Nicole Meister (original) (raw)
| Nicole Meister Hello! 👋 I'm Nicole (she/her), a PhD student and NSF graduate research fellow at Stanford University advised by Tatsu Hashimoto and Carlos Guestrin in the Stanford NLP group. My research interests are broadly in building fair and robust AI systems. Previously, I graduated from Princeton University with a BSE in electrical and computer engineering and minors in cognitive science and robotics, where I was fortunate to be advised by Olga Russakovsky. Email / Google Scholar / Twitter |  |
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News
- Jan 2025: Paper accepted to NAACL: Benchmarking Distributional Alignment of Large Language Models
- Jan 2024: Paper accepted to ICLR: Proving Test Set Contamination for Black-Box Language Models (Outstanding Paper Honorable Mention)
- Nov 2023: Paper accepted to SatML: Unifying Corroborative and Contributive Attributions in Large Language Models
- July 2023: Paper accepted to ICCV: Gender Artifacts in Visual Datasets
- July 2022: Paper accepted to ECCV: HIVE: Evaluating the Human Interpretability of Visual Explanations
- May 2022: Graduated from Princeton 🎓: received Calvin Dodd MacCracken Senior Thesis Award (one of 3 students across all of Princeton Engineering), Sigma Xi Book Award for Outstanding Undergraduate Research (one of 4 in 40 in department)
- Sept 2021: Interned at Adobe Research, developed new reading Adobe Acrobat tool for acronym definitions and built acronym disambiguation dataset)
- March 2021: Paper accepted to ReScienceC journal [Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias (one of 23/82 reproducibility reports accepted)
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Gender Artifacts in Visual Datasets Nicole Meister*, Dora Zhao*, Angelina Wang, Vikram V. Ramaswamy, Dr. Ruth Fong, Prof. Olga Russakovsky ICCV, 2023 [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. |
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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. |
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[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. |
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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. |
Teaching & Outreach
Creating inclusive spaces is extremely important to me as supportive environments have been crucial to my decision to pursue a graduate degree.
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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) |
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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. |
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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. |
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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. |