Chelsea Finn

About

Chelsea Finn is a researcher focused on developing scalable AI systems to provide personalized feedback in large online computer science courses. Her work addresses the challenge of delivering high-quality, individualized feedback to thousands of students, which is traditionally labor-intensive and difficult to scale. Finn and her collaborators proposed a meta-learning based AI system that trains neural networks to analyze student code and generate feedback with minimal instructor input. This system was tested on student solutions from Stanford's CS106A exams and demonstrated feedback quality comparable to human instructors. It was successfully deployed in the Code in Place 2021 course, an online computer science offering with over 12,000 students, where the AI-generated feedback achieved a 97.9% student agreement rate, surpassing the 96.7% agreement rate for human instructor feedback. Finn's research highlights the difficulty of providing feedback at scale due to the vast diversity of student solutions, which follow a Zipf distribution, and the complexity of reasoning about student misconceptions. Her work explores computational approaches, including supervised learning and generative grading, to automate feedback and overcome the limitations of traditional methods such as unit tests and crowdsourced instructor annotations. This research contributes to advancing online education by enabling effective, scalable feedback mechanisms for open-ended student work in programming…

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Engineering
• Data Mining
• Political Science
• Human–computer interaction
• Management science
• Mathematics
• Computer vision

Selected publications

• On the Opportunities and Risks of Foundation Models

  arXiv (Cornell University) · 2021 · 2169 citations

  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(…

  PublisherOA PDFDOI

• How to train your robot with deep reinforcement learning: lessons we have learned

  The International Journal of Robotics Research · 2021 · 531 citations

  Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an ap…

  DOI

• WILDS: A Benchmark of in-the-Wild Distribution Shifts

  arXiv (Cornell University) · 2020 · 286 citations

  Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML community today. To address this gap, we present WILDS, a curated benchmark of 10 datasets reflecting a diverse range of distribution shifts that naturally arise in real-wor…

• MOPO: Model-based Offline Policy Optimization

  arXiv (Cornell University) · 2020 · 217 citations

  Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. This problem setting offers the promise of utilizing such datasets to acquire policies without any costly or dangerous active exploration. However, it is also challenging, due to the distributional shift between the offline training data and those states visited by the learned policy. Despite significant recent progress, the most successful prior methods are mo…

  DOI

• π₀: A Vision-Language-Action Flow Model for General Robot Control

  2025-06-21 · 124 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Sergey Levine

  262 shared

• Tianhe Yu

  53 shared

• Pieter Abbeel

  University of California, Berkeley

  49 shared

• Karol Hausman

  Google (United States)

  45 shared

• Rafael Rafailov

  40 shared

• Archit Sharma

  35 shared

• Annie Xie

  34 shared

• Eric Mitchell

  Neuroscience Institute

  32 shared

Labs

• Stanford AI LabPI

  Meta-learning, reinforcement learning, and computer vision research

Education

• Ph.D., Computer Science

  Stanford University

  2015

• M.S., Computer Science

  Stanford University

  2011

• B.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology (MIT)

  2007

Awards & honors

• Presidential Early Career Award for Scientists and Engineers…
• Research Fellowship, Alfred P. Sloan Foundation (2023)
• Early Academic Career Award in Robotics and Automation, IEEE…
• Young Investigator Award, Office of Naval Research (2021)
• Microsoft Faculty Fellowship, Microsoft (2020)

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