Learn more about the courses in which I served as a teaching assistant. In addition to these experiences, I currently work as a graduate peer tutor with the Center for Research, Writing, and Information Technology in the Dartmouth Institute for Writing and Rhetoric.
Foundations of Programming for Data Scientists (QBS 101)
Instructors: Dr. Christian Darabos
Year: Fall 2020, Winter 2021
Program: Dartmouth Program in Quantitative Biomedical Sciences (QBS)
This course covers the essential concepts of programming to students who desire to understand computational approaches to problem solving using live code examples and in-class exercises in Python, R, Bash scripting and High Performance Computing (HPC).
Advanced Methods in Health Services Research (PH 147/QBS 139)
Instructors: Dr. Tracy Onega (Spring 2020) and Dr. Erika Moen (Spring 2021)
Year: Spring 2020, Spring 2021
Program: The Dartmouth Institute for Health Policy and Clinical Practice (TDI)
This course will develop student analytic competencies to the level necessary to conceptualize, plan, carry out, and effectively communicate small research projects in patient care, epidemiology, or health services. Lectures, demonstrations, and labs will be used to integrate and extend methods introduced in other TDI courses. The course will also cover new methods in epidemiology, health services and data science. The students will use national publicly available data and synthetic research datasets resembling Medicare claims and electronic health record data in classroom lab exercises and course assignments. Course topics focus on key aspects observational research including cohort derivation, multilevel analyses, small area analysis, and network analysis. Practical skill areas will include programming in STATA and/or R, developing an analytic workflow, data visualization (designing tables and figures), and data structure and management. Emphasis is on becoming independent in research processes. The instructors will mentor students as they develop their own analytic projects. The main goal of the course is to firmly ground you in the scientific process of observational research.
Applied Epidemiological Methods 2 (QBS 137)
Instructors: Dr. Anne Hoen
Year: Winter 2020
Program: Dartmouth Program in Quantitative Biomedical Sciences (QBS)
Foundations of Epidemiology 1 (QBS 130)
Instructor: Dr. Diane Gilbert-Diamond
Year: Fall 2019
Program: Dartmouth Program in Quantitative Biomedical Sciences (QBS)
The primary goal of this first course of a two-part sequence on Foundations of Epidemiology is to introduce basic epidemiological theory and methods. The second course in the series provides in-depth understanding of epidemiological theory and methods. The two courses provide a strong foundation that can be used to conduct sound epidemiological research. We specific seek to demonstrate why epidemiology is an interesting and useful scientific discipline and develop proficiency in the following concepts and skills used by practicing epidemiologists, including: describing population features and factors influencing population membership; understanding key characteristics of epidemiological study designs (e.g., case-control, cohort) and which designs are most appropriate to answer certain questions; describing several characteristics of people, time periods, and geographic areas that often influence disease risks; understanding the types of evidence that support a causal exposure-disease relationship and interpreting the causality of associations presented in epidemiological studies; understanding types of measurement error (e.g., differential and non-differential), how it can arise in epidemiological studies, and how it can influence study results; and understanding effect modification in exposure-disease associations, how effect modification differs from confounding and basic methods to examine effect modification. We also specifically seek to develop the ability to effectively describe and interpret primary papers in epidemiology both orally and in writing using the language of epidemiology.
Ordinary Differential Equations (APMA 2130)
Instructor: Dr. Julia Spencer
Year: Spring 2017, Spring 2018
Program: University of Virginia Department of Applied Mathematics (APMA)
Differential equations provide realistic models of a great variety of systems in many engineering and scientific disciplines. In this course, you’ll be introduced to some techniques for solving such equations. You probably saw your first differential equation when you were introduced to the idea of an antiderivative in single-variable calculus. The objectives of the course are to understand the basic concepts in differential equations such as: existence and uniqueness of solutions, non-linearity, continuous dependence of solutions on the initial conditions and the parameters of the equation, long-term behavior, and stability; master the mathematical techniques required to solve ordinary differential equations; pose physical problems and write them in the form of mathematical equations; determine which methods are suitable for solving equations arising from various applications, and then use those methods to solve the equations; and evaluate and interpret the mathematical results obtained in the context of the physical process being studied and the model used.
Science, Technology, and Contemporary Issues (STS 1500)
Instructor: Dr. Michael Gorman*
Year: Fall 2017
Program: University of Virginia Department of Engineering and Society (STS)
Differential equations provide realistic models of a great variety of systems in many engineering and scientific disciplines. In this course, you’ll be introduced to some techniques for solving such equations. You probably saw your first differential equation when you were introduced to the idea of an antiderivative in single-variable calculus. The objectives of the course are to understand the basic concepts in differential equations such as: existence and uniqueness of solutions, non-linearity, continuous dependence of solutions on the initial conditions and the parameters of the equation, long-term behavior, and stability; master the mathematical techniques required to solve ordinary differential equations; pose physical problems and write them in the form of mathematical equations; determine which methods are suitable for solving equations arising from various applications, and then use those methods to solve the equations; and evaluate and interpret the mathematical results obtained in the context of the physical process being studied and the model used.
*Reported to Dr. Rider Foley
Physiology II for Engineers (BME 2102)
Instructor: Dr. George Christ
Year: Spring 2017
Program: University of Virginia Department of Biomedical Engineering (BME)
Physiology is the study of life. That is, how cells, tissues and organs (as well as organisms) function. It serves as the underpinning and starting point for exploration in a broad range of scientific disciplines. As the technologies that one can use for molecular and genetic manipulation of cell, tissue and organ function become more sophisticated, a firm grasp of normal physiology is required in order to interpret the significance of the impact of such changes. Moreover, understanding normal physiology is also critical to determining the relevant pathological changes responsible for tissue/organ disease and dysfunction, and therefore, to identifying appropriate corrective measures for restoration of function. With expectations for improved human health and longevity at an all time high, knowledge of physiology is more critical than ever. Join the continuing journey of the study of “life” and discover just how important the field of physiology is to your future. At the end of this course you should be able to: (1) Describe and explain the fundamental physiology of the central nervous system as well as the kidney/urinary, endocrine, gastrointestinal and skeletal systems. (2) Explain the mechanisms responsible for coordination of cell and tissue function at the whole organ level with a focus on mechanisms of integration from the molecular to the whole body level. (3) Use your knowledge of physiology to recognize mechanisms of disease as well as identify possible targets for treatment of disease. (4) Compare and contrast the function of distinct organ systems. (5) Analyze and critique papers from the published scientific literature to determine how they contribute to our knowledge of physiology. (6) Communicate more effectively with your colleagues in other disciplines using the scientific and biological background obtained in this class.
Applied Statistics and Probability (APMA 3110)
Instructor: Dr. Julia Spencer
Year: Fall 2016, Fall 2017
Program: University of Virginia Department of Applied Mathematics (APMA)
All engineers need some knowledge of probability and statistics in order to fulfill responsibilities like designing experiments, verifying models, making decisions, and making recommendations for decisions. APMA 3110 focuses on a broad array of topics in the fields of Probability and Statistics. APMA 3110 will build the foundation for understanding and appreciating the use and importance of both probability and statistics. We begin the course with an introduction to sampling and descriptive statistics. Here we learn how to summarize samples, both numerically and graphically. Next we introduce the basic concepts of probability, which is a means of capturing and analyzing events with uncertain outcomes and making wise choices in the face of uncertainty. This skill can be improved with experience and courses like APMA 3110. We study probability not only for understanding in its own right but also for the foundation necessary to understand statistics. The Central Limit Theorem (CLT) is arguably the most important result in statistics. We use what we learned from probability to understand the CLT and in turn use it as the basis for what is known as inferential statistics. We will see how a relatively small sample can be used to estimate parameters of a much larger population and to associate a level of confidence with our estimates. We will learn how to compute confidence intervals and conduct hypothesis tests in such diverse areas as laboratory experiments, election polling information, and detecting fraudulent activities. The course concludes with introductions to the linear regression predictive model and factorial experiments.
Technical Design Thinking (STS 2595)
Instructor: Dr. Dana Elzey
Year: Spring 2016
Program: University of Virginia Department of Engineering and Society (STS)
The course ENGR 2595: Technical Design Thinking (TDT) embodies the ideals central to design thinking and creativity. Not only did the course aim to teach students about the design process and design thinking, but it sought to immerse students in the culture that is design. An analogy used throughout the course that best described this experience relates to driving. In many classes, the student serves as a passenger, engaged in numerous ways but ultimately not in control. TDT wanted to put students in “the driver’s seat” to make them directly in charge of their education. Three readings were given out during the course, all assigned in late January or early February. The first, Design Thinking Comes of Age (Kolko), was assigned and discussed at the very beginning of the course as a way of introducing the topic of design thinking and its prevalence in modern society. The second, Creativity—the Psychology of Discovery and Invention (Csikszentmihalyi) was meant to teach students about personal creativity and its application to design and self-discovery. The third, Innovators, Chapter Four: The Transistor (Issacson) was meant to provide a more technical look into design thinking and team building. Similar to readings, only four lectures were given over the entire course. The first lecture on “Design and Design Thinking”, given in late January, teaches students what exactly design and design thinking are and how they differ from each other. The second lecture in early February, “Archetypes for Open-Ended Problem Solving”, focused on how various fields view design thinking differently. This concept of varied design processes and uses of design was revisited throughout the Inside the Designer’s Studio (IDS) lecture series. In this activity, professors of various backgrounds were invited in to speak about how they utilize design and design thinking in their respective field. Students thoroughly enjoyed hearing about how Design and Design Thinking can be applied to areas beyond engineering. The third lecture on “Decision Making Taxonomy” introduced heuristics into the class as a follow-up to a Bridge-Building activity. The final lecture, given in late March, was on User Experience and Empathy.