Krish Seetah | AI, Archaeology, and Archives: How Data Science is Helping to Reveal Past Epidemics | 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

eventSeminar

Krish Seetah | AI, Archaeology, and Archives: How Data Science is Helping to Reveal Past Epidemics

Status
Past
Date
Wednesday, February 22, 2023 10:00 AM - 11:00 AM PST/PDT
Location
Hybrid
Topics
Sciences (Social, Health, Biological, Physical)
Healthcare
Share
Link copied to clipboard!
Event Contact
Madeleine Wright
mwright7@stanford.edu

Related Events

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.

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.

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.

Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata
Apr 13, 202612:00 PM - 1:00 PM
April
13
2026

Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.

Event

Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata

Apr 13, 202612:00 PM - 1:00 PM

Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.

At no time in recent memory has the impact of disease on society been more palpable. But how do we study the nexus between society, ecology, and disease? Our team utilizes a precise and novel integration of archaeological, historical, anthropological, climatic, and ancient human and pathogen genetic datasets, using a longitudinal lens to achieve a better understanding of disease impact- specifically malaria - over time. Rich, robust, and large datasets are drawn from two regional contexts capturing critical periods in global disease transformations. Data science approaches are used to extricate critical features of the human-malaria relationship over the last 300 years, and despite the research being in the early stages of development, have already revealed key aspects of how socio-political factors influenced the impact of the disease on human lives.

Speaker
Krish Seetah
Associate Professor, Stanford Doerr School of Sustainability, of Oceans, of Anthropology; Senior Fellow, Woods Institute for the Environment, Stanford University