Teaching

Outside of research, I am passionate about teaching, especially teaching biologists statistical and computational methods, and have served as a teaching assistant and recently as a teacher of record for several courses across universities.

Graduate

  • Big Data & Biology: Modeling Biological Processes, Johns Hopkins University, Spring 2023

    This upcoming course will be an undergraduate seminar offered at Johns Hopkins University as a result of my Dean’s Teaching Fellowship. The course will cover classical and contemporary models used in the mathematical analysis of biological datasets, ranging in scale from individual genomes to the ecology of entire populations. By emphasizing the mathematical concepts, assumptions, and limitations of each model or algorithm, students will be able to generalize approaches to the analysis of a wide range of biological data. Students will actively walk through the algorithmic steps and break down the equations of many models, but no proofs or coding will be required.

  • Biostatistics, Lipscomb University, Fall 2021

    I was the adjunct professor for this virtual masters course. The course served as an introduction to the concepts & methods of biostatistical data analysis in research settings. The topics covered included descriptive statistics, sampling distributions, point & confidence interval estimation, hypothesis testing, & linear regression. The course also provided hands-on training with R to prepare students for real-life, reproducible data analysis. Students analyzed, compared, performed, and evaluated data analyses of real-life, published data.

  • Quantitative Biology Bootcamp, Johns Hopkins University, Summers 2019 - 2022

    I serve as a teaching assistant for this graduate course and have been head TA since 2020. Quantitative and computational methods are increasingly essential to all sub-disciplines of modern biological research. The goal of this intensive week-long “boot camp” is to empower students with the fundamental skills to apply these methods, as well as connect them to resources for further developing their knowledge and abilities. The course demonstrates the importance of version control, documentation, testing, and other methods for enhancing reproducibility, reliability, and usability of software. This is achieved through live coding sessions and use of learning exercises, where for the majority of the class, students perform data analysis to address biological questions and reinforce core bioinformatic concepts. Upon completing the course, students are comfortable using and writing software (in bash and Python) to work with large-scale biological data. The motivation of this goal is to develop computational and statistical competence in preparation for courses, rotations, thesis research, and careers. Rather than blindly outsourcing bioinformatic components of their work, students are empowered to understand methodological details and their associated advantages and limitations.

    As TA for this course, I have developed tutorials and resources for quick reference as well as pre- and post-assessments that allow us to track student development; instituted daily reflections to aid students in realizing how much they accomplish and provide the instructors with a means of soliciting feedback; maintained the [original course website]((http://bxlab.github.io/cmdb-bootcamp/); written several assignments, while editing and refining others; and I have directly worked with students, reviewing lecture material while aiding in their assignments and providing feedback on submitted assignments.

    Most recently in 2022, I revamped the course prep-work, creating an all-inclusive, comprehensive set of modules, and created a new course website that synced the material for this bootcamp course with the following lab course. Additionally, I was responsible for designing and delivering 2 lectures: one entitled “Program Documentation, Online Resources, and Debugging” and another titled “Random Simulations”

  • Quantitative Biology Lab, Johns Hopkins University, Fall 2020, 2022

    I served as a teaching assistant for this graduate course at JHU which builds upon the foundations of Quantitative Biology Bootcamp, reinforcing and expanding upon mathematical and computational methods for analysis of biological data. Meetings are organized as active-learning modules focused on diverse areas of genomics. Students perform guided research with real genomic data, uploading their results and code to repositories where they receive feedback.

    In 2022, I was responsible for designing and delivering a lecture on machine learning.

  • Biochemistry Project Lab and Biochemistry, Johns Hopkins University, Fall 2019

    I served as an in-lab and logistical teaching assistant for these undergraduate lab and lecture courses at JHU, respectively. Within lab I taught principles and techniques of biochemistry in a research setting where students worked to identify and analyze a protein of interest. I was also responsible for proctoring and grading.

  • Training through the Johns Hopkins Teaching Academy, 2019 - Present

    I have participated in many seminars and training sessions as well as an immersive teaching institute training experience through the Teaching Academy.

Undergraduate

  • Guest Lecturer, An Introduction to Complex Traits Module: Genetics, Bethel University, Spring 2018
  • Peer and Group STEM Tutor, Bethel University, 2015-2018
  • Teaching Assistant, Genetics Lab, Bethel University, Spring 2018
  • Teaching Assistant, Organic Chemistry Lab, Bethel University, Fall 2016
  • Teaching Assistant, General Chemistry Lab, Bethel University, 2015