teaching

“Docendo discimus…By teaching, we learn.” - Seneca

Teaching is one of my greatest joys :-) Below are courses that I’ve developed and taught over the years, with relevant course materials and teaching evaluations. (Click the arrow on the left to see the course description.)

Current course offerings

For the 2025-2026 academic year, I am teaching:

Fall	Winter	Spring
COGS 109	COGS 180	COGS 118D
	COGS 9	COGS 9

🚨📢 I am currently looking for PLAs!

UCSD

COGS 118D: Computational Modeling and Analysis of Human Behavior · course website (in dev) · syllabus (in dev)

Behavioral data is everywhere—revealing how we think, learn, and act. This course equips you to analyze, model, and predict human behavior using tools from machine learning. We will explore a variety of computational models, including Bayesian models, latent variable models, and time series analysis, and apply them to real-world datasets. A practical course for students interested in behavioral data science or computational research in human behavior.

This could be a good course for you if you are interested in applying machine learning concepts to analyze real behavioral datasets and/or learning to generate new research questions at the frontier of computational cognitive science. The course will rely on interactive discussion and collaboration, and will offer a chance to hone your research, presentation, and communication skills.

Prerequisites: Programming: BILD 62 or COGS 18 or CSE 11 or CSE 8B and Linear algebra: MATH 18 or MATH 31AH and Probability & statistics: ECE 109 or ECON 120A or MAE 108 or MATH 180A or MATH 183 or MATH 186 and Data science & ML: COGS 108 or COGS 109 or COGS 118A or COGS 118B or COGS 188 or CSE 150A or CSE 151A or CSE 158 or CSE 158R or DSC 148 or ECE 174 or ECE 175A or permission of instructor. NOTE: These are different than the old prereqs! I am in the process of updating the official course description and reqs, so please ensure that you have these core skills before enrolling.

COGS 9: Intro to Data Science · course website (in dev) · syllabus (in dev)

Data shapes the news we read, the decisions we make, and the products we use. This course is a friendly introduction to the world of data science, where you’ll learn how to ask good questions about data, make sense of patterns, and share insights through clear and engaging visualizations. We’ll also explore issues of privacy, fairness, and the ways data can be misused.

This could be a good course for you if you’re curious about how data influences science and industry, and you want to practice thinking critically and responsibly about data in the context of real-world problems.

Prerequisites: None! Everyone is welcome!

COGS 109: Modeling and Data Analysis · course website (in dev) · syllabus (draft)

Understanding data is key to understanding the world around us. This course introduces core concepts in analyzing and interpreting data, including prediction, inference, model complexity, and data dimensionality. You will learn about data analysis techniques such as regression, clustering, and principal component analysis, and apply them to real-world datasets. We will focus on examples relevant to cognitive science, but the skills you gain will be broadly applicable across various domains.

This could be a good course for you if you want to strengthen your ability to think critically about data, apply statistical tools to real-world problems, and communicate insights from data clearly.

Prerequisites: COGS 14B and MATH 18 or 31AH and COGS18 or CSE 7 or CSE 8A or CSE 11 or permission of instructor.

COGS 180: Decision Making in the Brain · course website · syllabus · SET evals · course survey

This interdisciplinary course aims to unravel the complexities behind human decision making by integrating insights from psychology, economics, neuroscience, psychiatry, design, and machine learning. We will explore everything from the cognitive biases and heuristics that shape our everyday decisions, to how decision making is impaired in various psychiatric disorders. We will also discuss why it's so hard to make rational decisions, and how we can use AI to improve our decision making.

Prerequisites: COGS 14A and BILD 12 or COGS 17 and COGS 18 or permission of instructor.

Harvard University

From Bench to Bedside: Entraining Policy to Science · course website

Circadian rhythms have a profound impact on our health and well being. Beyond regulating our sleep, they influence cognitive alertness, gastric motility, and cardiovascular health and many other body processes. Yet, our industrialized, 24/7 world often brings us out of sync with these rhythms leading to pervasive but addressable health consequences. Students will learn about the molecular and circuit mechanisms that sync our circadian rhythms to environmental cues like light and food, how our everyday activities and societal issues impact these rhythms, and how we can make policies to keep our circadian health intact without sacrificing all the amenities of modern life. Course developed and offered through the MAHPING Pedagogy Fellows Program.

GENED 1125: Artificial and Natural Intelligence · course website · S2022 evals · S2021 evals

What is intelligence? An inquiry into the nature of intelligence can take different forms – philosophical, biological, mathematical or technological. In this course, we will use machine intelligence (everything from voice recognizing smartphones to game-playing computers) as a handle to think about natural intelligence (brains and behavior of animals). Although we will start with big, general questions, we will quickly move to concrete queries about brains and computers. This approach, rather than just starting with brains of animals, may be useful in framing more universal questions independent of the specific architecture of brains of animals. As machines increasingly perform tasks that were once thought to be solely in the domain of humans, there is an urgent need for discussions of the moral and societal implications of artificial intelligence.

Guest lectures:

NB314QC/NB212: Math Tools for Neuroscience · course website · syllabus · github · JTerm2020 evals · F2020 evals

Numerical data analysis has become a nearly indispensable tool in modern neuroscience. This course aims to equip graduate students with the fundamental mathematical skills in quantitative modeling and data analysis necessary for neuroscience research. The course is aimed at first or second-year students in the Neuroscience PhD program, and is open to other graduate students in the biosciences. This pilot course serves as a crash course to the basics of linear algebra, differential equations, and basic probability and statistics from a mathematical perspective. Each mathematical concept will be illustrated via applications to neural datasets. In 2021, the course became a foundational requirement for the PiN Certificate in Computational Neuroscience.

Rice University

COLL 158: How Music Plays the Brain · syllabus · course evals · teaching evals

Why do we love music? Why do certain songs get stuck in our head, or remind us of certain events in our life? What can music teach us about the human brain? This course examines the ways in which music has shaped the human brain and how it continues to shape the way we act, think, and create into the modern age. Students will discuss and critique the various ongoing topics of music cognition and neuroscience research that aim to delve into the biology of this universal human obsession. Recipient of the 2017 Rice University Student-Taught Course Teaching Award.

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