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Upcoming Events | Stanford HAI

Upcoming Events

AllConferenceSeminars
2027 Conference on Physics and AI (PhyStat 2027)
ConferenceMay 24, 2027
May
24
2027

The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced statistical and computational methods. 

May
24
2027

2027 Conference on Physics and AI (PhyStat 2027)

May 24, 2027

The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced statistical and computational methods. 

Data Science

Featured Videos

Hear from speakers and attendees at recent Stanford HAI events across research, policy, and education.

Previous Events at HAI

AllConferenceSeminarsWorkshops
HAI Weekly Seminar with Garance Burke - Steering Journalism Towards Data Science
SeminarFeb 21, 202011:00 AM - 12:00 PM
February
21
2020
Abstract: Algorithmic tools are transforming our daily lives, but journalism is still playing catch up. As in other times of global transition, news consumers are anxious that artificial intelligence will overtake human abilities and question whether these systems will  take our jobs, amplify racial bias or expose our privacy. As one of few technically trained data journalists, it’s clear to me that most newsrooms lack the training to understand how algorithms work, let alone how they are deployed to guide crucial decisions in hiring, banking, criminal justice and medicine. And the rapidly expanding field of algorithmic accountability reporting has yet to be codified in simple terms that most reporters can understand. Naturally, this leads to questions: How can we ensure that reporters ask the right questions? Or that a larger group of journalists can access work examining the technology's impacts on society? How can we encourage nuanced journalism about AI that accurately reflects the state of science? As an inaugural 2020 Human Centered Artificial Intelligence-John S. Knight journalism fellow, I am developing a new set of journalistic best practices to provide reporters and editors with scientifically rigorous standards for algorithmic accountability reporting. Bio: Garance Burke is an investigative journalist who applies her training in statistical analysis to reveal vital truths in the public interest. Often driven by data, her work for The Associated Press on topics ranging from immigration to cybersecurity has helped to shape presidential elections, inspire congressional hearings and spark federal investigations. As an inaugural 2020 Institute for Human-Centered Artificial Intelligence-John S. Knight Journalism fellow, she is deepening her data science skills to draft standards that will help train more reporters to produce deeper stories about the algorithmic systems they encounter on their beats. In 2019, her stories were honored as a finalist for the Pulitzer Prize in national reporting and the Anthony Shadid Award for Journalism Ethics, and received the Robert F. Kennedy Journalism Award and the National Press Club Award for Diplomatic Correspondence. Burke began her career at the Mexican financial newspaper El Financiero, then worked in Mexico City for The Washington Post and The Boston Globe. She received dual master’s degrees from the University of California, Berkeley’s Goldman School of Public Policy and Graduate School of Journalism, where she has taught as a lecturer in basic data journalism. 

HAI Weekly Seminar with Garance Burke - Steering Journalism Towards Data Science

Feb 21, 202011:00 AM - 12:00 PM
Abstract: Algorithmic tools are transforming our daily lives, but journalism is still playing catch up. As in other times of global transition, news consumers are anxious that artificial intelligence will overtake human abilities and question whether these systems will  take our jobs, amplify racial bias or expose our privacy. As one of few technically trained data journalists, it’s clear to me that most newsrooms lack the training to understand how algorithms work, let alone how they are deployed to guide crucial decisions in hiring, banking, criminal justice and medicine. And the rapidly expanding field of algorithmic accountability reporting has yet to be codified in simple terms that most reporters can understand. Naturally, this leads to questions: How can we ensure that reporters ask the right questions? Or that a larger group of journalists can access work examining the technology's impacts on society? How can we encourage nuanced journalism about AI that accurately reflects the state of science? As an inaugural 2020 Human Centered Artificial Intelligence-John S. Knight journalism fellow, I am developing a new set of journalistic best practices to provide reporters and editors with scientifically rigorous standards for algorithmic accountability reporting. Bio: Garance Burke is an investigative journalist who applies her training in statistical analysis to reveal vital truths in the public interest. Often driven by data, her work for The Associated Press on topics ranging from immigration to cybersecurity has helped to shape presidential elections, inspire congressional hearings and spark federal investigations. As an inaugural 2020 Institute for Human-Centered Artificial Intelligence-John S. Knight Journalism fellow, she is deepening her data science skills to draft standards that will help train more reporters to produce deeper stories about the algorithmic systems they encounter on their beats. In 2019, her stories were honored as a finalist for the Pulitzer Prize in national reporting and the Anthony Shadid Award for Journalism Ethics, and received the Robert F. Kennedy Journalism Award and the National Press Club Award for Diplomatic Correspondence. Burke began her career at the Mexican financial newspaper El Financiero, then worked in Mexico City for The Washington Post and The Boston Globe. She received dual master’s degrees from the University of California, Berkeley’s Goldman School of Public Policy and Graduate School of Journalism, where she has taught as a lecturer in basic data journalism. 
HAI Weekly Seminar with Lucy Suchman - Demystifying AI as an Ethical Project
SeminarFeb 14, 2020
February
14
2020

This talk develops the proposal that a central – and neglected – ethical challenge for the field of AI is demystification of the techniques and technologies that constitute it

HAI Weekly Seminar with Lucy Suchman - Demystifying AI as an Ethical Project

Feb 14, 2020

This talk develops the proposal that a central – and neglected – ethical challenge for the field of AI is demystification of the techniques and technologies that constitute it

Ethics, Equity, Inclusion
HAI Monthly Community Building Reception - A Conversation about AI Governance
Feb 11, 20204:00 PM - 5:30 PM
February
11
2020

Join California Supreme Court Justice Cuéllar, who teaches the popular “Regulating AI” course at Stanford, Dan Ho, Associate Director of HAI and professor at the law school and political science, and Terah Lyons, the Founding Executive Director of the Partnership on AI, for a conversation on the law, regulation, and governance of AI!  The three will provide a range of perspectives on the promise, challenges, and directions for AI governance.

HAI Monthly Community Building Reception - A Conversation about AI Governance

Feb 11, 20204:00 PM - 5:30 PM

Join California Supreme Court Justice Cuéllar, who teaches the popular “Regulating AI” course at Stanford, Dan Ho, Associate Director of HAI and professor at the law school and political science, and Terah Lyons, the Founding Executive Director of the Partnership on AI, for a conversation on the law, regulation, and governance of AI!  The three will provide a range of perspectives on the promise, challenges, and directions for AI governance.

Government, Public Administration
Law Enforcement and Justice
AI for Good Seminar Series: AI for Healthcare
Feb 10, 20204:30 PM - 5:30 PM
February
10
2020

AI for Healthcare session will feature Marzyeh Ghassemi who targets “Healthy ML” focusing on creating and applying machine learning to understand and improve health. Improving health requires targeting and evidence. Marzyeh tackles part of this puzzle with machine learning. This session will cover some of the novel technical opportunities for machine learning in health challenges and the important progress to be made with a careful application to domain. She will also walk through the danger of applying methods without a robust understanding of the domain, and potential downstream uses.

AI for Good Seminar Series: AI for Healthcare

Feb 10, 20204:30 PM - 5:30 PM

AI for Healthcare session will feature Marzyeh Ghassemi who targets “Healthy ML” focusing on creating and applying machine learning to understand and improve health. Improving health requires targeting and evidence. Marzyeh tackles part of this puzzle with machine learning. This session will cover some of the novel technical opportunities for machine learning in health challenges and the important progress to be made with a careful application to domain. She will also walk through the danger of applying methods without a robust understanding of the domain, and potential downstream uses.

Healthcare
HAI Weekly Seminar with Bongjun Ko - The Value of Data: An Engineer’s Perspective
SeminarFeb 07, 202011:00 AM - 12:00 PM
February
07
2020

Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.

HAI Weekly Seminar with Bongjun Ko - The Value of Data: An Engineer’s Perspective

Feb 07, 202011:00 AM - 12:00 PM

Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.

Machine Learning
Psychology-Neuroscience-Artificial Intelligence, Part 1
WorkshopFeb 06, 202012:00 AM - 2:00 PM
February
06
2020

Psychology-Neuroscience-Artificial Intelligence, Part 1

Feb 06, 202012:00 AM - 2:00 PM
Sciences (Social, Health, Biological, Physical)
AI for Good Seminar Series: AI for Government
SeminarFeb 03, 20204:00 PM - 5:30 PM
February
03
2020

AI promises to transform how government agencies work.  Where will it have the biggest impact?  What are some challenges around transparency, privacy, bias, and accountability? This talk will go beyond the headlines and share highlights of a just-completed report on AI in the US Government.

AI for Good Seminar Series: AI for Government

Feb 03, 20204:00 PM - 5:30 PM

AI promises to transform how government agencies work.  Where will it have the biggest impact?  What are some challenges around transparency, privacy, bias, and accountability? This talk will go beyond the headlines and share highlights of a just-completed report on AI in the US Government.

Economy, Markets
AI for Good Seminar Series: AI for Earth and the Environment
Jan 27, 20204:30 PM - 5:30 PM
January
27
2020

How can AI and machine learning be leveraged to mitigate the impact of human activities on earth’s natural systems?  Learn about data science tools and strategies being used to safeguard our water supply, feed the worldwide human population, and promote greater biodiversity and global sustainability.

AI for Good Seminar Series: AI for Earth and the Environment

Jan 27, 20204:30 PM - 5:30 PM

How can AI and machine learning be leveraged to mitigate the impact of human activities on earth’s natural systems?  Learn about data science tools and strategies being used to safeguard our water supply, feed the worldwide human population, and promote greater biodiversity and global sustainability.

Energy, Environment
Race and Digital Civil Society Lightning Talks & Lunch
Jan 27, 202010:45 AM - 11:45 AM
January
27
2020

Bias in government automated decision systems, the future of farmwork, digital literacy, algorithms in bail decisions, and more.

Race and Digital Civil Society Lightning Talks & Lunch

Jan 27, 202010:45 AM - 11:45 AM

Bias in government automated decision systems, the future of farmwork, digital literacy, algorithms in bail decisions, and more.

Ethics, Equity, Inclusion
HAI Weekly Seminar with Johannes Eichstaedt - Measuring Physical and Mental Health Using Social Media
SeminarJan 24, 202011:00 AM - 12:00 PM
January
24
2020

The content shared on social media is among the largest data sets on human behavior in history. I leverage this data to address questions in the psychological sciences. Specifically, I apply natural language processing and machine learning to characterize and measure psychological phenomena with a focus on mental and physical health.

 

HAI Weekly Seminar with Johannes Eichstaedt - Measuring Physical and Mental Health Using Social Media

Jan 24, 202011:00 AM - 12:00 PM

The content shared on social media is among the largest data sets on human behavior in history. I leverage this data to address questions in the psychological sciences. Specifically, I apply natural language processing and machine learning to characterize and measure psychological phenomena with a focus on mental and physical health.

 

Sciences (Social, Health, Biological, Physical)
Communications, Media
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