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Upcoming Events | Stanford HAI

Upcoming Events

AllConferenceSeminars
Daniel Wurgaft | A Resource Rational Analysis of Representations: Geometry, Inference, Capacity
SeminarNov 09, 20264:00 PM - 5:30 PM
November
09
2026

A Center for Neural Data Science Seminar Series

November
09
2026

Daniel Wurgaft | A Resource Rational Analysis of Representations: Geometry, Inference, Capacity

Nov 09, 20264:00 PM - 5:30 PM

A Center for Neural Data Science Seminar Series

Data Science
Marlowe | AI + Data for Science with Alex Pentland
SeminarNov 11, 20264:30 PM - 5:30 PM
November
11
2026

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

November
11
2026

Marlowe | AI + Data for Science with Alex Pentland

Nov 11, 20264:30 PM - 5:30 PM

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

Data Science
Khaoula Chehbouni | Best Practices for the Use of LLMs as Judges
SeminarNov 13, 20261:00 PM - 2:15 PM
November
13
2026

An AI Measurement Science (AIMS) Seminar Series

November
13
2026

Khaoula Chehbouni | Best Practices for the Use of LLMs as Judges

Nov 13, 20261:00 PM - 2:15 PM

An AI Measurement Science (AIMS) Seminar Series

Data Science
Special Seminar and Fireside Chat: Understanding Work Using AI
Nov 13, 20262:00 PM - 4:00 PM
November
13
2026

Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.

November
13
2026

Special Seminar and Fireside Chat: Understanding Work Using AI

Nov 13, 20262:00 PM - 4:00 PM

Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.

Machine Learning
Industry, Innovation
Government, Public Administration
Regulation, Policy, Governance
Christopher Ong | Research Acceleration: The View Inside OpenAI
Nov 16, 202612:00 PM - 1:00 PM
November
16
2026

In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.

November
16
2026

Christopher Ong | Research Acceleration: The View Inside OpenAI

Nov 16, 202612:00 PM - 1:00 PM

In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.

Machine Learning
Industry, Innovation
AI100 | AI Goes Mainstream: The Scaling Era and the Rise of Generative AI
SeminarNov 18, 202612:00 PM - 1:15 PM
November
18
2026

This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.

November
18
2026

AI100 | AI Goes Mainstream: The Scaling Era and the Rise of Generative AI

Nov 18, 202612:00 PM - 1:15 PM

This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.

Foundation Models
Industry, Innovation
Democracy
Marlowe | AI + Data for Science with Andreas Tolias
SeminarNov 18, 20264:30 PM - 5:30 PM
November
18
2026

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

November
18
2026

Marlowe | AI + Data for Science with Andreas Tolias

Nov 18, 20264:30 PM - 5:30 PM

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

Data Science
Marlowe | AI + Data for Science with Leonidas Guibas
SeminarDec 02, 20264:30 PM - 5:30 PM
December
02
2026

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

December
02
2026

Marlowe | AI + Data for Science with Leonidas Guibas

Dec 02, 20264:30 PM - 5:30 PM

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

Data Science
Angèle Christin & Hannah Zeavin | Gurus, Hucksters, Entertainers: How Influencers Reshaped Social Media
SeminarDec 09, 202612:00 PM - 1:15 PM
December
09
2026

Please join us for an illuminating investigation of the complex work of social media content creators—their lives, conflicts, and controversies. 

December
09
2026

Angèle Christin & Hannah Zeavin | Gurus, Hucksters, Entertainers: How Influencers Reshaped Social Media

Dec 09, 202612:00 PM - 1:15 PM

Please join us for an illuminating investigation of the complex work of social media content creators—their lives, conflicts, and controversies. 

Communications, Media
YoungJu Jo | Data-driven Discovery and Control of Neural Computations
SeminarDec 14, 20264:00 PM - 5:30 PM
December
14
2026

A Center for Neural Data Science Seminar Series

December
14
2026

YoungJu Jo | Data-driven Discovery and Control of Neural Computations

Dec 14, 20264:00 PM - 5:30 PM

A Center for Neural Data Science Seminar Series

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 John Markoff - Second Thoughts on Digital Utopianism
SeminarMar 27, 202011:00 AM - 12:00 PM
March
27
2020

Bio: John Markoff is HAI’s Journalist-in-Residence. He is also a research affiliate at the Center for Advanced Study in the Behavioral Sciences or CASBS, participating in projects focusing on the future of work and artificial intelligence. He is currently researching a biography of Stewart Brand, the creator of the Whole Earth Catalog. Previously he was a Berggruen Fellow at CASBS. He has also been a staff historian at the Computer History Museum in Mountain View, Calif. Until 2017, he was a reporter at The New York Times, beginning in March 1988 as the paper’s national computer writer. Prior to joining the Times, he worked for the San Francisco Examiner. He has written about technology for Pacific News Service. He was a reporter at Infoworld and West Coast editor for Byte Magazine and wrote a column on personal computers for the San Jose Mercury. He has also been a lecturer at the University of California at Berkeley School of Journalism and an adjunct faculty member of the Stanford Graduate Program on Journalism. In 2013 he was awarded a Pulitzer Prize in explanatory reporting as part of a New York Times project on labor and automation. In 2007, he was named a fellow of the Society of Professional Journalists, the organization’s highest honor. In June of 2010, the New York Times presented him with the Nathaniel Nash Award, which is given annually for foreign and business reporting. He is the co-author of The High Cost of High Tech, published by Harper & Row. He co-wrote Cyberpunk: Outlaws and Hackers on the Computer Frontier published Simon & Schuster. Hyperion published Takedown: The Pursuit and Capture of America's Most Wanted Computer Outlaw, which he co-authored with Tsutomu Shimomura. What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry, was published by Viking Books. Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots, was published by HarperCollins Ecco. Markoff grew up in Palo Alto, California, and graduated from Whitman College, Walla Walla, Washington. He attended graduate school at the University of Oregon and received a masters degree in sociology.

HAI Weekly Seminar with John Markoff - Second Thoughts on Digital Utopianism

Mar 27, 202011:00 AM - 12:00 PM

Bio: John Markoff is HAI’s Journalist-in-Residence. He is also a research affiliate at the Center for Advanced Study in the Behavioral Sciences or CASBS, participating in projects focusing on the future of work and artificial intelligence. He is currently researching a biography of Stewart Brand, the creator of the Whole Earth Catalog. Previously he was a Berggruen Fellow at CASBS. He has also been a staff historian at the Computer History Museum in Mountain View, Calif. Until 2017, he was a reporter at The New York Times, beginning in March 1988 as the paper’s national computer writer. Prior to joining the Times, he worked for the San Francisco Examiner. He has written about technology for Pacific News Service. He was a reporter at Infoworld and West Coast editor for Byte Magazine and wrote a column on personal computers for the San Jose Mercury. He has also been a lecturer at the University of California at Berkeley School of Journalism and an adjunct faculty member of the Stanford Graduate Program on Journalism. In 2013 he was awarded a Pulitzer Prize in explanatory reporting as part of a New York Times project on labor and automation. In 2007, he was named a fellow of the Society of Professional Journalists, the organization’s highest honor. In June of 2010, the New York Times presented him with the Nathaniel Nash Award, which is given annually for foreign and business reporting. He is the co-author of The High Cost of High Tech, published by Harper & Row. He co-wrote Cyberpunk: Outlaws and Hackers on the Computer Frontier published Simon & Schuster. Hyperion published Takedown: The Pursuit and Capture of America's Most Wanted Computer Outlaw, which he co-authored with Tsutomu Shimomura. What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry, was published by Viking Books. Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots, was published by HarperCollins Ecco. Markoff grew up in Palo Alto, California, and graduated from Whitman College, Walla Walla, Washington. He attended graduate school at the University of Oregon and received a masters degree in sociology.

HAI Weekly Seminar with Marietje Schaake
SeminarMar 20, 202011:00 AM - 12:00 PM
March
20
2020

Bio: Marietje Schaake is an International Policy Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and the International Policy Director of the Cyber Policy Center, where she conducts policy-relevant research focused on cyber policy recommendations for industry and government. In addition to her own research, she represents the center to governments, NGOs, and the technology industry. Schaake also teaches courses on cyber policy from an international perspective, and brings to Stanford leaders from around the world to discuss cyber policy.  Prior to joining Stanford, Marietje Schaake led an active career in politics and civic service. She was a representative of the Dutch Democratic Party and the Alliance of Liberals and Democrats for Europe (ALDE) in European Parliament where she was first elected in 2009. In European Parliament, Schaake focused on trade, foreign policy and technology, and as a member of the Global Commission on the Stability of Cyberspace, and founder of the European Parliament Intergroup on the European Digital Agenda, Schaake develops solutions to strengthen the rule of law online, including initiating the net neutrality law now in effect throughout Europe.

HAI Weekly Seminar with Marietje Schaake

Mar 20, 202011:00 AM - 12:00 PM

Bio: Marietje Schaake is an International Policy Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and the International Policy Director of the Cyber Policy Center, where she conducts policy-relevant research focused on cyber policy recommendations for industry and government. In addition to her own research, she represents the center to governments, NGOs, and the technology industry. Schaake also teaches courses on cyber policy from an international perspective, and brings to Stanford leaders from around the world to discuss cyber policy.  Prior to joining Stanford, Marietje Schaake led an active career in politics and civic service. She was a representative of the Dutch Democratic Party and the Alliance of Liberals and Democrats for Europe (ALDE) in European Parliament where she was first elected in 2009. In European Parliament, Schaake focused on trade, foreign policy and technology, and as a member of the Global Commission on the Stability of Cyberspace, and founder of the European Parliament Intergroup on the European Digital Agenda, Schaake develops solutions to strengthen the rule of law online, including initiating the net neutrality law now in effect throughout Europe.

HAI Weekly Seminar with Brian Cantwell Smith - Reckoning and Judgment: The Promise of AI
SeminarMar 06, 202011:00 AM - 12:00 PM
March
06
2020

Abstract: New developments in Artificial Intelligence, particularly deep learning and other forms of “second-wave” AI, are attracting enormous public attention.  Both triumphalists and doomsayers are predicting that human-level AI may be “just around the corner.”  To assess whether that prediction is true, we need a broad understanding of intelligence, in terms of which to assess: (i) what kinds of intelligence machines currently have, and will likely have in the future; and (ii) what kinds of intelligence people currently have, and may be capable of in the future.  As the first step in this direction, I distinguish two kinds of intelligence: (i) “reckoning,” the kind of calculative rationality that computers excel at, including both first- and second-wave AI; and (ii) “judgment,” a form of dispassionate, deliberative thought, grounded in ethical commitment and responsible action, that is appropriate to the situation in which it is deployed.  AI will develop world-changing reckoning systems, I argue, but nothing in AI as currently conceived approaches what is required to build a system capable of judgment. 

Bio: Brian Cantwell Smith is Reid Hoffman Professor of Artificial Intelligence and the Human at the University of Toronto, where he is also Professor of Information, Philosophy, Cognitive Science, and the History and Philosophy of Science and Technology, as well as being a Senior Fellow at Massey College.   Smith’s research focuses on the philosophical foundations of computation, artificial intelligence, and mind, and on fundamental issues in metaphysics and epistemology.  In the 1980s he developed the world’s first reflective programming language (3Lisp).  He is the author of *On the Origin of Objects* (MIT Press, 1996), and of *On the Promise of Artificial Intelligence: Reckoning and Judgment* (MIT Press, 2019).

HAI Weekly Seminar with Brian Cantwell Smith - Reckoning and Judgment: The Promise of AI

Mar 06, 202011:00 AM - 12:00 PM

Abstract: New developments in Artificial Intelligence, particularly deep learning and other forms of “second-wave” AI, are attracting enormous public attention.  Both triumphalists and doomsayers are predicting that human-level AI may be “just around the corner.”  To assess whether that prediction is true, we need a broad understanding of intelligence, in terms of which to assess: (i) what kinds of intelligence machines currently have, and will likely have in the future; and (ii) what kinds of intelligence people currently have, and may be capable of in the future.  As the first step in this direction, I distinguish two kinds of intelligence: (i) “reckoning,” the kind of calculative rationality that computers excel at, including both first- and second-wave AI; and (ii) “judgment,” a form of dispassionate, deliberative thought, grounded in ethical commitment and responsible action, that is appropriate to the situation in which it is deployed.  AI will develop world-changing reckoning systems, I argue, but nothing in AI as currently conceived approaches what is required to build a system capable of judgment. 

Bio: Brian Cantwell Smith is Reid Hoffman Professor of Artificial Intelligence and the Human at the University of Toronto, where he is also Professor of Information, Philosophy, Cognitive Science, and the History and Philosophy of Science and Technology, as well as being a Senior Fellow at Massey College.   Smith’s research focuses on the philosophical foundations of computation, artificial intelligence, and mind, and on fundamental issues in metaphysics and epistemology.  In the 1980s he developed the world’s first reflective programming language (3Lisp).  He is the author of *On the Origin of Objects* (MIT Press, 1996), and of *On the Promise of Artificial Intelligence: Reckoning and Judgment* (MIT Press, 2019).

HAI Weekly Seminar with Thomas Dimson - Algorithms Algorithms Algorithms
SeminarFeb 28, 202011:00 AM - 12:00 PM
February
28
2020

Abstract: The biggest challenge with the democratization of content is how to make sense of the scale. In the last decade, curation of content has consolidated into the hands of a few of the largest technology companies. Today, that curation takes the form of machine learning — often dubbed algorithms by the media. Thomas helped build and introduce the most controversial algorithms of Instagram: non-chronological feed and personalized recommendations. He will discuss challenges from the perspective of an engineer in the control room as Instagram scaled to serve over a billion people. Thomas will share a few of his thoughts about future directions as we start to form a dialogue about the responsibilities of platforms operating on a global scale.

Bio: Thomas Dimson is the original author of “The Algorithm” — the recommender systems behind Instagram's feed, stories and discovery surfaces. He joined Instagram as one of its first 50 employees in 2013, working for seven years as a principal engineer and eventually an engineering director. In that time, he also invented products such as the stories polling sticker, Hyperlapse, and engineering and was named one of the top ten most creative people in business by Fast Company. Thomas graduated from the University of Waterloo with a bachelor's of mathematics and received his master's in computer science from Stanford with a specialization in artificial intelligence.

HAI Weekly Seminar with Thomas Dimson - Algorithms Algorithms Algorithms

Feb 28, 202011:00 AM - 12:00 PM

Abstract: The biggest challenge with the democratization of content is how to make sense of the scale. In the last decade, curation of content has consolidated into the hands of a few of the largest technology companies. Today, that curation takes the form of machine learning — often dubbed algorithms by the media. Thomas helped build and introduce the most controversial algorithms of Instagram: non-chronological feed and personalized recommendations. He will discuss challenges from the perspective of an engineer in the control room as Instagram scaled to serve over a billion people. Thomas will share a few of his thoughts about future directions as we start to form a dialogue about the responsibilities of platforms operating on a global scale.

Bio: Thomas Dimson is the original author of “The Algorithm” — the recommender systems behind Instagram's feed, stories and discovery surfaces. He joined Instagram as one of its first 50 employees in 2013, working for seven years as a principal engineer and eventually an engineering director. In that time, he also invented products such as the stories polling sticker, Hyperlapse, and engineering and was named one of the top ten most creative people in business by Fast Company. Thomas graduated from the University of Waterloo with a bachelor's of mathematics and received his master's in computer science from Stanford with a specialization in artificial intelligence.

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 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
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
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
HAI Weekly Seminar with Subutai Ahmad - Sparsity in the neocortex, and its implications for machine learning
SeminarJan 10, 202011:00 AM - 12:00 PM
January
10
2020

Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.

HAI Weekly Seminar with Subutai Ahmad - Sparsity in the neocortex, and its implications for machine learning

Jan 10, 202011:00 AM - 12:00 PM

Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.

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