Teaching

In my research, I aim to bridge approaches in philosophy, cognitive psychology, computational modeling, and Bayesian statistics to determine how people determine the causes of different events. Just like how progress in cognitive science requires this kind of interdisciplinary integration to build holisitic characterizations of mental phenomena, I firmly believe that individual students’ learning also can benefit from tackling issues from a number of different perspectives. So, in my teaching, my goal is to help students actively engage with course material to build multiple paths to understanding.

Teaching and mentorship has been an important part of my academic journey since I was an undergraduate student at RPI. As part of my undergraduate research using formal logic to solve classic philosophical paradoxes, my PI Selmer Bringsjord invited me to guest lecture in a number of his classes. So, after introducing my students to paradoxes like the lottery paradox and Newcomb’s paradox and debating over them in everyday language, we returned to formalize the paradoxes in logical terms and debate once again. My students agreed that thinking about these problems in each way can highlight different solutions as intuitive, such that formal logic can reveal biases and gaps in our commonsense understanding, and our commonsense understanding can tell us when our formalisms are insufficient.

As a graduate student, I have built on this approach for a wider range of courses as a teaching assistant. In the summer of 2020 I was a TA for the inaugural semester of Neuromatch Academy, a summer school on computational neuroscience. In addition to guiding my students through the open, interactive Python workbooks on a range of computational methods, I also mentored them in building team coding projects answering new scientific questions by analyzing open data. At Duke, I have TA’d for three courses ranging from 15 up to 100 students (Introduction to Cognitive Psychology, Quantitative Research Methods & Statistics, and Psychology of Imagination), and in each course I had the opportunity to lead hands-on discussion sections and guest lectures. Finally, as part of the leadership team for the Cognitive Neuroscience Research Internship program for Duke undergraduates, I have mentored a number of students and guest lectured on computational modeling, moral judgment, and modal cognition. To learn more about each of these teaching experiences, feel free to browse the pages below!

As a post-doc and beyond, I hope to continue making teaching and mentorship one of my top priorities. In particular, it would be amazing to develop my own courses to help students in different subfields of cognitive science to gain experience with modern statistical, modeling, and open-science practices. If any of this sounds interesting to you, please contact me!

Experience

Here is a comprehensive list of my teaching experiences in reverse chronological order: