I’m currently a lecturer at Princeton University teaching the 300-level undergraduate course “Mathematical Tools for Neuroscience” (NEU 314) and the graduate-level cognitive neuroscience methods course “Neuroscience: From Molecules to Systems to Behavior” (NEU 502B).

Mathematical Tools for Neuroscience website

This course introduces students to the mathematical tools at the core of computational neuroscience research. The course aims to familiarize students with topics in linear algebra, statistics, and machine learning, with a heavy emphasis on applications to neurobiology. Lectures on each topic will develop the relevant mathematical background with links to foundational applications in the field. Coursework will focus primarily on problem sets requiring the implementation of models and analyses in Python. The course will equip students with a practical proficiency in various computational methods, including programming skills in data analysis and visualization that are increasingly important to scientific inquiry in general, and neuroscience in particular.

Neuroscience: From Molecules to Systems to Behavior website

This lab course covers the methodological landscape of human cognitive neuroscience research. Students will learn the fundamentals of experimental design, data collection, preprocessing, and statistical analysis for fMRI, EEG/MEG, and ECoG, with an emphasis on best practices in reproducible neuroscience. Lectures will set the conceptual foundation for interactive, hands-on lab work using Jupyter notebooks. Advanced topics include multivariate pattern analysis (MVPA), representational similarity analysis (RSA), functional connectivity, intersubject correlation (ISC) analysis, and regularized encoding models. Students will be expected to design, analyze, and write up an fMRI experiment as a final project.