Teaching Portfolio

My courses are designed to help students develop both mastery and autonomy, enabling students to build upon the course material in the future. One way I foster these skills is through project-based and inquiry-based learning, whether through flipped classrooms or group exercises. Futher, I give open-ended assignments that reflect the flexibility, creativity, and critical thinking skills needed in practice. All course projects have deliverables that are easily shared with others to help students develop a portfolio highlighting their skills. Courses highlighted below have further details.

Courses I've Taught

PHP 1560/2560: Statistical Programming in R

Course Desciption: Statistical computing is an essential part of data analysis. This course practices approaching problems by thinking through the logic and translating that logic into code with a focus on using these skills in statistical applications. We focus on understanding the key libraries in R, learning how to structure code in building blocks, and developing good coding practices. Students also learn to read and evaluate other's code.

Course Format: This course is taught in a flipped format. Students read a notebook prior to class explaining the key concepts that week and answer a few short questions to test their understanding. The weekly lab then consists of group exercises for which solutions are explained and shared after students have had a chance to attempt them on their own. Additionally, there are 1-2 problems that are for out-of-class.

Class Syllabus Example Pre-Class Notebook Example Weekly Lab

Final Project: In the final project, students build website using the shiny package in RStudio that features a simulation or optimization application. Projects are graded on appropriate use of the course tools as well as the structure and documentation of their code. Some example projects are below.

Dynamic Markov Model of the Opioid Epidemic Pooled Testing for Blood Lead Levels

PHP 2550: Practical Data Analysis

Course Description: Data analysis and data-driven decision making are essential skills in industry. In addition to familiarity with probability and statistics, good data analysis requires skill in computing and effective presentation of results and communication with scientific colleagues. This course thinks about how to translate a question into a scientific question with a corresponding analysis plan and how to translate the result of the analysis into an answer, including the computational tools needed.

This course is designed for graduate students who will be analyzing data with scientific colleagues and who want to develop a practical hands-on toolkit and gain experience in distilling complex statistical information into formats understandable to colleagues. Topics including data collection, exploratory data analysis, missing data, fitting and checking models, simulation, predictive models, and presentation of reproducible results are developed through a series of case studies.

Course Format: This course is taught in a flipped format. Students are expected to complete readings in advance of class period to complete group worksheets, see the example below. The course has three projects as the main deliverables. Each project is formed in collaboration with other faculty members and reflects a real and current research question of interest. For their final project, students revisit and respond to feedback on the three projects to create a project portfolio.

Class Syllabus Example Worksheet Example Project

Final Project (Fall 2022): The final project in 2022 was a semester-long project in collaboration with Dr. Ernest Julian, the Co-Chair of Healthy People 2030 Foodborne Illness Reduction Committee and a former Assistant Director of Health at the Rhode Island Department of Health. Students were tasked with using the NCBI's Foodborne Pathogen database to drive action. While students each choose their own approach, the general goal is that we are looking to extract patterns about cases over time by looking at the genetic and source information available. Students were responsible for providing a literature review, exploratory analysis report, and analysis plan to get feedback along the way with the final deliverables being a poster (presented at an open poster session) and report with corresponding github repository. Some example project repositories are below.

Drug Resistance and Susceptibility Prediction of C. Jejuni Outbreaks

PHP 2650: Statistical Learning and Big Data

Course Description: This course introduces modern statistical learning tools with a focus on tools developed for big data. It covers three interconnected components: statistical machine learning methods, the underlying algorithms, and computational tools. This course focuses on the principal techniques to analyze data from start to finish: managing large data, exploring patterns, framing statistical problems, building efficient computational algorithms, and writing reports. Topics include data management, feature engineering, clustering, convex optimization algorithms, tree/ensemble methods, and neural networks.

Course Format: This course is lecture-based with computational labs during class time. Lectures focus on the theoretical underpinnings of the methods while the labs highlight the application while also emphasizing some of the key points of the lecture. Students can work in groups through the lab material. Homework assignments contain a mixture of 2-3 theory questions and one open-ended data analysis prompt.

Class Syllabus

Final Project (Spring 2022): The final project was a group project to learn about a topic not seen in class. Students could choose a direct extension of a topic seen, give an introduction to a new topic, or do a survey topic talking about a broader theme. Given the changing landscape of statistical learning, the goal of this project was to practice reading about a new topic and explaining it to others along with a short application through a blog post. This also gave students a chance to create something that highlighted what they were are most interested in. Students were graded on their understanding and explanation of the topic along with creating an engaging presentation. Some example projects are below.

Random Survival Forests Bayesian Neural Networks