HAI Weekly Seminar with Akshay Chaudhari | Stanford HAI
Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
    • Subscribe to Email
  • Research

    • Research
    • Fellowship Programs
    • Grants
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs
Navigate
  • About
  • Events
  • AI Glossary
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Your browser does not support the video tag.
eventSeminar

HAI Weekly Seminar with Akshay Chaudhari

Status
Past
Date
Wednesday, November 03, 2021 10:00 AM - 11:00 AM PST/PDT
Location
Virtual
Topics
Healthcare
Overview
Watch Event Recording

Beyond Image Interpretation in Radiology: Data-Efficient AI for Accelerating MRI Acquisition

Overview
Watch Event Recording
Share
Link copied to clipboard!
Event Contact
Kaci Peel
kpeel@stanford.edu

Related Events

Arvind Narayanan | Adapting to the Transformation of Knowledge Work
May 18, 202612:00 PM - 1:00 PM
May
18
2026

The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.

Event

Arvind Narayanan | Adapting to the Transformation of Knowledge Work

May 18, 202612:00 PM - 1:00 PM

The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.

Inside the 2026 AI Index Report | Stanford HAI
SeminarMay 20, 202612:00 PM - 1:15 PM
May
20
2026

The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.

Seminar

Inside the 2026 AI Index Report | Stanford HAI

May 20, 202612:00 PM - 1:15 PM

The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.

Eyck Freymann | AI and Strategic Stability: A Framework for U.S.–China Technology Competition
SeminarMay 27, 202612:00 PM - 1:15 PM
May
27
2026

Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.

Seminar

Eyck Freymann | AI and Strategic Stability: A Framework for U.S.–China Technology Competition

May 27, 202612:00 PM - 1:15 PM

Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.

Recent applications of artificial intelligence (AI) in radiology have focused on image interpretation tasks such as image classification, segmentation, or detection. However, a fundamental challenge in radiology is to acquire these medical images in a safe and efficient manner. New AI techniques have been proposed to solve the inverse problem of image reconstruction wherein only a limited set of measurements are used to reconstruct medical images with high diagnostic quality. Specifically, in this talk, I will describe how physics-guided AI is currently being used to improve the speed of magnetic resonance imaging (MRI). I will further describe how we may eschew requiring large extents of paired datasets required for supervised model training by using novel unsupervised and semi-supervised approaches for accelerated MRI. Beyond data efficiency, these approaches can help mitigate the challenge of distribution shifts for trained models. I will conclude by describing a 1.5TB dataset that we have made publicly available to help evaluate MRI reconstructions with clinically-relevant metrics. 

Akshay Chaudhari
Assistant Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Biomedical Data Science, Stanford University