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Hoffman-Yee Research Grants | Stanford HAI

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researchGrant

Hoffman-Yee Research Grants

Status
Closed
Date
Call for proposals will open in Winter 2025
Topics
Healthcare
Overview
Call for Proposals
2024 Grant Recipients
2022 Grant Recipients
2020 Grant Recipients
Events
Overview
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2024 Grant Recipients
2022 Grant Recipients
2020 Grant Recipients
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    This year’s winners propose innovative, bold ideas pushing the boundaries of artificial intelligence.

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    September
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  • 2023 Hoffman-Yee Symposium
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  • Four Research Teams Awarded New Hoffman-Yee Grant Funding
    Nov 13
    announcement
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    This year's research spans foundation models, health care algorithms, social values in social media, and improved chip technology.

  • DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines
    Christopher Potts, Omar Khattab, Matei Zaharia
    Jan 16
    Research
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    The ML community is rapidly exploring techniques for prompting language models (LMs) and for stacking them into pipelines that solve complex tasks. Unfortunately, existing LM pipelines are typically implemented using hard-coded “prompt templates”, i.e. lengthy strings discovered via trial and error. Toward a more systematic approach for developing and optimizing LM pipelines, we introduce DSPy, a programming model that abstracts LM pipelines as text transformation graphs, or imperative computational graphs where LMs are invoked through declarative modules. DSPy modules are parameterized, meaning they can learn how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. We design a compiler that will optimize any DSPy pipeline to maximize a given metric, by creating and collecting demonstrations. We conduct two case studies, showing that succinct DSPy programs can express and optimize pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. Within minutes of compiling, DSPy can automatically produce pipelines that outperform out-of-the-box few-shot prompting as well as expert-created demonstrations for GPT-3.5 and Llama2-13b-chat. On top of that, DSPy programs compiled for relatively small LMs like 770M parameter T5 and Llama2-13b-chat are competitive with many approaches that rely on large and proprietary LMs like GPT-3.5 and on expert-written prompt chains. DSPy is available at https://github.com/stanfordnlp/dspy

  • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
    Christopher Potts, Omar Khattab, David Broman, Josh Purtell, Michael J Ryan, Krista Opsahl-Ong, Matei Zaharia
    Nov 14
    Research
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    Language Model Programs, i.e. sophisticated pipelines of modular language model (LM) calls, are increasingly advancing NLP tasks, but they require crafting prompts that are jointly effective for all modules. We study prompt optimization for LM programs, i.e. how to update these prompts to maximize a downstream metric without access to module-level labels or gradients. To make this tractable, we factorize our problem into optimizing the free-form instructions and few-shot demonstrations of every module and introduce several strategies to craft task-grounded instructions and navigate credit assignment across modules. Our strategies include (i) program- and data-aware techniques for proposing effective instructions, (ii) a stochastic mini-batch evaluation function for learning a surrogate model of our objective, and (iii) a meta-optimization procedure in which we refine how LMs construct proposals over time. Using these insights we develop MIPRO, a novel algorithm for optimizing LM programs. MIPRO outperforms baseline optimizers on five of seven diverse multi-stage LM programs using a best-in-class open-source model (Llama-3-8B), by as high as 13% accuracy. We have released our new optimizers and benchmark in DSPy at [http://dspy.ai](http://dspy.ai).

  • Equitable Implementation of a Precision Digital Health Program for Glucose Management in Individuals with Newly Diagnosed Type 1 Diabetes
    Ramesh Johari, Ananta Addala, Franziska K Bishop, Korey Hood, Ming Yeh Lee, Victoria Y Ding, Priya Prahalad, Dessi P Zaharieva, Johannes Ferstad, Manisha Desai, David Scheinker, David Maahs
    Jul 30
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    Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases.

  • Smart Start—Designing Powerful Clinical Trials Using Pilot Study Data
    Emily Fox, Ramesh Johari, Priya Prahalad, Dessi P Zaharieva, Johannes Ferstad, Manisha Desai, David Scheinker, David Maahs
    Jan 22
    Research
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    BACKGROUND

    Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials.

    METHODS

    We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial.

    RESULTS

    Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention.

    CONCLUSIONS

    Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)

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    Aug 21
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    Social media platforms are too often understood as monoliths with clear priorities. Instead, we analyze them as complex organizations torn between starkly different justifications of their missions. Focusing on the case of Meta, we inductively analyze the company’s public materials and identify three evaluative logics that shape the platform’s decisions: an engagement logic, a public debate logic, and a wellbeing logic. There are clear trade-offs between these logics, which often result in internal conflicts between teams and departments in charge of these different priorities. We examine recent examples showing how Meta rotates between logics in its decision-making, though the goal of engagement dominates in internal negotiations. We outline how this framework can be applied to other social media platforms such as TikTok, Reddit, and X. We discuss the ramifications of our findings for the study of online harms, exclusion, and extraction.

  • Measuring receptivity to misinformation at scale on a social media platform
    Nathaniel Persily, Christopher K Tokita, Jonathan Nagler, Joshua A Tucker, Kevin Aslett, Richard Bonneau, William P Godel, Zeve Sanderson
    Sep 10
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    Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation’s initial spread. Our paper provides a more precise estimate of misinformation’s impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.

  • ReMix: Optimizing Data Mixtures for Large Scale Imitation Learning
    Dorsa Sadigh, Chethan Anand Bhateja, Joey Hejna, Karl Pertsch, Yichen Jiang
    Sep 05
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    Increasingly large robotics datasets are being collected to train larger foundation models in robotics. However, despite the fact that data selection has been of utmost importance to scaling in vision and natural language processing (NLP), little work in robotics has questioned what data such models should actually be trained on. In this work we investigate how to weigh different subsets or "domains'' of robotics datasets during pre-training to maximize worst-case performance across all possible downstream domains using distributionally robust optimization (DRO). Unlike in NLP, we find that these methods are hard to apply out of the box due to varying action spaces and dynamics across robots. Our method, ReMix, employs early stopping and action normalization and discretization to counteract these issues. Through extensive experimentation on both the Bridge and OpenX datasets, we demonstrate that data curation can have an outsized impact on downstream performance. Specifically, domain weights learned by ReMix outperform uniform weights by over 40% on average and human-selected weights by over 20% on datasets used to train the RT-X models.

Having the ability to navigate around furniture, negotiate stairs and corridors, get on and off chairs or beds, and get in and out of vehicles is essential for an individual to carry out day-to-day tasks and maintain independence. The goal of this project is to create intelligent wearable robotic devices that enable individuals with physical impairments to complete daily tasks by augmenting their capability to move in constrained spaces such as home environments. Our approach is to create an intelligent human agent which in turn trains a wearable device to be intelligent. Simply put, we use AI to teach AI. We expect the intelligent wearable devices to understand human behaviors and intentions in the context of their environments, and make decisions to complement human movement patterns and capabilities. The success of this project will expand the impact of AI to augmenting human physical skills and mobility, improving independence and quality of life for older adults and people with physical impairments.

NAME

ROLE

SCHOOL

DEPARTMENTS

Karen Liu

PI

Engineering

Computer Science

Steve Collins

Co-PI

Engineering

Mechanical Engineering

Scott Delp

Co-PI

Engineering

Bioengineering, Mechanical Engineering, Orthopaedic Surgery

Leo Guibas

Co-PI

Engineering

Computer Science, Electrical Engineering (courtesy)

VJ Periyakoil

Co-PI

School of Medicine

Primary Care

We aim to build an intelligent learning platform that improves how it teaches as it learns more about each student and students overall. Currently, most personalized tutors simply move the student forward or backward among lessons.  No systems leverage the enormous range of techniques and strategies to inspire and enable effective learning: our course might immerse students in discovery learning, orchestrate tutoring, employ teachable agents or offer discursive opportunities depending on what it learns. Using all interactions with each student -- answers, partial work, explorations, side comments -- our course will adjust the learning experience, so students receive customized content and types of instruction. To get there, our team will engage with central questions of AI algorithms and theory. We will build and test new educational tools, targeting online programming and data science learning spaces, to: automate and widen assessments of what students know and can do, facilitate effective and inclusive online peer interactions, enhance instructor efficacy, construct narratives that engage and include students, and automatically identify which activity best supports desired learning outcomes. These threads will blend together to create a joyful new learning experience when students take “the smartest course in the world.”

NAME

ROLE

SCHOOL

DEPARTMENTS

Chris Piech

PI

Engineering

Computer Science

Emma Brunskill

Co-PI

Engineering

Computer Science

Noah Goodman

Co-PI

Humanities and Sciences, Engineering

Psychology, Computer Science, Linguistics (courtesy)

James Landay

Co-PI

Engineering 

Computer Science

Jennifer Langer-Osuna

Co-PI

Graduate School of Education

 

Dan Schwartz

Co-PI

Graduate School of Education

Social interactions of all kinds require continual adaptation. Consider how the language and behavior of a medical patient and their caregiver will evolve over the course of their relationship. Initially, the patient will need to rely on detailed instructions like "Please get me the eprosartan mesylate from the downstairs bathroom. I will get some water myself". As the two adapt to each other, such descriptions will simplify to "Time for my meds" -- with a sense that implies coordinated action that is specialized to their relationship. This joint process of grounding (finding the right bottle) and adaptation (what "meds" means) is fast, largely unconscious, and essential for success.

To date, work on artificial agents has largely set these processes aside. However, as we ask these agents to interact with us in more open-ended ways, their lack of physical and social grounding is leading increasingly to poor task performance; an assistive robot acting as an in-home caregiver would be a liability if it failed to adapt to its patient and context. We are addressing these problems via interwoven efforts in characterizing the cognitive and social dynamics of the phenomena and training robots and interactive virtual agents. Our core objectives are to facilitate the development of next-generation intelligent agents and to understand the broader societal effects such technologies will have.

NAME

ROLE

SCHOOL

DEPARTMENTS

Christopher Potts

PI

Humanities and Sciences

Linguistics, Computer Science (Courtesy)

Judith Degen

Co-PI

Humanities and Sciences

Linguistics

Mike Frank

Co-PI

Humanities and Sciences

Psychology, Linguistics (courtesy)

Noah Goodman

Co-PI

Humanities and Sciences, Engineering

Psychology, Computer Science, Linguistics (courtesy)

Thomas Icard

Co-PI

Humanities and sciences

Philosophy, Computer Science (courtesy)

Dorsa Sadigh

Co-PI

Engineering

Computer Science, Electrical Engineering

Mariano-Florentino Cuéllar

Participating Faculty

Law 

Truly intelligent autonomous agents must be able to discover useful behaviors in complex environments without having humans available to continually pre-specify tasks and rewards. This ability is beyond that of today's most advanced autonomous robots.  In contrast, human infants naturally exhibit a wide range of interesting, apparently spontaneous, visuomotor behaviors - including navigating their environment, seeking out and attending to novel objects, and engaging physically with these objects in novel and surprising ways. In short, young children are excellent at playing - ``scientists in the crib'' who create, intentionally, events that are new, informative, and exciting to them. Aside from being fun, play behaviors are an active learning process, driving the self-supervised learning of representations underlying sensory judgments, motor planning capacities, and social interaction.

But how exactly do such young children know how to play, and how can we formalize and harness the  human play capacity to substantially improve the flexibility and interactivity of autonomous artificial agents? Here, we propose using deep neural network software agents, endowed with a mathematically formalized sense of ``curiosity'' and sophisticated perceptual capacities, to naturally generate playful behavior in novel environments. Combining cognitive science ideas with deep reinforcement learning, we seek to make a substantial leap in the fundamentals of AI.  From a cognitive science and clinical perspective, we will use these improved AI systems to build better quantitative models of development and improve our understanding of developmental disorders.

NAME

ROLE

SCHOOL

DEPARTMENTS

Dan Yamins

PI

Humanities and Sciences, Engineering

Psychology, Computer Science

Mike Frank

Co-PI

Humanities and Sciences

Psychology, Linguistics (courtesy)

Nick Haber

Co-PI

Education

Computer Science (courtesy)

Fei-Fei Li

Co-PI

Engineering

Computer Science

Dennis P. Wall

Co-PI

Medicine

Pediatrics, Psychiatry (courtesy), Biomedical Data Sciences 
 

Collecting revenue is a core function of government. Taxes fund nearly all public programs, from health care to environmental protection to military defense. The Internal Revenue Service (IRS) relies critically on taxpayer audits to detect under-payment and to encourage honest income reporting, but the process faces considerable challenges. 

The annual tax gap – the difference between taxes owed and paid – is nearing $500 billion. This shortfall starves the government of needed resources, while contributing to growing wealth inequality. The IRS has faced shrinking enforcement resources and a dwindling capacity to audit taxpayers for evasion or fraud, with a 42% drop in the audit rate from 2010 to 2017. Some analysts have suggested that audits excessively focus on lower-income taxpayers, and a more complete analysis and approach to these distributive concerns is important. 

In partnership with the IRS, our team is using AI and new active learning methods to help modernize our country’s system of tax collection and risk prediction. Our work seeks to develop a fair, effective, and explainable AI system for identifying tax evasion and addressing the human-centered challenges of integrating AI in a complex bureaucracy with ~10,000 diverse revenue agents, 150M taxpayers, and 1M audits annually.

NAME

ROLE

SCHOOL

DEPARTMENTS

Jacob Goldin

PI

Law

Law

Daniel Ho

Co-PI

Law

Political Science

Guido Imbens

Co-PI

Business

Economics

Anne Joseph O'Connell

Co-PI

Law

Law

Jure Leskovec

Co-PI

Engineering

Computer Science

Rebecca Lester

Co-PI

Business

Business

Humans understand the world via concepts, models that express our conception of ourselves and our society. We develop new AI technology to help humanists and social scientists trace how concepts develop and change over time and how concepts differ between groups. Our goal is to build a new kind of microscope for studying the dynamics of concept change and culture, using natural language processing and drawing on online texts from different languages, genres, and time periods, to answer deep humanistic and social science questions. Our work also enriches our understanding of how AI systems like neural network language models represent concepts themselves. Our multidisciplinary team from the humanities, social sciences, computational sciences, and the library ask questions like: How are complex concepts represented (including textual and visual elements) in modern neural networks? How do concepts become moralized over time? How do our conceptions of immigration and immigrants change with successive waves of immigrants? How do conceptions of gender and race vary across time and geography, and what are the implications for sociology and the history of science? What are the legal implications (for example for legal originalism) of conceptual and word meaning varying between groups or over time? This project applies AI to research into the social and historical dimensions of human thought, opening up new perspectives on both human and machine intelligence.

NAME

ROLE

SCHOOL

DEPARTMENTS

Ran Abramitzky

Co-PI

Humanities and Sciences

Economics

Mark Algee-Hewitt

Co-PI

Humanities and Sciences

English

R. Lanier Anderson

Co-PI

Humanities and Sciences

Philosophy

Dan Edelstein

Co-PI

Humanities and Sciences

French & Italian, History (courtesy)

Julian Nyarko

Co-PI

Stanford School of Law

Law

Dan Jurafsky

Co-PI

Humanities and Sciences, Engineering

Linguistics, Computer Science

Alison McQueen

Co-PI

Humanities and Sciences

Political Science

Londa Schiebinger

Co-PI

Humanities and Sciences

History

Rob Willer

Co-PI

Humanities and Sciences, Business (by courtesy)

Sociology, Psychology (by courtesy)

Jamil Zaki

Co-PI

Humanities and Sciences

Psychology

James Zou

Co-PI

School of Medicine

Biomedical Data Science, Computer Science (by courtesy), Electrical Engineering (by courtesy)

Catherine Coleman

Senior Personnel