Emily Jin

Hi — I'm Emily! I am currently pursuing my B.S. in Mathematics & M.S. in Computer Science at Stanford University.

I am a researcher in the Stanford Vision and Learning Lab (SVL), where I am fortunate to conduct research on visual reasoning with Prof. Jiajun Wu. In addition, I’ve had the wonderful opportunity to work with Prof. Li Fei-Fei and Prof. Tobias Gerstenberg on projects that explore various aspects of visual intelligence and its intersection with cognitive science. My research focuses on developing AI that understands the structure of the visual world and leverages this understanding to reason with human-like flexibility.

Beyond research, I am passionate about fostering diversity in tech and dedicated to providing engaging CS opportunities for underrepresented communities.

Email / Scholar / Github / LinkedIn

Research

My research interests lie in machine learning and computer vision, with an emphasis on visual reasoning. Inspired by human cognition, I aspire to develop machines that can perceive, understand, and reason about the visual world with human-like flexibility and adaptability. I am especially interested in learning representations and abstractions of the visual world to enable machines to perform complex, real-world reasoning tasks.

	Predicate Hierarchies Improve Few-Shot State Classification Emily Jin, Joy Hsu, Jiajun Wu ICLR, 2025 project page / paper / code
	MARPLE: A Benchmark for Long-Horizon Inference Emily Jin, Zhuoyi Huang, Jan-Philipp Fränken, Weiyu Liu, Hannah Cha, Erik Brockbank, Sarah A. Wu, Ruohan Zhang, Jiajun Wu, Tobias Gerstenberg NeurIPS Datasets and Benchmarks Track, 2024 project page / paper / code
	Whodunnit? Inferring What Happened From Multimodal Evidence Sarah A. Wu, Erik Brockbank, Hannah Cha, Jan-Philipp Fränken, Emily Jin, Zhuoyi Huang, Weiyu Liu, Ruohan Zhang, Jiajun Wu, Tobias Gerstenberg CogSci, 2024 preprint / pdf / poster / code
	Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI Emily Jin, Jiaheng Hu, Zhuoyi Huang, Ruohan Zhang, Jiajun Wu, Li Fei-Fei, Roberto Martín-Martín NeurIPS GenPlan Workshop & NeurIPS ALOE Workshop, 2023 paper / poster / code
	Modeling Dynamic Environments with Scene Graph Memory Andrey Kurenkov, Michael Lingelbach, Tanmay Agarwal, Emily Jin, Chengshu Li, Ruohan Zhang, Li Fei-Fei, Jiajun Wu, Silvio Savarese Roberto Martín-Martín ICML*, 2023 project page / paper / code
	MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing Zelun Luo, Zane Durante, Linden Li, Wanze Xie, Ruochen Liu, Emily Jin, Zhuoyi Huang, Lun Yu Li, Ruohan Zhang, Jiajun Wu, Juan Carlos Niebles, Ehsan Adeli, Li Fei-Fei NeurIPS Datasets and Benchmarks Track, 2022 project page / paper / code

Teaching & Outreach

As a young woman in STEM, I am incredibly grateful for the diverse opportunities, inspiring mentors, and inclusive communities that have supported me over the years. These experiences fuel my passion for empowering others through teaching and outreach. Here are some of the most memorable experiences I have had:

Stanford CS 231N. Deep Learning for Computer Vision.
Course Assistant, Spring 2025.

Stanford CS 157. Introduction to Logic.
Course Assistant, Fall 2024.

Stanford AI4ALL. A summer program designed to ignite students' passion for AI through early, hands-on exposure to the field — an experience that sparked my early interest in AI when I attended it in high school.
Program Manager, 2020-Present.

AIHacks. Southern California's first all-female high school hackathon.
Founder and Executive Director, 2019-2020
Media: Spectrum News / Los Angeles Daily News

Website source from Jon Barron.