Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
talks
Talk on Dirichlet Process Mixture Modeling
Published:
Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
Repurposing insurance claims data to examine drug efficacy
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Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
Modernizing risk adjustment for assessing hospital surgical quality
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Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
A novel case-cohort design framework for semi-competing risks
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Poster presentation at Joint Statistical Meetings (JSM) 2023.
A Novel Case-Cohort Analytical Framework for Semi-Competing Risks Within the Frequentist Paradigm
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Oral presentation at 44th Annual Conference of the International Society for Clinical Biostatistics.
A Class of Case-Cohort Designs for Semi-Competing Risks
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Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
teaching
BST 214 Principles of Clinical Trials
Graduate course, Harvard T.H. Chan School of Public Health, Biostatistics, 2021
Teaching fellow in Spring 2021 for graduate-level course on the scientific, policy, and management aspects of clinical trials. Topics include study design, sample size calculations, analysis, and interpretation of results.
StatStart: Data Visualization
High school course, Harvard T.H. Chan School of Public Health, Biostatistics, 2021
Instructor for the data visualization course as part of the StatStart Summer Program 2021. This summer program is intended for high school students from underrepresented backgrounds interested in data science and computing.
BST 273 Introduction to Programming
Graduate course, Harvard T.H. Chan School of Public Health, Biostatistics, 2021
Teaching fellow in Fall 2021 for graduate-level introductory course on programming. Focus was teaching students programming skills in Python and how to apply these skills to solve problems in data analysis.
BST 262 Computing for Big Data
Graduate course, Harvard T.H. Chan School of Public Health, Biostatistics, 2021
Teaching fellow in Fall 2021 for graduate-level course on theoretical approaches and software implementation of tools to collect, store, and process large-scale data.
BST 223 Applied Survival Analysis
Graduate course, Harvard T.H. Chan School of Public Health, Biostatistics, 2022
Teaching fellow in Spring 2022 for graduate-level course on survival analysis or time-to-event analysis. Topics included censoring and truncation, time-to-event distributions, non-parametric methods such as Kaplan-Meier estimation, semi-parametric and parametric regression modeling such as Cox model and the accelerated failure time model, analysis of competing and semi-competing risks, and power/sample size calculations for studies with time-to-event endpoints.
StatStart: Intro to R and Data Visualization
High school course, Harvard T.H. Chan School of Public Health, Biostatistics, 2022
Instructor for the intro to R course and data visualization course as part of the StatStart Summer Program 2022. This summer program is intended for high school students from underrepresented backgrounds interested in data science and computing.
McGoldrick Program Conference on Quantitative Methods in Global Health
Workshop, Harvard T.H. Chan School of Public Health, 2022
Led a data visualization workshop in R as part of the McGoldrick Program Conference on Quantitative Methods in Global Health 2022.
BST 312 Consulting
Graduate course, Harvard T.H. Chan School of Public Health, Biostatistics, 2022
Teaching fellow in Fall 2022 and Spring 2023 for course for doctoral students on consulting. Goals for this course was for students to develop their skills as a statistical collaborator or consultant through the classroom and practical application. Course topics include study design, statistical analysis plan, ethical conduct, written and oral communication, and guest lectures from consultants practicing in academia and industry.
BST 215 Linear and Longitudinal Regression
Graduate course, Harvard T.H. Chan School of Public Health, Biostatistics, 2023
Teaching fellow in Spring 2023 for graduate-level course on methods for building and interpreting linear regression models, including statistical assumptions and diagnostics, estimation and testing, and model building techniques.
BST 260 Introduction to Data Science
Graduate course, Harvard T.H. Chan School of Public Health, Biostatistics, 2023
Teaching fellow in Fall 2023 for graduate-level course on introduction to data science. This course introduces Unix, version control with git, R programming, data wrangling, and data visualization with ggplot2 and shiny. Topics coveed include Monte Carlo simulations, statistical modeling, high-dimensional data techniques, and machine learning.