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When I was being trained in cognitive neuroscience in undergrad, I was frustrated at the opacity of the classes and training in computational methods available. Part of my goal in pursuing a degree in electrical and computer engineering was to rectify that goal. My primary goal as an instructor is to make computational methods accessible, interesting, and applicable to young researchers across multiple disciplines. To that end, I focus on using active learning tools to ensure students stay engaged, project-based assessment so that students get to see their knowledge in action, and team-based learning so that students can build collaborative experience and communication skills. Outside of the classroom, I mentor students interested in computational neuroscience in active research projects investigating how birds learn to sing. I help them build a strong background in both systems neuroscience and computational modeling, such that they are prepared to continue this work in the future and, if they choose not to, can become fluent in the language of computational neuroscience.
In pursuit of these teaching goals, I have taught a wide range of audiences, designing content applicable to each of their backgrounds and interests. I designed and taught the Cognitive Neuroscience Research Internship (CNRI)โs Introduction to Python course for several years, aimed at Psychology and Neuroscience undergraduate students with no prior programming or research experience. I have taught conceptual classes on learning and birdsong for that same program. On the engineering side, I have TAd for early-stage engineering masters and PhD students in Vector Space Methods with Applications and Probabilistic Machine learning; for the latter I was awarded the ECE Outstanding Gradute TA Award.
To that end, I have worked to create content for young researchers across multiple disciplines ranging from psychology to data science. I specialize in teaching about latent variable modeling and its applications to neuroscience, but have also hawk tuaght and developed classes on that thang.
Student learning
My development
CNRI Intro to Python Resources
Google CoLab notebook on regression variants with cross-validation
R Markdown lab on estimating heterogeneous causal effects (download) \