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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

Six Stanford research teams have received funding to solve some of the most challenging problems in AI.

Overview
Call for Proposals
2024 Grant Recipients
2022 Grant Recipients
2020 Grant Recipients
Events
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  • Stanford HAI Awards $2.75M in Hoffman-Yee Grants
    Shana Lynch
    Aug 18
    announcement

    This year’s winners propose innovative, bold ideas pushing the boundaries of artificial intelligence.

  • Stanford HAI Announces Hoffman-Yee Grants Recipients for 2024
    Nikki Goth Itoi
    Aug 21
    announcement

    Six interdisciplinary research teams received a total of $3 million to pursue groundbreaking ideas in the field of AI.

  • Stanford HAI Announces Four Hoffman-Yee Grantees
    Shana Lynch
    Oct 25
    announcement

    The second round of funding will sponsor teams that leverage AI to focus on real-world problems in health care, education, and society.

  • Hoffman-Yee Symposium
    conferenceSep 21, 2021
    September
    21
    2021
  • 2023 Hoffman-Yee Symposium
    conferenceSep 19, 20239:00 AM - 5:30 PM
    September
    19
    2023
  • Four Research Teams Awarded New Hoffman-Yee Grant Funding
    Nov 13
    announcement
    Your browser does not support the video tag.

    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
    Omar Khattab, Christopher Potts, Matei Zaharia
    Jan 16
    Research
    Your browser does not support the video tag.

    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
    Omar Khattab, David Broman, Josh Purtell, Michael J Ryan, Krista Opsahl-Ong, Christopher Potts, Matei Zaharia
    Nov 14
    Research
    Your browser does not support the video tag.

    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
    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, Ramesh Johari
    Jul 30
    Research
    Your browser does not support the video tag.

    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
    Priya Prahalad, Dessi P Zaharieva, Johannes Ferstad, Manisha Desai, David Scheinker, David Maahs, Emily Fox, Ramesh Johari
    Jan 22
    Research
    Your browser does not support the video tag.

    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.)

  • Internal Fractures: The Competing Logics of Social Media Platforms
    Chenyan Jia, Chunchen Xu, Jeanne Tsai, Jeffrey Hancock, Michael Bernstein, Angèle Christin
    Aug 21
    Research
    Your browser does not support the video tag.

    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
    Christopher K Tokita, Jonathan Nagler, Joshua A Tucker, Kevin Aslett, Richard Bonneau, William P Godel, Zeve Sanderson, Nathaniel Persily
    Sep 10
    Research

    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
    Chethan Anand Bhateja, Joey Hejna, Karl Pertsch, Yichen Jiang, Dorsa Sadigh
    Sep 05
    Research
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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.

Imagine if we could build digital twins of your cells and simulate how they would respond to a drug treatment before exposing you to the drug while considering your sex, age, and comorbidities. The key to such a medical future requires developing end-to-end frameworks for cell modeling. Recent scientific and technological advances present a historic opportunity to make unprecedented progress in our fight against human disease. Specifically, new sources of biomedical data are rapidly becoming available, and new AI techniques make it possible to understand these massive datasets. We aim to (1) create a multimodal foundation model for cells capable of capturing function and state across human tissues and individuals, (2) develop an intuitive chat model interface to augment the ability of biologists to use and understand it, and (3) showcase its capacities by modeling cells affected by the menstrual cycle to answer critical questions in women’s health, with an initial focus on cardiovascular disease management.

NAME

ROLE

SCHOOL

DEPARTMENTS

Emma Lundberg

Lead PI

Engineering

Bioengineering

Russ Altman

Co-PI

Engineering

Bioengineering

Jure Leskovec

Co-PI

Engineering

Computer Science

Stephen Quake

Co-PI

Engineering

Bioengineering

Serena Yeung-Levy

Co-PI

Medicine

Biomedical Data Science

The human brain, a complex organ influenced by psychological, biological, environmental, and physical factors, remains a significant challenge in neuroscience. Despite advances in understanding its structure, function, and biochemistry, a comprehensive, data-driven model integrating these diverse aspects has yet to be realized. Existing research has made strides with brain foundation models using MRI data and multi-modal approaches, but gaps remain in integrating various modalities and domains. This project aims to develop a “Brain World Model,” named “Brain-Bind,” by unifying datasets that reflect different aspects of brain health. This model will be trained in an end-to-end manner, harmonizing data sources, developing modality-specific encoders, and employing self-supervised learning to integrate non-imaging data with MRI-derived representations. The resulting model is intended to enhance diagnostic precision, inform personalized care, and contribute to a deeper understanding of neurological and cognitive processes.

NAME

ROLE

SCHOOL

DEPARTMENTS

Ehsan Adeli

Lead PI

Medicine

Psychiatry and Behavioral Sciences

Akshay Chaudhari

Co-PI

Medicine

Radiology

Anshul Kundaje

Co-PI

Medicine

Genetics

Fei-Fei Li

Co-PI

Engineering

Computer Science

Feng Vankee Lin

Co-PI

Medicine

Psychiatry and Behavioral Sciences

Killian Pohl

Co-PI

Medicine

Psychiatry and Behavioral Sciences

Jiajun Wu

Co-PI

Engineering

Computer Science

Dan Yamins

Co-PI

Humanities and Sciences

Psychology

Massive datasets are the cornerstone for developing large language models (LLMs) and other generative AI. However, these datasets have also sparked debates regarding generative AI, highlighted by several copyright disputes involving OpenAI. This proposal is dedicated to exploring critical aspects of data creation and attribution for generative AI. Our approach is three-fold: Firstly, we aim to establish guiding principles and scaling laws for assembling datasets tailored for training and aligning LLMs and other generative AI, ensuring their responsible application in various sectors. Secondly, we plan to develop scalable methods to trace the generative AI outputs to specific training data, enabling data attribution and valuation. Finally, we will investigate how to effectively use and monitor synthetic data produced by generative AI. These goals are closely linked and mutually reinforcing, with successful data attribution and synthetic data methods informing and improving the strategies for dataset design. Throughout our project, we will ground our research with real-world legal and policy considerations and high-impact applications in law and medicine.

NAME

ROLE

SCHOOL

DEPARTMENTS

James Zou

Lead PI

Medicine

Biomedical Data Science

Surya Ganguli

Co-PI

Humanities and Sciences

Applied Physics

Tatsunori Hashimoto

Co-PI

Engineering

Computer Science

Daniel Ho

Co-PI

Law

Law 

Curt Langlotz

Co-PI

Medicine

Radiology

Percy Liang

Co-PI

Engineering

Computer Science

Mark Lemley

Co-PI

Law 

Law 

Megan Ma

Senior/Lead Research Scholar

Law

Law

Julian Nyarko

Co-PI

Law

Law 

Christopher Ré

Co-PI

Engineering

Computer Science

Ellen Vitercik

Co-PI

Engineering 

Management Science and Engineering

DNA encodes the fundamental language for all living organisms. Recently, large language models have been used to learn this mysterious biological language to unlock a better understanding of this blueprint of life. Yet, learning from DNA has its distinct challenges over natural language - it’s extremely long, with the human genome over 3 billion nucleotides in length. It’s also highly sensitive to small changes, where a single point mutation can mean the difference between having a disease or not. Overcoming these technical challenges of modeling long sequences in DNA can lead to a deeper understanding of human disease, the creation of novel therapeutics, and the possibility to engineer life itself.

This project aims to develop a new line of long sequence language models that can reproduce the organization of DNA sequences from the molecular to the whole genome scale. We will build on the Hyena architecture, an efficient long sequence model that leverages breakthroughs in deep signal processing and scales sub-quadratically with the length of data. We will extend the long convolutions of Hyena with a bidirectional and diffusion training paradigm. This approach will enable the modeling and design of DNA sequences from scratch as well as with an “infilling” ability, allowing both greater control and the mimicking of evolution’s process of continuous updating. Our team is dedicated to leading the ethical development of DNA sequence modeling and design, and to bring the innovation of AI systems for the betterment of human health.

NAME

ROLE

SCHOOL

DEPARTMENTS

Brian Hie

Lead PI

Engineering

Chemical Engineering

Christopher Ré

Co-PI

Engineering

Computer Science

Stefano Ermon

Co-PI

Engineering

Computer Science

Stephen Baccus

Co-PI

Medicine

Neurobiology

Euan Ashley

Co-PI

Medicine

Cardiovascular Medicine

Visual media, in the form of images, video, animation, and 3D virtual environments are now central to the way people communicate stories, ideas and information. Yet, creating such visual content is challenging as it requires significant visual design expertise. The promise of modern generative AI tools is that they will assist users in creating production-quality visual content from a simple text prompt describing what the user wants. But current black-box AI are difficult to work with; the AI often misinterprets the intent of the user and users lack a predictive conceptual model for what the AI will produce given an input prompt. This mutual lack of a theory of mind leads to a collaboration by trial-and-error, where the user repeatedly tries different prompts hoping to find one that will produce the desired output.

In this project we take major steps towards allowing both entities (humans and AI) to develop a shared conceptual grounding that allows each to simulate how the other might operate given an input task. To this end, we focus on two key objectives. (1) First we will identify the concepts and mental processes expert human creators use when they make visual content. Such experts often convey their design intentions via natural language and sketches. We will analyze these linguistic and sketch outputs to identify common patterns in their workflows and we will investigate how such creators communicate with other human collaborators to establish common ground, repair misunderstandings, etc. (2) Second, we will build new generative AI tools that internally respect the ways humans mentally organize creation processes and workflows. For this objective, we will develop new algorithms and methods that incorporate the concepts humans use within generative AI models.

Ultimately we envision human creators collaborating with generative AI tools using a combination of natural language, example content and code snippets, in a turn-taking fashion to produce the desired content. Importantly, both the human and AI will communicate with one another through a shared understanding of the concepts and mental processes relevant to the creation task. We will validate our approach with human-subject experiments that examine how our generative AI tools lower usability barriers for creating production-quality visual content. With our generative AI tools we aim to democratize visual content creations so that users of all skill levels can easily express their ideas and tell their stories using visual media.

NAME

ROLE

SCHOOL

DEPARTMENTS

Maneesh Agrawala

Lead PI

Engineering

Computer Science

Judith Fan

Co-PI

Humanities and Sciences

Psychology

Kayvon Fatahalian

Co-PI

Engineering

Computer Science

Tobi Gerstenberg

Co-PI

Humanities and Sciences

Psychology

Nick Haber

Co-PI

Education

Graduate School of Education

Hari Subramonyam

Co-PI

Education

Graduate School of Education

Jiajun Wu

Co-PI

Engineering

Computer Science

Police body-worn cameras have been at the center of police reform efforts over the past decade. Yet the vast majority of the footage generated by those cameras is never examined, undermining the camera’s utility as a tool for accountability and improving interactions between the police and community members. We are harnessing artificial intelligence (AI) and large language models (LLMs) to unlock the research potential of body-worn camera footage to better understand the nature of law enforcement’s encounters with the public. In turn, leveraging the resulting insights could fuel both the development and systematic evaluation of officer trainings and other institutional interventions designed to improve policing. To advance these goals, we are building AI tools and state-of-the-art infrastructure on Stanford’s campus to receive, secure, and process police footage for the purpose of conducting research aimed at improving relations between the police and the public. This includes body-worn camera footage of routine vehicle stops from several law enforcement agencies. We will begin by using this data to evaluate the effectiveness of a state-wide legal intervention designed to improve police-community interactions during vehicle stops, eradicate racial disparities in those stops, and increase trust. We will also explore the effectiveness of training and other methods to reduce escalation. In a world where police departments are increasingly utilizing AI to fight crime, we see the value of harnessing AI to make accessible a largely untapped source of data to improve police-community relations – a goal that can be lauded by the police and the policed alike. Using AI in this way could be crucial to advancing solutions to one of society’s most pressing problems: how can we reimagine public safety?

NAME

ROLE

SCHOOL

DEPARTMENTS

Jennifer Eberhardt

Lead PI

Graduate School of Business

Graduate School of Business

Ralph Banks

Co-PI

Law

Law 

Dan Jurafsky

Co-PI

Humanities and Sciences

Linguistics

Benoit Monin

Co-PI

Graduate School of Business

Graduate School of Business