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

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

Skip to content
  • About

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

    • Research
    • Research Programs
    • Grants
    • Marlowe (opens in new tab)
    • 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
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

Carina Hong | AI That Can Prove It: From Mathematical Discovery to Verified Software
Oct 19, 202612:00 PM - 1:00 PM
October
19
2026

A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.

Event

Carina Hong | AI That Can Prove It: From Mathematical Discovery to Verified Software

Oct 19, 202612:00 PM - 1:00 PM

A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.

Nicholas Branigan | Non-ergodicity and Simpson’s paradox in neural correlates
SeminarOct 19, 20264:00 PM - 5:00 PM
October
19
2026

A Center for Neural Data Science Seminar Series

Event

Nicholas Branigan | Non-ergodicity and Simpson’s paradox in neural correlates

Oct 19, 20264:00 PM - 5:00 PM

A Center for Neural Data Science Seminar Series

Bay Area Tech Economics Seminar with Rehan Khan, Snowflake
SeminarOct 20, 20266:30 PM - 8:30 PM
October
20
2026

The Bay Area Tech Economics Seminar is for graduate-trained economists and other quantitative social scientists from academia, policy, or the private sector who are interested in the tech economy and methods for analyzing it, and in making connections with others in this broadly defined space.

Seminar
Your browser does not support the video tag.

Bay Area Tech Economics Seminar with Rehan Khan, Snowflake

Oct 20, 20266:30 PM - 8:30 PM

The Bay Area Tech Economics Seminar is for graduate-trained economists and other quantitative social scientists from academia, policy, or the private sector who are interested in the tech economy and methods for analyzing it, and in making connections with others in this broadly defined space.