Nicole Meister

Hello! 👋 I'm Nicole (she/her/hers), a first-year PhD student and NSF graduate fellow at Stanford University starting Fall 2022.

My research interests are broadly in computer vision and machine learning, particularly in building fair, robust, and interpretable AI systems.

I graduated from Princeton University with a BSE in electrical and computer engineering and minors in cognitive science and robotics. I was fortunate to work in the Princeton VisualAI Lab and be advised by Prof. Olga Russakovsky.

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Gender Artifacts in Visual Datasets
Nicole Meister*, Dora Zhao*, Angelina Wang, Vikram V. Ramaswamy, Dr. Ruth Fong, Prof. Olga Russakovsky
arxiv, 2022
[project page] [paper] [code]

We explore to what extent gendered information can truly be removed from the dataset. We develop a framework to identify gender artifacts, or visual cues that are correlated with gender.

HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Dr. Ruth Fong, Prof. Olga Russakovsky
ECCV, 2022
[project page] [paper] [code]

Human evaluation framework for diverse interpretability methods in computer vision. We identify two desiderata for explanations used to assist human decision making:
(1) Explanations should allow users to distinguish between correct and incorrect predictions.
(2) Explanations should be understandable to users.

[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
[journal] [arXiv] [openreview] [code]

Participated in ML Reproducibility Challenge 2020 and reproduced from scratch Singh et al. (CVPR 2020) that mitigates contextual bias in object and attribute recognition.
One of 23/82 reports accepted for publication to ReScience C, a peer-reviewed journal for new implementations and explicit replications of previously published papers.

Methods to Identify Patient Clusters and Build Precision Analytics for Diagnosis
Nicole Meister, Hannah Cowley, Corban Rivera, Karla M Gray-Roncal, Kathryn Fitzgerald, Claudia Allshouse, Anna Duerr, Aalok Shah, Paul Nagy, Peter A Calabresi, Antony Rosen, Ellen M Mowry, William R Gray-Roncal
Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS) , 2019
[poster] [abstract]

Implemented machine learning algorithms to predict prognosis of Multiple Sclerosis patients (able to predict walk time within 1 second accuracy). Used k-means to cluster patients and produce more accurate predictions.

Research Experiences

I have had the privilege of working with some amazing labs and groups.

Adobe NLP Research Intern (Summer 2021)
Nicole Meister, Walter Chang, Tushar Dublish, Franck Dernoncourt
[presentation] [video] [20 second demo] [media] [award]

New reading assistant tool in Acrobat (SmartAcronyms) that extracts acronyms and their definition from text with 95% accuracy, improved speed (21x) of existing acronym extraction method while maintaining precision and recall.
Selected to present internship project to Adobe CEO and Adobe Research VP at 2021 Adobe Award Symposium.
Adobe Research Women-In-Tech Scholarship Winner($10k)

Junior Independent Work (Spring 2021)
Nicole Meister, Sunnie Kim, Olga Russakovsky
[presentation] [paper] [code]

Decision rule method to selectively choose when to apply a model that performed well on out-of-context images and one that performed well on in-context images.
Explored image prediction confidence estimates and saliency heatmaps (interpretability methods).

UCSD REU (Summer 2020)
Nicole Meister, Dillon Hicks, Prof. Ryan Kastner
[presentation] [demo] [code] [award]

Web application to allow conservation groups to use CNNs to quantify and monitor mangroves. Finalist for NCWIT Collegiate Award.

Max Planck Institute for Collective Behavior (Summer 2019)
Nicole Meister, Jake Graving, Prof. Ian Couzin

Python based toolkit to identify and classify tactile interactions from a locust experiment video. Used toolkit to analyze locust limb interactions to better understand individual behavior.


I am grateful to my professors for their mentorship in projects exploring in algorithmic bias, interpretability, and more.

Anyone Can Skateboard: Tracing Sources of Gender Biases in Classifiers
Nicole Meister*, Dora Zhao*, Prof. Jia Deng
Advanced Computer Vision (Grad) Course Project, Fall 2021
[paper] [code]

Exploring the role of contextual objects in gender bias of computer vision models. (Python, Pytorch)

Does This Looks Like That? Comparing Human and ProtoPNet Prototypical Representations
Nicole Meister, Prof. Tom Griffiths
Computational Models of Cognition Course Project, Fall 2021
[paper] [code]

Conducted human study to evaluate and understand if the representations and prototypes identified by a computer vision interpretability method capture a sense of typicality and align with a human's concept of prototypes. (Python)

Exploring Debiasing Sentence Representation
Grace Cuenca*, Leslie Kim*, Nicole Meister*, Prof. Karthik Narasimhan, Prof. Danqi Chen
NLP Course Project, Spring 2021
[paper] [poster] [code]

Reproduced Liang et. al method to debias sentence representations, researched effects of size of context window on method (Python)

Dance Dance Revolution (DDR) Robot
Nicole Meister*, Janet Wang*
Robotics Course Project, Spring 2021
[report] [video]

Omni-wheeled robot that plays DDR by using computer vision to process DDR videos to extract dance moves (up, down left, right) to move and flash LED strip colors accordingly (Arduino, Python)

Exploring Impact of Facial Feature Segmentation on American Sign Language Recognition
Helen Chen*, Grace Cuenca*, Nicole Meister*, Prof. Olga Russakovsky
Computer Vision Course Project, Fall 2020
[paper] [code]

CNN-LSTM to recognize ASL gestures with 95% accuracy (Pytorch, Python)

Princeton Dance Schedule
Helen Chen*, Angela Li*, Nicole Meister*, Edward Tian*, Prof. Robert Dondero
Advanced Programming Techniques Course Project, Spring 2020
[website] [presentation] [project overview] [programmer's guide] [user's guide] [code]

Website to schedule and book dance studios. I worked on front-end and back-end to implement a scheduling algorithm to replace 40 hours of administrative work (currently used by Princeton Arts Council to schedule spaces) Advisor: Prof. Dondero (Python, Django, SQL, HTML/CSS, JavaScript, JQuery, Bootstrap, Heroku)

Teaching & Outreach

Creating inclusive spaces is extremely important to me as supportive environments have been crucial to my decision to pursue a graduate degree. I am extremely privileged to study at Princeton and want to (1) help Princeton students succeed (2) liberate the resources of an elite institution to support and uplift others.

Princeton TA & Grader
TA: Hold office hours, debug and grade assignments and exams.
Grader: Provide feedback to students regarding style, efficiency, design
ECE/COS 306 TA: Contemporary Logic Design (Fall 2021)
COS 429 TA & Grader: Computer Vision (Fall 2021)
COS 217 Grader: Introduction to Programming Systems (Spring 2020)
Peer Tutor for Introductory Computer Science Classes (Fall 2019-Spring 2021)

Data Science for Social Good
Founder (Summer 2020, 2021)
[2020 recap] [report] [example lessons]

Created a free 6-week course to empower high school students from historically underrepresented minority groups with the skills to leverage data science and web development.
Taught 20 hours/week online, prepared detailed lesson plans, mentored 21 students to complete data science project.
Improved program in second year by recruiting 10 program alumni to teach, advertise, and coordinate logistics of program.

Princeton Science Olympiad
Co-Director, 2020
[report] [video] [photos]

Organized high school science competition, managing 30 person team, $23k budget, 140 volunteers, 800 participants.
Prioritized accessibility, becoming first ever invitational to provide travel scholarships to teams and no attendance fee.

Princeton Outdoor Action (Freshman Orientation)

Leader Trainer, Technical Skills Trainer, DEI Committee
Plan and lead a weeklong immersion program through backcountry backpacking.
Teach other leaders-in-training necessary backcountry backpacking techniques and other leadership skills.

Design and source code from Jon Barron's website