mixture Model and also developed penalized model-based clustering model to identify disease subtypes using gene expression data.
At Harvard, I am excited to enrich my skills with a strong background in mathematics and statistics, and an understanding of the biology and dynamics behind statistical modeling.
My name is Matthew Quinn and I am originally from Pawtucket, Rhode Island. I graduated from Williams College this past spring where I double majored in mathematics and economics. During my time in college, I participated in two REUs pertaining to statistics. The first was at Worcester Polytechnic Institute where I worked with other students on classifying images of carbon nanotubes using machine learning methods. The second was atHarvard where I worked with Professor Tingley in the Government Department and other undergraduate students on developing statistical models that helped recommend material from HarvardX with which Harvard professors could supplement their courses. In my last year of college, I also completed an honors thesis in mathematics on computing the entropy associated with certain measure-preserving transformations, namely the odometer and Hajian-Kakutani Skyscraper transformations.
I’m very interested in computational statistics and machine learning methods. I hope to explore a range of public health applications but am especially interested in getting to work with large collections of health care data and medical records. Outside of school, I enjoy exercising, playing video games, and listening to music.