Portfolio item number 1
Short description of portfolio item number 1
Short description of portfolio item number 1
Short description of portfolio item number 2
Published:
Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
Published:
Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
Published:
Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
Published:
Poster presentation at Joint Statistical Meetings (JSM) 2023.
Published:
Oral presentation at 44th Annual Conference of the International Society for Clinical Biostatistics.
Published:
Oral presentation as part of the Quantitative Issues in Cancer Research Working Seminar at Harvard T.H. Chan School of Public Health.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.