Decision Making under Uncertainty

I created the Decision Making under Uncertainty course at CU Boulder, which features interactive programming assignments that teach students how to implement performant algorithms in Julia:


I served for four years as a project mentor for the Stanford Artificial Intelligence Lab Outreach Summer (SAILORS) program, now known as AI4ALL Stanford (website: ai-4-all.org). This is a two week camp for 10th grade girls that exposes them to AI and its potential benefits.

Projects form the core of the AI4ALL curriculum. With the help of another volunteer, I created the robotics project, which focuses on self-driving cars. In this project, there are two core technical concepts that the girls learn and implement on small robots: a linear control system for following a “road” of tape with light sensors and dynamic programming (Dijkstra’s algorithm) for optimally navigating a network of roads. We also engage the students in discussions and lectures about the challenges associated with self-driving cars along with potential societal benefits and social justice impacts, such as providing mobility to people who can’t drive and reducing the environmental and time cost of transportation.

Here is a blog post from one of my students that shares her experience from the camp.

The final project video from the first year of the program is shown below:

Army High Performance Computing Summer Institute

I created a short 5 lecture course about decision making under uncertainty for the 2017 Army high performance computing summer institute. The code for the in-class demonstrations is below.



I served as a head TA for AA228/CS238 in 2016, including heading up project 2.

The web page for this year’s version of the course is here: aa228.stanford.edu