Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
David Sandalow | AI for Good: Reducing Greenhouse Gas Emissions | Stanford HAI
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
  • 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

David Sandalow | AI for Good: Reducing Greenhouse Gas Emissions

Status
Past
Date
Wednesday, November 20, 2024 12:00 PM - 1:15 PM
Location
Gates Computer Science Building Room 119
Topics
Energy, Environment
Overview
Speakers

David Sandolow, Inaugural Fellow at Columbia University’s Center on Global Energy Policy, will present insights from the second edition of the Artificial Intelligence for Climate Change Mitigation Roadmap.

David Sandolow, Inaugural Fellow at Columbia University’s Center on Global Energy Policy, will present insights from the second edition of the Artificial Intelligence for Climate Change Mitigation Roadmap.

Expanding on the initial edition, the roadmap explores how AI technologies can help reduce greenhouse gas emissions in nine sectors including power, manufacturing and transportation. Key considerations include quantifying uncertainty, establishing causal relationships, and leveraging generative methods to unlock incremental and transformative opportunities.

Attendees will gain a deep understanding of the findings and leave with specific, actionable recommendations for how AI experts and others can use AI to help respond to climate change.

Resources and Opportunities:

Opportunities for funding at Stanford to apply artificial intelligence to clean energy and reducing greenhouse gas emissions: 

Undergrads:  Stanford Undergrad Program in Energy Research, for which students can propose a research topic for full-time pursuit this summer provided that a faculty member agrees to support it.

 Undergrads & Grads:  

  • Energy Impact Summer Fellowhip

  • TomKat e-Startup Internships (2025 positions to be posted soon)

  • Ecoprenuership Seed grants

  • eCatalyst Grants

Grads: 

  • Ecoprenuership Impact Founder

  • Ecoprenuerial Summer Innovation Sprint

  • Innovation Transfer Fellowship

  • Graduate Fellowships for Translational Research

  • TomKat Solutions Program

Faculty:  

  • Seed grants from Bits & Watts initiative for $100,000 on the topic of sustainably powering artificial intelligence

  • RFPs from Sustainability Accelerator in 6 solution areas; $15 million total across all topics.

Watch the full seminar.

Overview
Speakers
Share
Link copied to clipboard!
Event Contact
Annie Benisch
abenisch@stanford.edu
Related
  • AI and Sustainability: Will AI Help or Perpetuate the Climate Crisis?
    Nikki Goth Itoi
    Sep 19
    news

    Panelists in the Advancing Technology for a Sustainable Planet workshop detailed AI’s energy and regulatory challenges.

  • Move Aside, Crypto. AI Could Be The Next Climate Disaster
    Mack DeGeurin
    Apr 03
    media mention

    The 2023 AI Index report shows AI’s massive environmental and training costs, as well as increasing regulatory interest in the technology.

Related Events

Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?
SeminarApr 15, 202612:00 PM - 1:15 PM
April
15
2026

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

Seminar

Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?

Apr 15, 202612:00 PM - 1:15 PM

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

AI+Science: Accelerating Discovery
ConferenceMay 05, 20268:30 AM - 5:00 PM
May
05
2026

AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.

Conference

AI+Science: Accelerating Discovery

May 05, 20268:30 AM - 5:00 PM

AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.

Wolfgang Lehrach | Code World Models for General Game Playing
SeminarMay 13, 202612:00 PM - 1:15 PM
May
13
2026

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

Seminar

Wolfgang Lehrach | Code World Models for General Game Playing

May 13, 202612:00 PM - 1:15 PM

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.