Grant Programs

HAI strives to foster a culture of AI research in which technological advancements are inextricably linked to research about their potential societal impacts. HAI builds on the  strength of Stanford research by offering three grant programs

Proposals should address significant scientific, technical or societal challenges with meaningful interdisciplinary collaborations across the University and beyond. Proposals will also be evaluated and prioritized based on their support of at least 2 of the three HAI focus areas:

Congratulations to the grant winners!  Winning proposals seek to develop the next generation of AI technologies inspired by human intelligence, the human impact of AI, and/or augmenting human capabilities.  They also foster interdisciplinary collaborations among faculty, postdocs, and students, and present new, ambitious and speculative research that will help advance and guide the future of AI. Please see below for more information on the selected projects.

2019 HAI Seed Grant Recipients

• Artificial Intelligence for Scientific Discovery
  • Susan Athey (Business)
  • David Hirshberg (Economics)
  • Guido Imbens (Business, Economics)
  • Stefan Wager (Business)

• A Causal Decision-Learning Approach for Identifying Cost-Effective Clinical Pathways
  • Mohsen Bayati (Business)
  • Merle Ederhof (CERC)
  • James Martin (Stanford Health Care)
  • Alex Chin (Radiation Oncology)
  • Jeffrey Jopling (Surgery)

• Virtual Multisensory Interaction: From Robots to Humans
  • Jeannette Bohg (Computer Science)
  • Allison Okamura (Mechanical Engineering)
  • Doug James (Computer Science)

• Statistical Machine Learning for Understanding and Improving Social Mobility Among the Poor: A Precision Approach to Intervention
  • Emma Brunskill (Computer Science)
  • Geoff Cohen (Education)

• Understanding and Addressing Ethical Challenges with Implementation of Machine Learning to Advance Palliative Care
  • Danton Char (Anesthesiology)
  • Henry Greely (Law)
  • Nigam Shah (Medicine, Biomedical Data Science)

• Administering by Algorithm: Artificial Intelligence in the Regulatory State
  • David Freeman Engstrom (Law)
  • Dan Ho (Law)
  • Tino Cuellar (Law)

• RoboIterum: An Augmented Reality Interface for Iterative Design of Situated Interactions with Intelligent Robots
  • Sean Follmer (Mechanical Engineering)
  • James Landay (Computer Science)
  • Parastoo Abtahi (Computer Science)

• Urbanization at the Margins: Edges & Seams in the Global South
  • Roz Naylor (Earth System Science)
  • Zephyr Frank (History)
  • Erik Steiner (Spatial History Project)
  • Deland Chan (H&S)
  • Leonardo Barleta (History)

• Deep Neural Network for Real-Time EEG Decoding of Musical Rhythm Imagery: Towards a Brain-Computer Interface Application for Stroke Rehabilitation
  • Takako Fujioka (Music)
  • Irán Román (Music, Neuroscience)
  • Jay McClelland (Psychology)

• Multi-modal Inference in Brains, Minds, and Machines
  • Tobias Gerstenberg (Psychology)
  • Justin Gardner (Psychology)
  • Hyo Gweon (Psychology)
  • Scott W. Linderman (Statistics, Neuroscience)

• Video-based Real Time Monitoring of Respiratory Conditions in the Pediatric Emergency Department
  • Sumit Bhargava (Pediatrics)
  • Dan Imler (Emergency Medicine)
  • Peyton Greenside (Computer Science)
  • Karan Goel (Computer Science)

• Learning to Play: Understanding Infant Development with Intrinsically Motivated Artificial Agents
  • Daniel LK Yamins (Psychology)
  • Fei-Fei Li (Computer Science)
  • Mike Frank (Psychology)
  • Nick Haber (Psychology)
  • Damian Mrowca (Computer Science)
  • Stephanie Wang (Computer Science)
  • Kun Ho Kim (Computer Science)
  • Eli Wang (Electrical Engineering)
  • Bria Long (Psychology)
  • Judy Fan (Psychology)
  • George Kachergis (Psychology)
  • Hyo Gweon (Psychology)

• Folk Theories of AI Systems: An Approach for Developing Interpretable AI
  • Jeffrey Hancock (Communication)
  • Michael Bernstein (Computer Science)
  • Sunny Liu (Communication)
  • Danae Metaxa (Computer Science)

• “Personality Design” in Artificial Intelligence-enabled Robots, Conversational Agents and Virtual Assistants
  • Pamela Hinds (Management Science and Engineering)
  • Angele Christine (Communication)
  • Prachee Jain (Management Science and Engineering)

• Using AI to Safeguard Our Drinking Water

  • Kate Maher (Earth System Science)

  • Jef Caers (Geological Sciences)

  • Bill Mitch (Civil and Environmental Engineering)

  • James Dennedy-Frank (Biology)

• Predicting Learning Outcomes with Machine Teachers

  • Roy Pea (Education), Emma Brunskill (Computer Science), Bethanie Maples (Computer Science), Joyce He (Education)

• Developing a Computational Approach for Identifying Characteristics of Psychotherapy

  • Bruce Arnow (Psychiatry and Behavioral Sciences), Stewart Agras (Psychiatry and Behavioral Sciences), Nigam Shah (Medicine, Biomedical Data Science), Fei-Fei Li (Computer Science), Adam Miner (Psychiatry and Behavioral Sciences), Albert Haque (Computer Science), Michelle Guo (Computer Science)

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

  • Michael Bernstein (Computer Science), Camille Utterback (Art and Art History)

• Modeling Finger Sense Training and Math Learning in Children

  • Allison Okamura (Mechanical Engineering)

  • Jo Boaler (Education)

  • Dorsa Sadigh (Computer Science, Electrical Engineering)

  • Melisa Orta Martinez (Mechanical Engineering)

  • Julie Walker (Mechanical Engineering)

  • Margaret Koehler (Mechanical Engineering)

• PopBots: An Army of Conversational Agents for Daily Stress Management

  • Dan Jurafsky (Computer Science, Linguistics)

  • Pablo E. Paredes (Radiology, Psychiatry and Behavioral Sciences)

• Opportunistic Screening for Coronary Artery Disease Using Artificial Intelligence

  • David Maron (Cardiovascular Medicine)

  • Alex Sandhu (Cardiovascular Medicine)

  • Fatima Rodriguez (Cardiovascular Medicine)

  • Bhavik Patel (Radiology)

  • Matthew Lungren (Radiology)

  • Curtis Langlotz (Radiology)

  • Christopher Chute (Computer Science)

  • Pranav Rajpurkar (Computer Science)

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

  • Jeffrey Hancock (Communication)

  • Gabriella Harani (Communication)

  • Jure Leskovec (Computer Science)

  • Adam Miner (Psychiatry and Behavioral Sciences)

  • Róbert Pálovics (Computer Science)

  • Katie Roehrick (Communication)

• Using Artificial Intelligence to Optimize Patient Mobility and Functional Outcomes

  • Arnold Milstein (Medicine)

  • Fei-Fei Li (Computer Science)

  • Francesca Rinaldo Salipur (CERC, General Surgery)

  • Bingbin Liu (Computer Science)

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

  • Krish Seetah (Anthropology)

  • Robert Dunbar (Earth System Science)

  • Carlos Bustamante (Biomedical Data Science, Genetics)

  • Giulio De Leo (Biology)

  • Erin Mordecai (Biology)

  • Michelle Barry (CIGH)

  • Bright Zhou (Medicine)

  • David Pickel (Classics)

  • Hannah Moots (Anthropology)

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

  • Stephen Boyd (Electrical Engineering)

  • Mohsen Bayati (Business)

  • Lei Xing (Radiation Oncology)

  • Varun Vasudevan (ICME)

  • Kate Horst (Radiation Oncology)

• The Economic Consequences of Artificial Intelligence

  • Nick Bloom (Economics)

  • Matt Gentzkow (Economics)

  • Pete Klenow (Economics)

  • Caroline Hoxby (H&S, Hoover Institution, SIEPR)

  • Tim Bresnahan (Economics)

  • Michael Webb (Economics)

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

  • Gary Darmstadt (Pediatrics-Neonatology)

  • James Zou (Biomedical Data Science)

  • Londa Schiebinger (History)

  • Ann Weber (Pediatrics)

  • Valerie Meausoone (Population Health Sciences)

• Developing Artificial Intelligence Tools for Dynamic Cancer Treatment Strategies

  • Daniel Rubin (Radiology, Biomedical Data Science)

  • Michael Gensheimer (Radiation Oncology)

  • Susan Athey (Business)

  • Ross Shachter (Management Science and Engineering)

  • Jiaming Zeng (Management Science and Engineering)

• Want out? Removing Individuals’ Data from Machine Learning Models

  • James Zou (Biomedical Data Science)

  • Greg Valiant (Computer Science)

  • Amy Motomura (Law)

2018 HAI Seed Grant Recipients

• Adversarial Examples for Humans?

  • Gregory Valiant

  • Noah Goodman

• Automated Moderation of Small Group Deliberation

  • Ashish Goel

  • James Fishkin

• “Always On” Genetic Patient Diagnosis

  • Gill Bejerano

  • Jon Bernstein

• Correcting Gender and Ethnic Biases in AI Algorithms

  • James Zou

  • Londa Schiebinger

  • Serena Yeung

  • Carlos Bustamante

• Dynamic Artificial Intelligence-Therapy for Autism on Google Glass

  • Dennis Wall

  • Tom Robinson

  • Terry Winograd

• Enabling Natural-Language Interactions in Educational Software

  • Alex Kolchinski

  • Sherry Ruan

  • Dan Schwartz

  • Emma Brunskill

• Fast, Multiphase Human-in-the-loop Optimization of Exoskeleton Assistance

  • Steven Collins

  • Emma Brunskill

• Free Exploration in Human-Centered AI Systems

  • Mohsen Bayati

  • Ramesh Johari

• Gender Bias in Conversations with Chatbots

  • Katie Roehrick

  • Jeff Hancock

  • Byron Reeves

  • Londa Schiebinger

  • James Zou

  • Garrick Fernandez

  • Debnil Sur

• Harnessing AI to Answer Questions about Diversity and Creativity

  • Dan McFarland

  • Londa Schiebinger

  • James Zou

• The Impact of Artificial Intelligence on Perceptions of Humanhood

  • Benoît Monin

  • Erik Santoro

• The Impact on Society of Autonomous Mobile Robots: A Pilot Study

  • Marco Pavone

  • Mark Duggan

  • David Grusky

• Improving Refugee Integration Through Data-Driven Algorithmic Assignment

  • Jens Hainmueller

  • Kirk Bansak

  • Andrea Dillon

  • Jeremy Ferwerda

  • Dominik Hangartner

  • Duncan Lawrence

  • Jeremy Weinstein

• Learning Behavior Change Interventions At Scale

  • Michael Bernstein

  • James Landay

• Learning Decision Rules with Complex, Observational Data

  • Xinkun Nie

  • Stefan Wager

• Learning Haptic Feedback for Motion Guidance

  • Julie Walker

  • Andrea Zanette

  • Mykel Kochenderfer

  • Allison Okamura

• Mining the Downstream Effects of Artificial Intelligence on How Clinicians Make Decisions

  • Ron Li

  • Jason Ku Wang

  • Lance Downing

  • Lisa Shieh

  • Christopher Sharp

  • Jonathan Chen

• New Moral Economy in an Age of Artificial Intelligence

  • Margaret Levi

  • Justice Mariano-Florentino Cuellar

  • Roberta Katz

  • John Markoff

  • Jane Shaw

• Novel Approach to Map Seasonal Changes in Infection Risk for Schistosomiasis: a Multi-Scale Integration of Satellite Data and Drone Imagery by Using Artificial Intelligence

  • Giulio De Leo

  • Susanne Sokolow

  • Eric Lambin

  • Zac YC Liu

  • Chris Re

  • I. Jones

  • R. Grewelle

  • A. Ratner

  • A. Lund

• Planning for Multi-Modal Human-Robot Communication

  • Yuhang Che

  • Cara Nunez

  • Allison Okamura

  • Dorsa Sadigh

• Scaling Collection of Labeled Data for Creating AI Systems through Observational Learning

  • Daniel Rubin

  • Chris Re

  • Jared Dunnmon

  • Alex Ratner

  • Darvin Yi

• Smart Learning Healthcare System for Human Behavior Change

  • Michelle Guo

  • Fei-Fei Li

  • Arnold Milstein

• Using AI to Facilitate Citizen Participation in Democratic Policy Deliberations

  • Deger Turan

  • Frank Fukuyama

  • Jerry Kaplan

  • Larry Diamond

  • Eileen Donahoe

  • Chris Potts

• Using Computer Vision to Measure Neighborhood Variables Affecting Health

  • Jackelyn Huang

  • Nikhil Naik

• Using Deep Learning for Imaging Alzheimer’s Disease with Simultaneous Ultra-low-dose PET/MRI

  • Greg Zaharchuck

  • Bill Dally

  • John Pauly

  • Elizabeth Mormino

For any questions related to the Stanford Institute for Human Centered Artificial Intelligence grant programs, please contact us at HAI-Grants@stanford.edu.