Seed Research Grants

Status

Closed

Date

Call for proposals will open in Summer 2025

Artificial Intelligence for Scientific Discovery

A Causal Decision-Learning Approach for Identifying Cost-Effective Clinical Pathways

Virtual Multisensory Interaction: From Robots to Humans

Statistical Machine Learning for Understanding and Improving Social Mobility Among the Poor: A Precision Approach to Intervention

Understanding and Addressing Ethical Challenges with Implementation of Machine Learning to Advance Palliative Care

Administering by Algorithm: Artificial Intelligence in the Regulatory State

RoboIterum: An Augmented Reality Interface for Iterative Design of Situated Interactions with Intelligent Robots

Urbanization at the Margins: Edges & Seams in the Global South

Deep Neural Network for Real-Time EEG Decoding of Musical Rhythm Imagery: Towards a Brain-Computer Interface Application for Stroke Rehabilitation

Multi-modal Inference in Brains, Minds, and Machines

Video-based Real Time Monitoring of Respiratory Conditions in the Pediatric Emergency Department

Learning to Play: Understanding Infant Development with Intrinsically Motivated Artificial Agents

Folk Theories of AI Systems: An Approach for Developing Interpretable AI

“Personality Design” in Artificial Intelligence-enabled Robots, Conversational Agents and Virtual Assistants

Using AI to Safeguard Our Drinking Water

Predicting Learning Outcomes with Machine Teachers

Developing a Computational Approach for Identifying Characteristics of Psychotherapy

Crowdsourcing Concept Art: An Art Style Classifier to Maintain Consistent Artistic Vision at Scale

Modeling Finger Sense Training and Math Learning in Children

PopBots: An Army of Conversational Agents for Daily Stress Management

Opportunistic Screening for Coronary Artery Disease Using Artificial Intelligence

Promoting Well-Being by Predicting Behavioral Vulnerability in Real-Time

Using Artificial Intelligence to Optimize Patient Mobility and Functional Outcomes

Predicting Malaria Outbreaks: AI to Learn, Classify and Predict Across Diverse Paleo-demographic, Climatic and Genomic Data

Robust Deep Neural Network Optimization with Second Order Method for Biomedical Applications

The Economic Consequences of Artificial Intelligence

Uncovering gender inequalities in East Africa: Using AI to gain insights from media data

Developing Artificial Intelligence Tools for Dynamic Cancer Treatment Strategies

Want out? Removing Individuals’ Data from Machine Learning Models

Link copied to clipboard!

Stanford HAI Funds Groundbreaking AI Research Projects
Nikki Goth Itoi
Quick ReadJan 30
news
Thirty-two interdisciplinary teams will receive $2.37 million in Seed Research Grants to work toward initial results on ambitious proposals.
Policy-Shaped Prediction: Avoiding Distractions in Model-Based Reinforcement Learning
Nicholas Haber, Miles Huston, Isaac Kauvar
Dec 13
Research
Model-based reinforcement learning (MBRL) is a promising route to sampleefficient policy optimization. However, a known vulnerability of reconstructionbased MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods —including DreamerV3 and DreamerPro — with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning.
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
Ayush Kanodia, Herman Brunborg, Keyon Vafa, Tianyu Du, Susan Athey
Dec 11
Research
Vafa et al. (2024) introduced a transformer-based econometric model, CAREER, that predicts a worker’s next job as a function of career history (an “occupation model”). CAREER was initially estimated (“pre-trained”) using a large, unrepresentative resume dataset, which served as a “foundation model,” and parameter estimation was continued (“fine-tuned”) using data from a representative survey. CAREER had better predictive performance than benchmarks. This paper considers an alternative where the resume-based foundation model is replaced by a large language model (LLM). We convert tabular data from the survey into text files that resemble resumes and fine-tune the LLMs using these text files with the objective to predict the next token (word). The resulting fine-tuned LLM is used as an input to an occupation model. Its predictive performance surpasses all prior models. We demonstrate the value of fine-tuning and further show that by adding more career data from a different population, fine-tuning smaller LLMs surpasses the performance of fine-tuning larger models.
How Persuasive Is AI-generated Propaganda?
Michael Tomz, Alex Stamos, Jason Chao, Josh A. Goldstein, Shelby Grossman
Feb 20
Research
Can large language models, a form of artificial intelligence (AI), generate persuasive propaganda? We conducted a preregistered survey experiment of US respondents to investigate the persuasiveness of news articles written by foreign propagandists compared to content generated by GPT-3 davinci (a large language model). We found that GPT-3 can create highly persuasive text as measured by participants’ agreement with propaganda theses. We further investigated whether a person fluent in English could improve propaganda persuasiveness. Editing the prompt fed to GPT-3 and/or curating GPT-3’s output made GPT-3 even more persuasive, and, under certain conditions, as persuasive as the original propaganda. Our findings suggest that propagandists could use AI to create convincing content with limited effort.
Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising
Danaë Metaxa, Michelle Lam, Ayush Pandit, Colin H. Kalicki, Poonam Sahoo, Rachit Gupta
Oct 04
Research
Algorithm audits are powerful tools for studying black-box systems without direct knowledge of their inner workings. While very effective in examining technical components, the method stops short of a sociotechnical frame, which would also consider users themselves as an integral and dynamic part of the system. Addressing this limitation, we propose the concept of sociotechnical auditing: auditing methods that evaluate algorithmic systems at the sociotechnical level, focusing on the interplay between algorithms and users as each impacts the other. Just as algorithm audits probe an algorithm with varied inputs and observe outputs, a sociotechnical audit (STA) additionally probes users, exposing them to different algorithmic behavior and measuring their resulting attitudes and behaviors. As an example of this method, we develop Intervenr, a platform for conducting browser-based, longitudinal sociotechnical audits with consenting, compensated participants. Intervenr investigates the algorithmic content users encounter online, and also coordinates systematic client-side interventions to understand how users change in response. As a case study, we deploy Intervenr in a two-week sociotechnical audit of online advertising (N = 244) to investigate the central premise that personalized ad targeting is more effective on users. In the first week, we observe and collect all browser ads delivered to users, and in the second, we deploy an ablation-style intervention that disrupts normal targeting by randomly pairing participants and swapping all their ads. We collect user-oriented metrics (self-reported ad interest and feeling of representation) and advertiser-oriented metrics (ad views, clicks, and recognition) throughout, along with a total of over 500,000 ads. Our STA finds that targeted ads indeed perform better with users, but also that users begin to acclimate to different ads in only a week, casting doubt on the primacy of personalized ad targeting given the impact of repeated exposure. In comparison with other evaluation methods that only study technical components, or only experiment on users, sociotechnical audits evaluate sociotechnical systems through the interplay of their technical and human components.
How Culture Shapes What People Want From AI
Hazel Markus, Daigo Misaki, Xiao Ge, Jeanne Tsai, Chunchen Xu
May 11
Research
There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.
Minority-group incubators and majority-group reservoirs for promoting the diffusion of climate change and public health adaptations
Alyson L Singleton, Caroline Muraida, Cesar Augusto Lopez, Ian Harryman, James Holland Jones, Mallory J Harris, Matthew Adam Turner, Ronan Forde Arthur
Jan 01
Research
Current theory suggests that heterogeneous metapopulation structures can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in simulated populations with minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations.
Interaction of a Buoyant Plume with a Turbulent Canopy Mixing Layer
Hayoon Chung, Jeffrey R Koseff
Jun 23
Research
This study aims to understand the impact of instabilities and turbulence arising from canopy mixing layers on wind-driven wildfire spread. Using an experimental flume (water) setup with model vegetation canopy and thermally buoyant plumes, we study the influence of canopy-induced shear and turbulence on the behavior of buoyant plume trajectories. Using the length of the canopy upstream of the plume source to vary the strength of the canopy turbulence, we observed behaviors of the plume trajectory under varying turbulence yet constant cross-flow conditions. Results indicate that increasing canopy turbulence corresponds to increased strength of vertical oscillatory motion and variability in the plume trajectory/position. Furthermore, we find that the canopy coherent structures characterized at the plume source set the intensity and frequency at which the plume oscillates. These perturbations then move longitudinally along the length of the plume at the speed of the free stream velocity. However, the buoyancy developed by the plume can resist this impact of the canopy structures. Due to these competing effects, the oscillatory behavior of plumes in canopy systems is observed more significantly in systems where the canopy turbulence is dominant. These effects also have an influence on the mixing and entrainment of the plumes. We offer scaling analyses to find flow regimes in which canopy induced turbulence would be relevant in plume dynamics.

researchGrant

Seed Research Grants

Status

Closed

Date

Call for proposals will open in Summer 2025

Artificial Intelligence for Scientific Discovery

Susan Athey, David Hirshberg, Guido Imbens, Stefan Wager

A Causal Decision-Learning Approach for Identifying Cost-Effective Clinical Pathways

Mohsen Bayati, Merle Ederhof, James Martin, Alex Chin, Jeffrey Jopling

Virtual Multisensory Interaction: From Robots to Humans

Jeannette Bohg, Allison Okamura, Doug James

Statistical Machine Learning for Understanding and Improving Social Mobility Among the Poor: A Precision Approach to Intervention

Emma Brunskill, Geoff Cohen

Understanding and Addressing Ethical Challenges with Implementation of Machine Learning to Advance Palliative Care

Danton Char, Henry Greely, Nigam Shah

Administering by Algorithm: Artificial Intelligence in the Regulatory State

David Freeman, Dan Ho, Tino Cuellar

RoboIterum: An Augmented Reality Interface for Iterative Design of Situated Interactions with Intelligent Robots

Sean Follmer, James Landay, Parastoo Abtahi

Urbanization at the Margins: Edges & Seams in the Global South

Roz Naylor, Zephyr Frank, Erik Steiner, Deland Chan, Leonardo Barleta

Deep Neural Network for Real-Time EEG Decoding of Musical Rhythm Imagery: Towards a Brain-Computer Interface Application for Stroke Rehabilitation

Takako Fujioka, Irán Román, Jay McClelland

Multi-modal Inference in Brains, Minds, and Machines

Tobias Gerstenberg, Justin Gardner, Hyo Gweon, Scott W. Linderman

Video-based Real Time Monitoring of Respiratory Conditions in the Pediatric Emergency Department

Sumit Bhargava, Dan Imler, Peyton Greenside, Karan Goel

Learning to Play: Understanding Infant Development with Intrinsically Motivated Artificial Agents

Daniel LK Yamins, Fei-Fei Li, Mike Frank, Nick Haber, Damian Mrowca, Stephanie Wang, Kun Ho Kim, Eli Wang, Bria Long, Judy Fan, George Kachergis, Hyo Gweon

Folk Theories of AI Systems: An Approach for Developing Interpretable AI

Jeffrey Hancock, Michael Bernstein, Sunny Liu, Danae Metaxa

“Personality Design” in Artificial Intelligence-enabled Robots, Conversational Agents and Virtual Assistants

Pamela Hinds, Angele Christine, Prachee Jain

Using AI to Safeguard Our Drinking Water

Kate Maher, Jef Caers, Bill Mitch, James Dennedy-Frank

Predicting Learning Outcomes with Machine Teachers

Roy Pea, Bethanie Maples

Developing a Computational Approach for Identifying Characteristics of Psychotherapy

Bruce Arnow, Stewart Agras, Nigam Shah, Fei-Fei Li, Adam Miner, Albert Haque, Michelle Guo

Crowdsourcing Concept Art: An Art Style Classifier to Maintain Consistent Artistic Vision at Scale

Michael Bernstein, Camille Utterback

Modeling Finger Sense Training and Math Learning in Children

Allison Okamura, Jo Boaler, Dorsa Sadigh, Melisa Orta Martinez, Julie Walker, Margaret Koehler

PopBots: An Army of Conversational Agents for Daily Stress Management

Dan Jurafsky, Pablo E. Paredes

Opportunistic Screening for Coronary Artery Disease Using Artificial Intelligence

David Maron, Alex Sandhu, Fatima Rodriguez, Bhavik Patel, Matthew Lungren, Curtis Langlotz, Christopher Chute, Pranav Rajpurkar

Promoting Well-Being by Predicting Behavioral Vulnerability in Real-Time

Jeffrey Hancock, Gabriella Harani, Jure Leskovec, Adam Miner, Róbert Pálovics, Katie Roehrick

Using Artificial Intelligence to Optimize Patient Mobility and Functional Outcomes

Arnold Milstein, Fei-Fei Li, Francesca Rinaldo Salipur, Bingbin Liu

Predicting Malaria Outbreaks: AI to Learn, Classify and Predict Across Diverse Paleo-demographic, Climatic and Genomic Data

Krish Seetah, Robert Dunbar, Carlos Bustamante, Giulio De Leo, Erin Mordecai, Michelle Barry, Bright Zhou, David Pickel, Hannah Moots

Robust Deep Neural Network Optimization with Second Order Method for Biomedical Applications

Stephen Boyd, Mohsen Bayati, Lei Xing, Varun Vasudevan, Kate Horst

The Economic Consequences of Artificial Intelligence

Nick Bloom, Matt Gentzkow, Pete Klenow, Caroline Hoxby, Tim Bresnahan, Michael Webb

Uncovering gender inequalities in East Africa: Using AI to gain insights from media data

Gary Darmstadt, James Zou, Londa Schiebinger, Ann Weber, Valerie Meausoone

Developing Artificial Intelligence Tools for Dynamic Cancer Treatment Strategies

Daniel Rubin, Michael Gensheimer, Susan Athey, Ross Shachter, Jiaming Zeng

Want out? Removing Individuals’ Data from Machine Learning Models

James Zou, Greg Valiant, Amy Motomura

Link copied to clipboard!

Stanford HAI Funds Groundbreaking AI Research Projects
Nikki Goth Itoi
Quick ReadJan 30
news
Thirty-two interdisciplinary teams will receive $2.37 million in Seed Research Grants to work toward initial results on ambitious proposals.
Policy-Shaped Prediction: Avoiding Distractions in Model-Based Reinforcement Learning
Nicholas Haber, Miles Huston, Isaac Kauvar
Dec 13
Research
Model-based reinforcement learning (MBRL) is a promising route to sampleefficient policy optimization. However, a known vulnerability of reconstructionbased MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods —including DreamerV3 and DreamerPro — with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning.
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
Ayush Kanodia, Herman Brunborg, Keyon Vafa, Tianyu Du, Susan Athey
Dec 11
Research
Vafa et al. (2024) introduced a transformer-based econometric model, CAREER, that predicts a worker’s next job as a function of career history (an “occupation model”). CAREER was initially estimated (“pre-trained”) using a large, unrepresentative resume dataset, which served as a “foundation model,” and parameter estimation was continued (“fine-tuned”) using data from a representative survey. CAREER had better predictive performance than benchmarks. This paper considers an alternative where the resume-based foundation model is replaced by a large language model (LLM). We convert tabular data from the survey into text files that resemble resumes and fine-tune the LLMs using these text files with the objective to predict the next token (word). The resulting fine-tuned LLM is used as an input to an occupation model. Its predictive performance surpasses all prior models. We demonstrate the value of fine-tuning and further show that by adding more career data from a different population, fine-tuning smaller LLMs surpasses the performance of fine-tuning larger models.
How Persuasive Is AI-generated Propaganda?
Michael Tomz, Alex Stamos, Jason Chao, Josh A. Goldstein, Shelby Grossman
Feb 20
Research
Can large language models, a form of artificial intelligence (AI), generate persuasive propaganda? We conducted a preregistered survey experiment of US respondents to investigate the persuasiveness of news articles written by foreign propagandists compared to content generated by GPT-3 davinci (a large language model). We found that GPT-3 can create highly persuasive text as measured by participants’ agreement with propaganda theses. We further investigated whether a person fluent in English could improve propaganda persuasiveness. Editing the prompt fed to GPT-3 and/or curating GPT-3’s output made GPT-3 even more persuasive, and, under certain conditions, as persuasive as the original propaganda. Our findings suggest that propagandists could use AI to create convincing content with limited effort.
Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising
Danaë Metaxa, Michelle Lam, Ayush Pandit, Colin H. Kalicki, Poonam Sahoo, Rachit Gupta
Oct 04
Research
Algorithm audits are powerful tools for studying black-box systems without direct knowledge of their inner workings. While very effective in examining technical components, the method stops short of a sociotechnical frame, which would also consider users themselves as an integral and dynamic part of the system. Addressing this limitation, we propose the concept of sociotechnical auditing: auditing methods that evaluate algorithmic systems at the sociotechnical level, focusing on the interplay between algorithms and users as each impacts the other. Just as algorithm audits probe an algorithm with varied inputs and observe outputs, a sociotechnical audit (STA) additionally probes users, exposing them to different algorithmic behavior and measuring their resulting attitudes and behaviors. As an example of this method, we develop Intervenr, a platform for conducting browser-based, longitudinal sociotechnical audits with consenting, compensated participants. Intervenr investigates the algorithmic content users encounter online, and also coordinates systematic client-side interventions to understand how users change in response. As a case study, we deploy Intervenr in a two-week sociotechnical audit of online advertising (N = 244) to investigate the central premise that personalized ad targeting is more effective on users. In the first week, we observe and collect all browser ads delivered to users, and in the second, we deploy an ablation-style intervention that disrupts normal targeting by randomly pairing participants and swapping all their ads. We collect user-oriented metrics (self-reported ad interest and feeling of representation) and advertiser-oriented metrics (ad views, clicks, and recognition) throughout, along with a total of over 500,000 ads. Our STA finds that targeted ads indeed perform better with users, but also that users begin to acclimate to different ads in only a week, casting doubt on the primacy of personalized ad targeting given the impact of repeated exposure. In comparison with other evaluation methods that only study technical components, or only experiment on users, sociotechnical audits evaluate sociotechnical systems through the interplay of their technical and human components.
How Culture Shapes What People Want From AI
Hazel Markus, Daigo Misaki, Xiao Ge, Jeanne Tsai, Chunchen Xu
May 11
Research
There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.
Minority-group incubators and majority-group reservoirs for promoting the diffusion of climate change and public health adaptations
Alyson L Singleton, Caroline Muraida, Cesar Augusto Lopez, Ian Harryman, James Holland Jones, Mallory J Harris, Matthew Adam Turner, Ronan Forde Arthur
Jan 01
Research
Current theory suggests that heterogeneous metapopulation structures can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in simulated populations with minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations.
Interaction of a Buoyant Plume with a Turbulent Canopy Mixing Layer
Hayoon Chung, Jeffrey R Koseff
Jun 23
Research
This study aims to understand the impact of instabilities and turbulence arising from canopy mixing layers on wind-driven wildfire spread. Using an experimental flume (water) setup with model vegetation canopy and thermally buoyant plumes, we study the influence of canopy-induced shear and turbulence on the behavior of buoyant plume trajectories. Using the length of the canopy upstream of the plume source to vary the strength of the canopy turbulence, we observed behaviors of the plume trajectory under varying turbulence yet constant cross-flow conditions. Results indicate that increasing canopy turbulence corresponds to increased strength of vertical oscillatory motion and variability in the plume trajectory/position. Furthermore, we find that the canopy coherent structures characterized at the plume source set the intensity and frequency at which the plume oscillates. These perturbations then move longitudinally along the length of the plume at the speed of the free stream velocity. However, the buoyancy developed by the plume can resist this impact of the canopy structures. Due to these competing effects, the oscillatory behavior of plumes in canopy systems is observed more significantly in systems where the canopy turbulence is dominant. These effects also have an influence on the mixing and entrainment of the plumes. We offer scaling analyses to find flow regimes in which canopy induced turbulence would be relevant in plume dynamics.