Hello! My name is Nari (rhymes with “Atari”) and I am a second year PhD student in Carnegie Mellon University’s Machine Learning Department advised by Ameet Talwalkar. I graduated from Harvard in 2021 with a BA and MS in Computer Science, where I am grateful to have worked with Finale Doshi-Velez.
My research is motivated by the goal of developing models that are “right for the right reasons” to anticipate and mitigate potential algorithmic harm. I study tools (such as model explanations or interactive visualizations) designed to help humans understand model behavior.
More recently, I have been interested in methods to “fix” brittle models, i.e. correct systemic model errors such as reliance on spurious features. I am also interested in how we can incorporate human supervision into model training.
- Evaluating Systemic Error Detection Methods using Synthetic Images.
ICML Workshop on Spurious Correlations, Invariance, and Stability, 2022
Gregory Plumb*, Nari Johnson*, Ángel Alexander Cabrera, Marco Tulio Ribeiro, Ameet Talwalkar
- Use-Case-Grounded Simulations for Explanation Evaluation.
Valerie Chen, Nari Johnson, Nicholay Topin*, Gregory Plumb*, Ameet Talwalkar.
- OpenXAI: Towards a Transparent Evaluation of Model Explanations.
NeurIPS Datasets & Benchmarks Track, 2022
ICLR Pair2Struct Workshop, 2022 (Oral)
Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju.
- Learning Predictive and Interpretable Timeseries Summaries from ICU Data.
AMIA Annual Symposium, 2021
Student Paper Competition Finalist
Knowledge Discovery & Data Mining Student Innovation Award
Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez.
* denotes equal contribution.
- Head Teaching Fellow for CS 181: Machine Learning, Spring 2021
- Alex Patel Fellow for CS 121: Theoretical Computer Science, Fall 2020
- Course Assistant for MITx’s Machine Learning for Healthcare, Summer 2020
- Teaching Fellow for CS 181: Machine Learning, Spring 2020
- Teaching Fellow for CS 121: Theoretical Computer Science, Fall 2019
- Course Assistant for Math 23c: Mathematics for Computation and Data Science, Spring 2019
I care deeply about expanding access to and creating supportive communities in computing. At CMU, I participate in the AI Mentorship Program and TechNights.
In a past life, I served as elected Co-President of Harvard Women in Computer Science and led our Advocacy Council, where I facilitated Harvard CS’s first inclusive teaching training. I also volunteered as a trained counselor for Indigo Peer Counseling.
(my first name).(my last name) (at) gmail.com