About
Hi there! My name is Tommy, and I am a PhD candidate in population health at Northeastern University in Boston, Massachusetts. My research spans multiple disciplines such as environmental health, child and maternal health, COVID-19, occupational health and safety, firearm-related injury, and missing data.
For my dissertation I am developing a novel imputation approach for handling missing data.
I've had a number of different jobs since my undergraduate studies including billing specialist, full-stack web developer, database administrator, programmer/analyst, statistical programmer, and (now) part-time lecturer. I suppose one could say I am a 'jack of all trades.
Other than school and work, I enjoy running, biking, board games, Spotify new music Friday, and all things French Bulldogs.
Interests : machine learning, public health, biostatistics, epidemiology, data science, missing data, firearm injury
Research
My approach as an applied statistician is to think outside the box and find new and exciting ways to answer research questions. I am open to any and all domains of public health with a particular interest in machine learning. If you would like to collaborate on a project, please feel free to contact me. A few research projects I am working on are below:
Non-Fatal Firearm Injuries
Estimating the population distribution of non-fatal firearm injury intent in the United States.
Multiple Imputation by Super Learning (MISL)
Developing a novel imputation technique using the Super Learner framework.
Occupational Health and Safety
Determining the optimal weights to help score a pre-qualification survey for assessing safety on commercial construction sites.
Teaching
My teaching style focuses on learning statistical concepts and application through the use of programming. I believe students work best by example; almost all lectures are accompanied by some discussion or lab-based activity to further solidify concepts learned in the classroom.
Northeastern University
Instructor of Record: SLPA 6420, Practical Statistics for Speech-Language Pathology and Audiology
Summer 2019, Summer 2020, Summer 2021
Introduces concepts in data analysis using statistical methods with an overall focus on profession-specific application and interpretation. Introduces students to the STATA statistical software.
Instructor of Record: PHTH 2210, Foundations in Biostatistics
Fall 2020
Undergraduate course intended to introduce fundamental concepts of biostatistics. Through the programming language R, students learn to apply statistical thinking to make informed decisions related to practical problems across several health disciplines.
University of California, Berkeley
Teaching Assistant: PBHLTH 241, Categorical Data Analysis
Spring 2018
Biostatistical concepts and modeling relevant to the design and analysis of multifactor population-based cohort and case-control studies, including matching. Measures of association, causal inference, confounding interaction. Introduction to regression, including logistic regression.
Teaching Assistant: PBHLTH 142, Introduction to Probability and Statistics in Biology and Public Health
Fall 2016, Spring 2016, Fall 2017
Descriptive statistics, probability, probability distributions, point and interval estimation, hypothesis testing, chi-square, correlation and regression with biomedical applications.