Nicole Meister

Hello! 👋 I'm Nicole (she/her/hers), a senior at Princeton University studying computer science and electrical engineering, with minors in cognitive science and robotics.

Currently, I work in the Princeton VisualAI Lab and I am lucky to be advised by Prof. Olga Russakovsky and Sunnie Kim. This past summer, I worked with Dr. Franck Dernoncourt in Adobe Research's NLP Group.

This fall, I am applying to Ph.D. programs to pursue my interest in machine learning and computer vision, with a larger goal of building fair and interpretable AI systems. Throughout my undergraduate career, I have worked towards this goal from three fronts:

(1) research projects mitigating bias in computer vision models and improving evaluation of interpretability methods
(2) research engineering projects to make technology more accessible and apply computer science to new problems
(3) direct outreach to teach computer science to students who identify with marginalized groups.

In my spare time, I enjoy exploring the outdoors 🍂through running, biking, and hiking.

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn

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.

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.

Analyzed international child marriage trends to identify relationship with human trafficking metrics and visualize change in child marriage rates.
Findings presented to US Embassy in Bangladesh, included in USAID Report, and in review at Journal of Human Trafficking.

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.

Explored data augmentation techniques and noise injection that simulates instrument noise to improve robustness of CNN that classifies X-Ray images.

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

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)

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).

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

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.

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

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)

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

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)

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

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)

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.

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.

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.

Design and source code from Jon Barron's website