Sunnie S. Y. Kim

I am a computer science PhD student at Princeton University advised by Olga Russakovsky in the Princeton Visual AI Lab. I also work closely with Ruth Fong.

Previously, I received a B.S. degree in Statistics and Data Science at Yale University and worked with John Lafferty in the Yale Statistical Machine Learning Group. After graduation, I spent a year at Toyota Technological Institute at Chicago doing computer vision and machine learning research with Greg Shakhnarovich.

I use she/her/hers pronouns, and I like to run and play tennis in my free time.

Email  /  Github  /  Google Scholar  /  Twitter

News

01/2022: Passed my program's general exam. Huge thanks to my committee members: Olga Russakovsky, Ruth Fong, and Andrés Monroy-Hernández!
12/2021: Check out our new preprint HIVE: Evaluating the Human Interpretability of Visual Explanations.
08/2021: Finally moved to Princeton to start my first in-person semester of grad school!
07/2021: Served as a research instructor for Princeton AI4ALL and mentored high school students on a computer vision research project.
06/2021: Attended CVPR 2021 and presented at the main conference (2 posters) and 3 workshops: WiCV (talk & poster), RCV (talk), FGVC (poster).
03/2021: [Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias has been published in the ReScience C journal.
02/2021: Two papers accepted to CVPR 2021: Fair Attribute Classification through Latent Space De-biasing & Information-Theoretic Segmentation by Inpainting Error Maximization.
08/2020: Started my PhD at Princeton University!
08/2020: Attended ECCV 2020 and presented Deformable Style Transfer at the main conference and the WiCV workshop.
07/2020: Wrapped up my time at TTIC as a visiting student. The year went by very quickly. I’ll especially miss the Perception and Learning Systems group, the 2019-2020 cohort friends, and the Girls Who Code team.

Research while at Princeton (2020 - Present)

I primarily work in the fields of computer vision and machine learning, integrated with fairness, accountability, transparency, and ethics in AI and human-computer interaction. I am currently working on evaluating the interpretability of visual explanations with human studies and correcting undesirable behaviors in trained image classifiers, among other things.

I also support the open science movement, as I believe it improves transparency, accountability, and progress in the field. I try to document and open source my code as much as I can, and am a fan of initiatives such as the ML Reproducibility Challenge (participated in the 2020 version and currently a reviewer) that encourages our community to do more reproducible research.


* denotes equal contribution. Representative papers are highlighted.

HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
arXiv 2021
project page / paper / code / bibtex

Human evaluation framework for diverse interpretability methods in computer vision.

Cleaning and Structuring the Label Space of the iMet Collection 2020
Vivien Nguyen*, Sunnie S. Y. Kim*
CVPR 2021 Fine-Grained Visual Categorization Workshop
paper / extended abstract / code / bibtex

Cleaned and structured the noisy label space of the iMet Collection dataset for fine-grained art attribute recognition.

[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister, Olga Russakovsky
ReScience C 2021
paper (journal) / paper (arXiv) / code / openreview / bibtex

Reproducibility report on Singh et al. (CVPR 2020) that mitigates contextual bias in object and attribute recognition. One of 23/82 reports accepted for publication from the ML Reproducibility Challenge 2020.

Fair Attribute Classification through Latent Space De-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky
CVPR 2021
project page / paper / code / demo / 2min talk / 5min talk / 10min talk / bibtex

GAN-based data-augmentation method for fairer visual classification. Featured in Coursera's GANs Specialization course.

Research while at TTIC (2019 - 2020)

I was introduced to deep learning and computer vision during my gap year at TTIC. (Late by these days standards? Well I was a statistics major in undergrad. 😄) During this research-focused year, I got to explore the capabilities of AI and learned that I love doing research all day.

Information-Theoretic Segmentation by Inpainting Error Maximization
Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Gregory Shakhnarovich, David McAllester
CVPR 2021
project page / paper

Cheap, class-agnostic, and learning-free method for unsupervised image segmentation.

Deformable Style Transfer
Sunnie S. Y. Kim, Nicholas Kolkin, Jason Salavon, Gregory Shakhnarovich
ECCV 2020
project page / paper / code / demo / 1min talk / 10min talk / slides / bibtex

Integration of texture and geometry style transfer for images.

Research while at Yale (2016 - 2019)

In my undergraduate years, I was fortunate to gain research experience in various fields (e.g., statistics, neuroscience, environmental science, psychology) under the guidance of many great mentors.

Shallow Neural Networks Trained to Detect Collisions Recover Features of Visual Loom-Selective Neurons
Baohua Zhou, Zifan Li, Sunnie S. Y. Kim, John Lafferty, Damon A. Clark
eLife 2022
paper / code / bibtex

Anatomically-constrained shallow neural networks trained to detect impending collisions resemble experimentally observed LPLC2 neuron responses for many visual stimuli.

2018 Environmental Performance Index
Zachary A. Wendling, Daniel C. Esty, John W. Emerson, Marc A. Levy, Alex de Sherbinin, ..., Sunnie S. Y. Kim et al.
website / report & data / discussion at WEF18 / news 1 / news 2

A biennial evaluation of environmental health and ecosystem vitality of 180 countries, conducted by researchers at Yale and Columbia in collaboration the World Economic Forum (WEF). I built the full data pipeline and led the data analysis work.

Which Grades are Better, A’s and C’s, or All B’s? Effects of Variability in Grades on Mock College Admissions Decisions
Woo-kyoung Ahn, Sunnie S. Y. Kim, Kristen Kim, Peter K. McNally
Judgment and Decision Making 2019
paper

Psychology research study of negativity bias in decision-making.


Academic Service

Organizing Committee: NESS NextGen Data Science Day 2018

Reviewer/Program Committee:
Conferences: ICCV (2021), CVPR (2022)
Workshops: CVPR 2021 Responsible Computer Vision, CVPR 2022 Fine-Grained Visual Categorization
Challenges: ML Reproducibility Challenge (2020, 2021)

Conference Volunteer: NeurIPS (2019, 2020), ICLR (2020), ICML (2020)

Teaching & Outreach

I deeply care about increasing diversity and inclusion in STEM. As a woman in STEM who never considered pursuing a career in it before college, I experienced firsthand the importance of having a supportive environment to join and stay in the field. So I'm passionate about creating environments where women and other historically underrepresented minorities in STEM feel supported and happy :) through outreach, mentorship, and community building.

Princeton COS 429 Computer Vision
Graduate TA, Fall 2021
Princeton AI4ALL
Research Instructor, Summer 2021
TTI-Chicago Girls Who Code
Facilitator & Instructor, 2019-2020
Yale S&DS Departmental Student Advisory Committee
Co-founding Member, 2017-2019
Yale S&DS 365/565 Data Mining and Machine Learning
Undergraduate TA, Fall 2018
Yale S&DS 230/530 Data Exploration and Analysis
Undergraduate TA, Fall 2017

Website modified from here.