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Seed Grant Recipients 2022

The Seed Research Grants are designed to support new, ambitious, and speculative ideas with the objective of getting initial results.

  • How might we augment human capabilities by enabling people to practice challenging interpersonal interactions? Workers in an organization, for example, regularly face challenging conversations with their managers and colleagues. Such skills today are typically taught by experts in person or via videoconference, which is time-consuming and costly. Access to trainers who can teach these skills to learners is limited. Can we make such training tools more broadly accessible, developing processes to help prepare people for these challenging interpersonal interactions? Our project will develop interpersonal skill training tools that embody simulated social roles from large pretrained language models such as GPT-3. We will innovate algorithms and interfaces for responsibly eliciting this wide variety of simulated actors from a large language model, and exploring how these large language models can be adapted responsibly to various social roles in different contexts to achieve positive outcomes.

    Name

    Role

    School

    Department

    Diyi Yang

    Main PI

     School of Engineering

    Computer Science

    Michael Bernstein

    Co-PI

     School of Engineering

    Computer Science

    Jeffrey Hancock

    Co-PI

    School of Humanities and Sciences

    Communication

  • Digital media platforms (e.g. social media, video sharing, online video games, etc.) often feature attention-seeking and attention-absorbing design elements that are powered by AI algorithms to maximize user interaction and time spent engaging with the experiences. Beyond streamlining online experiences, such algorithm-fueled excessive consumption of digital media may constitute addictive behavior patterns and may impose detrimental effects on mental health. With the growing interest from policymakers to regulate such mechanisms, we need better evidence about the addictive risks of AI-powered engagement mechanisms to meaningfully inform legislation to reduce harm. Our work takes a user-centric, real-world approach to analyzing digital addiction. We focus on collecting real-time, first-hand data of usage patterns on smartphones, including usage patterns across specific apps, and the addictive platform features engaged with. This approach will inform human-centered policy solutions to curtail design features driving digital addiction. In addition, to allow for ongoing syndromic monitoring, we seek to develop self-report measures of AI-powered addiction of digital experiences, leveraging the team’s expertise in addiction, survey design and psychometric evaluation. 

    Name

    Role

    School

    Department

    Anna Lembke

    Main PI

    School of Medicine

    Psychiatry and Behavioral Sciences

    Johannes Eichstaedt

    Co-PI

    School of Humanities and Sciences

    Psychology

  • Abstract coming soon.

    Name

    Role

    School

    Department

    Edith Sullivan

    Main PI

    School of Medicine

    Psychiatry and Behavioral Sciences

    Ehsan Adeli

    Co-PI

    School of Medicine

    Psychiatry and Behavioral Sciences

    Christine Gould

    Co-PI

    School of Medicine

    Psychiatry and Behavioral Sciences

    Feng Lin

    Co-PI

    School of Medicine

    Psychiatry and Behavioral Sciences

    Arnold Milstein

    Co-PI

    School of Medicine

    Med/Hospital Medicine

  • AI will influence the future of people from many diverse backgrounds. There is an urgent need to incorporate the perceptions, imaginings, concerns, and creativity of diverse groups into AI developments. In the US, many AI products tend to reflect values and philosophical traditions of European American contexts. The fact that people in Black, Latinx, Asian and Indigenous communities may have different ideas and concerns when it comes to AI and its potential has received much less attention. The rapid advancement of AI can further propagate cultural views of groups that are currently in power, potentially creating a self-reinforcing cycle of inequity.

    The lack of a diverse cultural grounding for AI theorists and developers limits the space of the imaginary. Tapping into a variety of cultural ideas can catalyze new categories of AI and create broader societal and environmental benefits. Currently, inclusive AI developments are hindered by a dearth of first-hand knowledge about how underrepresented groups actually imagine and think of AI. We propose to empirically study conceptions of AI held by group members from diverse cultural backgrounds. Our efforts will be a necessary first step to guide critical applications of cultural insights to broadening the intellectual foundations of AI.

    Name

    Role

    School

    Department

    Hazel Markus

    Main PI

    School of Humanities and Sciences

    Psychology

    Michele Elam

    Co-PI

    School of Humanities and Sciences

    English

    Jeanne Tsai

    Co-PI

    School of Humanities and Sciences

    Psychology

  • Scientists and journalists often communicate research findings to lay people through data visualizations and text (e.g., the positive and negative effects of caffeine intake, the effectiveness of indoor masking in reducing infection, the impact of human activity on global climate, etc.). Well crafted articles allow readers to attend to both text and chart elements to contextualize data, extract factual knowledge, build connections between data and concepts, as well as generate and check hypotheses. For example, in an article about Rising Global Temperatures (Figure 1), the text, might reference an upward trend in global temperature between 2000 and 2019, or the point with the highest value in 2016. At each such text reference, an active reader can choose to examine the chart, identify the aspect of the data being emphasized by the text (e.g. the segment of the time series between 2000 and 2019), and may also see the surrounding context (e.g., an El Niño caused record setting temperature in 2016).

    In this case, the text incorporates information that is not present in the visualization to explain the feature it emphasizes. In this project, the team will explore AI-augmented reading experiences that link text with visualizations to facilitate information extraction, processing, and sensemaking. By developing computational models of reader behavior, the team will create mixed-initiative tools for readers of interactive documents. For instance, our tools will guide active reading by automatically highlighting correspondences between text and visualization, will prompt readers to externalize their knowledge through annotations and interactivity, and will support interactive question-answering.

    Name

    Role

    School

    Department

    Maneesh Agrawala

    Main PI

    School of Engineering

    Computer Science

    Hariharan Subramonyam

    Co-PI

    Graduate School of Education

    Graduate School of Education

  • Abstract coming soon.

    Name

    Role

    School

    Department

    E.J. Chichilnisky

    Main PI

    School of Medicine

    Neurosurgery

  • Cancer cells evade their host’s immune system by negatively regulating T cells via immune checkpoints. By blocking these checkpoint proteins, the ability of the immune system to recognize and kill cancer cells restores. Despite the promising results, individual response rate of checkpoint blockade varies greatly among patients. Furthermore, there exist no good biomarkers to predict an individual’s response to immune checkpoint inhibitors (ICI). In this project, we will develop and validate a novel deep learning strategy for stratification of immunotherapy patients based on the patient’s gene mutation data.  We will first develop a transformer neural network-based model with explicitly incorporation of prior biological knowledge about the gene pathways by encoding the protein-protein interactions via graph convolution. A novel method to embed individual’s binary mutation data to encode both PPI and gene occurrence information will be established to facilitate the inference process. Combined with the self-attention mechanism of the transformer model, the proposed framework proposes to yield the interpretable biomarkers for individualized cancer immunotherapy with immune checkpoint Inhibitors.

    Name

    Role

    School

    Department

    Lei Xing

    Main PI

    School of Medicine

    Radiation Oncology

    Art Owen

    Co-PI

    School of Humanities and Sciences

    Statistics

  • Patients present to the Emergency Department (ED) with diverse and rapidly progressive disease states for which the necessity and optimal timing of life-saving interventions is difficult to predict. ED patients are routinely connected to sophisticated monitors that continuously measure several dimensions of physiology, which are known to predict a wide variety of disease outcomes. Yet the vast majority of this monitoring data is discarded, and is inaccessible to clinicians. We propose to develop and validate PhysioHub, an AI-powered analysis and prediction platform for patient physiologic monitoring. PhysioHub will synthesize real-time data from the electronic health record and from continuously monitored vital signs and physiologic waveforms, and deploy multimodal deep learning models to enhance the ED physician’s ability to understand her patients’ physiology, clinical risks, and likely responses to therapeutic interventions. We aim to augment the capability of ED physicians to target optimal care at the most critical periods in the clinical course, while reducing the burden of repetitive monitoring tasks and distracting false alarms.

    Name

    Role

    School

    Department

    David Kim

    Main PI

    School of Medicine

    Emergency Medicine

  • Abstract coming soon.

    Name

    Role

    School

    Department

    Jon-Paul Pepper

    Main PI

    School of Medicine

    Otolaryngology - Head & Neck Surgery

    Serena Yeung

    Co-PI

    School of Medicine

    Biomedical Data Science

  • Illegal fishing is a global issue that threatens our food security and livelihoods. A key to preventing illegal fishing is seafood traceability, but a common practice of transshipping catches between vessels at sea could obscure the source of the catches if it is not properly monitored. By contrast, transshipment in ports is considered to offer more tractable monitoring and enforcement, making them a promising approach for enhanced traceability. However, there is currently no systematic tool to observe in-port transshipments, and consequently, we still have a poor understanding of broad patterns of transshipment in ports. Our project aims to create a new capacity to track in-port transshipments from satellite imagery by harnessing recent AI developments toward better fisheries monitoring and enforcement.

    Name

    Role

    School

    Department

    James Leape

    Main PI

    School of Sustainability

    Center for Ocean Solutions

    Trevor Hastie

    Co-PI

    School of Humanities and Sciences

    Statistics

    Fiorenza Micheli

    Co-PI

    School of Sustainability

    Center for Ocean Solutions

    Serena Yeung

    Co-PI

    School of Medicine

    Biomedical Data Science

  • The overall goal of this project is to enable robots to creatively solve physical puzzles by manipulating or combining objects in novel ways. While robots are more and more capable of manipulating a variety of objects, humans distinguish themselves in their capacity to generalize their manipulation skills to novel objects, scenarios, or goals - often done in only a handful of attempts of trial and error. Currently, robots lack this creativity, flexibility, and fast adaptation which ultimately will be required when deploying them into environments they have never seen before or into environments that their creators did not anticipate (e.g. disaster zones or other planets). The research proposed here develops and tests computational models of physical problem solving, serving the twin goals of (1) equipping robots with this capacity, and (2) better understanding the underlying cognitive mechanisms that support this capacity in humans. The core research question addressed in this proposal is how an agent can effectively reason about the infinitely many ways of interacting with novel objects in order to achieve various goals in previously unseen scenarios.

    Name

    Role

    School

    Department

    Jeannette Bohg

    Main PI

    School of Engineering

    Computer Science

    Tobias Gerstenberg

    Co-PI

    School of Humanities and Sciences

    Psychology

  • There is a growing interest in neuromodulation techniques for their potential to treat various mental conditions. In particular, repetitive transcranial magnetic stimulation (or TMS) of the dorsolateral prefrontal cortex (DLPFC) is an FDA approved treatment for patients affected by drug-resistant major depression. Several studies have demonstrated the benefits of personalized TMS targeting that accounts for inter-patient brain structure and head shape variability rather than the current standardized approach. For clinicians, there are trade-offs between the targeting precision and the time and complexity of the targeting protocol. Here we leverage evidence that there is a correlation between head features and brain anatomy in humans, to train a compact AI model to predict brain target regions based on a patients’ head features. To assure better model performance, the supervised training phase will be guided by the specific information contained on the MRI scans following a student-teacher approach. As an initial step, the developed model will be focused on localizing the DLPFC for treating depression symptoms. A similar design can be used at a later stage to improve the external targeting of other cortical regions.

    Name

    Role

    School

    Department

    Jennifer McNab

    Main PI

    School of Medicine

    Radiology

    Ehsan Adeli

    Co-PI

    School of Medicine

    Psychiatry and Behavioral Sciences

    Nolan Williams

    Co-PI

    School of Medicine

    Psychiatry and Behavioral Sciences

  • Online advertising largely sustains the news we consume everyday. The use of AI-powered algorithmic bidding and targeting in advertising affects the revenue generated by different news websites, thereby incentivizing the creation of different types of content. However, despite concerns that the digital advertising business model inherently amplifies harmful content, the presence of a bias towards publishing such content based on its potential for monetization by ads has not been empirically investigated. To address this gap, our project investigates whether the advertising-based business model of mainstream digital platforms amplifies partisanship and misinformation. Specifically, we will examine 1) whether advertising on misinformation and partisan content performs better than advertising on other content, and 2) which types of ads are more likely to be algorithmically placed on misinformation and partisan websites. In examining the factors underlying machine behavior in this setting, we will uncover how algorithmic advertising shapes the incentives of which news gets monetized and sustained.

    Name

    Role

    School

    Department

    Erik Brynjolfsson

    Main PI

    Dean of Research

    Human-Centered Artificial Intelligence/Digital Economy Lab

  • A major challenge in containing wildfires is monitoring and preventing spot fire spread. Spot fires are an increasingly prevalent route of wildfire spread in which the main fire launches burning pieces of wood called firebrands downwind that ignite new fires upon landing. Spot fires are notoriously unpredictable and can ignite as far as a mile or more from the main fire. Physics-informed machine learning could be a powerful tool for analyzing spot fire spread, but a lack of understanding of the rich physical dynamics governing firebrand transport prevents the development of effective algorithms. To address this, we are performing laboratory experiments to examine the mechanisms that guide firebrand transport. We model firebrand transport in a water flow-tunnel by releasing particles (firebrands) in a heated plume (smoke column) in a canopy (forest) subject to a current (wind). By utilizing standard fluid mechanics modeling approaches we are able to examine the impact of various physical features such as forest structure, wind speed, and flame intensity on the variability of firebrand trajectories. These features are typically already known from existing wildfire data sets or can be easily obtained for future fires, so the results from our experiments will enable physics-informed modeling of spot fire spread. Achieving better spot fire spread models should, in turn, lead to more effective mitigation strategies.

    Name

    Role

    School

    Department

    Jeffrey Koseff

    Main PI

    School of Sustainability

    Civil and Environmental Engineering

    Nicholas Ouellette

    Co-PI

    School of Sustainability

    Civil and Environmental Engineering

  • Abstract coming soon.

    Name

    Role

    School

    Department

    Allison Okamura

    Main PI

    School of Engineering

    Mechanical Engineering

    Jeannette Bohg

    Co-PI

    School of Engineering

    Computer Science

    Carla Pugh

    Co-PI

    School of Medicine

    Surgery

  • Our team's primary research aim is to study how medical AI algorithms should be evaluated and regulated. While AI algorithms have made significant strides in the research world, only a fraction of them have received regulatory approval, and even fewer actually reach the patients they are intended to serve. An imminent challenge in medical AI lies in ensuring its safe and effective deployment in healthcare settings. Our team's primary research goal is to study the gap between research and deployment through the lens of clinical trials and regulation. Our research will focus on a) advancing a data-driven understanding of the regulatory landscape for medical AI in the US and b) developing computational methods for assessing and reducing performance disparities in evaluation and deployment.

    Name

    Role

    School

    Department

    James Zou

    Main PI

    School of Medicine

    Biomedical Data Science

    Daniel Ho

    Co-PI

    School of Law

    Law School

  • In this proposed research we will pilot an approach to: 1) identify ethical issues that may emerge with development and multi-site deployment of a healthcare AI (AI-HC) application for risk assessment of pulmonary embolus (PE); and 2) develop consensus on how to address these ethical issues, once identified. We will also 3) develop consensus on an ethics “label” to communicate ethical constraints. In doing 1, 2 & 3 we will refine a generalizable approach for identifying and addressing ethical challenges with an AI-HC as well as provide guidance for how to communicate ethical concerns with future AI-HC.

    No systematic approach has yet emerged regarding how to survey the landscape of AI-HC conception, development, calibration, implementation, evaluation, and oversight.  Bereft of any conceptual map of this landscape, the identification of ethical concerns arising from this emerging, complex, cross-disciplinary technology that potentially affects many aspects of healthcare has thus far been reactive, ad hoc, and fragmented.  This is problematic, especially for so-called “wicked” problems, which unlike more straightforward and “tame” technical problems, typically defy a singular formulation of the problem, are nested within systems that have interrelated problems, and have social values woven into their fabric such that solutions are not simply true or false but rather better or worse. In this research, we aim to enhance our ability to identify – proactively, systematically, and in a more thoroughgoing and integrated manner – the variety of ethically relevant decisions and their ethically relevant consequences regarding AI-HC (through this initial case study of AI-HC for PE).

    Name

    Role

    School

    Department

    Danton Char

    Main PI

    School of Medicine

    Anesthesiology

    Hank Greely

    Co-PI

    Law School

    Law School

    Michelle Mello

    Co-PI

    Law School

    Law School

    Nigam Shah

    Co-PI

    School of Medicine

    Biomedical Informatics

    Melissa Valentine

    Co-PI

    School of Engineering

    Management Science and Engineering

  • Large language models (LLMs) have increasingly been incorporated into real-world settings, including high-stakes use cases that involve value judgments such as content moderation and toxicity detection. Such use cases, however, may require language models to make value judgments that are shaped by cultural and societal norms that can differ vastly across different parts of the world. If an LLM is biased toward a particular value system or culture (e.g., Western or American), it could result in adverse downstream effects such as imposing hegemonic worldviews and homogenizing users’ perspectives and belief systems. Therefore, we propose to study the cultural views and values reflected by LLMs, and how these representations may shape human perception of different cultures.

    Name

    Role

    School

    Department

    Tatsunori Hashimoto

    Main PI

    School of Engineering

    Computer Science

  • The symbolic AI models of the mid-twentieth century were designed to leverage valuable insights about causal structure, ranging from commonsense reasoning to advanced scientific knowledge. These insights were expressed directly in hand-designed structures, and this ensured that model behaviors were systematic and human-interpretable. Unfortunately, these models tended also to be brittle and narrowly specialized, limiting their utility. By contrast, present-day AI models are general-purpose and data-driven, and they can flexibly acquire a range of complex behaviors, which has opened up many new avenues for productive applications of AI. However, the trade-offs are now evident: these models often find opaque, unsystematic solutions, and they are completely dependent on available data. As a result, a model that performs well in testing can turn out to be dangerous and unpredictable once deployed in the wider world. To address this, we have begun to lay the foundation for allowing AI models to incorporate important insights about causal structure while still learning in a data-driven fashion. In this method, we define high-level causal models, usually in symbolic terms, and then train neural networks to conform to the structure of those models while also learning specific tasks. The central technical piece of this proposal is interchange intervention training (IIT), in which we swap internal representations in the target neural model in a way that is guided by the input–output behavior of the causal model. IIT objectives are easily defined, fully differentiable, and provide a formal guarantee that, if the loss is minimized, the target model is a causal abstraction of the neural network. For the present project, we will use IIT to induce causal structure in massive foundation models and to distill large language models, and we will more robustly quantify the extent to which IIT leads models to robustly acquire capabilities that are only sparsely represented in the training data.

    Name

    Role

    School

    Department

    Christopher Potts

    Main PI

    School of Humanities and Sciences

    Linguistics

    Thomas Icard

    Co-PI

    School of Humanities and Sciences

    Philosophy

  • Cancer immunotherapy represents one of the most important advances in modern medicine. Specifically, the discovery of immune checkpoints led to the regulatory approval and clinical use of a number of therapeutic agents for cancer treatment. Immunotherapy has dramatically improved long-term survival for some patients and is now the standard of care for treating most advanced cancers. Unfortunately, only a small proportion of patients respond to immunotherapy and would experience clinical benefit. Moreover, these drugs may cause serious side effects and are substantially more costly. Therefore, it is crucial to identify, on a personalized basis, which patients will respond to and benefit from immunotherapy. In this project, we propose to develop interpretable AI systems by integrating multi-modal radiology, pathology, and clinical data for predicting immunotherapy response in cancer patients.  Because this information is obtained from data that are routinely available, the proposed model could be readily integrated into clinical workflows and have a positive impact on cancer patients.

    Name

    Role

    School

    Department

    Ruijiang Li

    Main PI

    School of Medicine

    Radiation Oncology - Radiation Physics

    Maximilian Diehn

    Co-PI

    School of Medicine

    Radiation Oncology - Radiation Therapy

  • Over the last few years, millions of children have had short or long term disruptions to in-person formal school. While AI educational tools could be a key part of the solution, ensuring such software is effective is often challenging. In part this is because the outcomes of interest, such as passing a class or graduation, are often delayed, which makes it hard for adaptive methods like reinforcement learning to learn quickly enough to be practical. Other methods, such as static causal discovery, often are designed for a single environment and may not transfer to different student distributions or when new pedagogical content or interventions are introduced. In collaboration with our partner War Child, that supports children in conflict regions, we will use and advance new methods to develop automatic ways of identifying short-term proxy variables that can be used for data-driven AI software optimization that can transfer. 

    Name

    Role

    School

    Department

    Emma Brunskill

    Main PI

    School of Engineering

    Computer Science

  • Emerging adulthood—the transitional period from late teens through mid-twenties—is a particularly important time for studying social, economic, and health risk factors. During this period, emerging adults experience multiple changes to their daily lives, such as leaving their family home, gaining independence in activities of daily living, and increased social, financial, and academic responsibilities. As a result, emerging adulthood is a pivotal point for health over the life course, yet traditional epidemiologic methods are severely limited in their ability to measure human behavior during dynamic periods. Traditional epidemiologic data collection often relies on self-reported behavior and has long gaps between collection waves. This project seeks to leverage the ubiquity of smartphone ownership among emerging adults, combined with modern algorithmic inference, to quantify dynamic human behavior and exposures at fine spatiotemporal resolution. We seek to understand how comparable metrics based on traditional data collection are to those derived from novel, digital phenotyping data collection and build AI-based models to compare how predictive these metrics are of different health behaviors. Importantly, a primary goal is to understand the experiences of participants themselves, especially among traditionally excluded groups such as lower-income or racially minoritized participants, to understand how smartphone data collection can be used to improve both data quality and gather data from more generalizable samples. 

    Name

    Role

    School

    Department

    Mathew Kiang

    Main PI

    School of Medicine

    Epidemiology and Population Health

    Gabriella Harari

    Co-PI

    School of Humanities and Sciences

    Communication

    Johannes Eichstaedt

    Co-PI

    School of Humanities and Sciences

    Psychology

  • Our team will explore applications of artificial intelligence in the study of artistic creative process.  We will leverage technical resources from fields such as machine learning and computer vision, integrate contemporary research perspectives from the humanities, and focus on artists and artworks connected to Stanford’s Asian American Art Initiative.  Representative technical aims include the use of shape fingerprinting and manifold learning methods to characterize the geometry of an artist’s trajectory in creating new forms over time; application of mixed-modal clustering to glean new insights from digital artists’ archives; and investigation of principled approaches to surfacing stereotypes, stemming from or projected onto art objects, that may be embedded in large generative models.

    Name

    Role

    School

    Department

    Hideo Mabuchi

    Main PI

    School of Humanities and Sciences

    Applied Physics

    Marci Kwon

    Co-PI

    School of Humanities and Sciences

    Art and Art History

    Jean Ma

    Co-PI

    School of Humanities and Sciences

    Art and Art History

  • Geometric principles are the basis of engineering, architecture, and other human activities. Furthermore, geometry as a discipline has been used for centuries to scaffold the development of rigorous mathematical thinking in students. How do humans learn to generalize geometric concepts and prove geometric propositions? Can AI systems for geometric reasoning augment human learning during formal education? We aim to answer these questions by developing a framework for constructing geometric principles, studying human cognition compared to computer vision algorithms, and creating neuro-symbolic systems to model geometric reasoning. These components will allow us to understand human sensitivities to geometry and develop AI systems to solve increasingly complex, real-world geometric programs — achieving our goal of leveraging AI to challenge and tutor human geometry students.

    Name

    Role

    School

    Department

    Noah Goodman

    Main PI

    School of Humanities and Sciences

    Psychology

    Jiajun Wu

    Co-PI

    School of Engineering

    Computer Science

  • Abstract coming soon.

    Name

    Role

    School

    Department

    Jennifer Eberhardt

    Main PI

    School of Humanities and Sciences

    Psychology

    Dan Jurafsky

    Co-PI

    School of Humanities and Sciences

    Linguistics

  • The human eye exhibits remarkable capabilities, but in many situations it would be desirable to have vision and imaging capabilities that greatly surpass those of our eye. In our research, we aim to address this important challenge by creating new optical technologies that are inspired by human intelligence and can augment our own vision capabilities. We will aim to realize such optical elements in a compact and flat form factor, so that they can easily be integrated into regular eyewear. With this type of optics, humans will be able to experience and learn more about the world around us. In our opinion, the current state-of-the-art in creating enhanced vision and imaging capabilities has barely scratched the surface of what could be possible.

    Name

    Role

    School

    Department

    Mark Brongersma

    Main PI

    School of Engineering

    Materials Science and Engineering

  • The transmission of an infectious disease is fundamentally a social event. In general, for an infection to be transmitted, a susceptible individual needs to be brought into contact with an infectious individual. The likelihood that these two agents will interact in a way that will cause disease transmission depends on the behavior of the agents, the norms and institutions that shape their behavior, and the social structures in which they are embedded. Despite the central importance of social structure and human behavior, remarkably little research has focused on how such behavior should be represented in mathematical and computational models of infectious-disease transmission dynamics. This is a real problem since these models are fundamental tools for developing effective interventions for highly-consequential societal phenomena. Our project will adopt the tools of "Scientific Machine Learning" (SciML) to address this important problem. We will use machine learning to train neural networks on a wide range of possible functional forms for the interaction terms. These neural networks can then serve as "universal approximators"---basically, extremely general, data-informed functions---that we can include in standard transmission-dynamics models. AI tools have heretofore been of limited utility at the outset of epidemics---when we need forecasting the most---because of the sparseness of data as outbreaks get started. SciML, by training AI on mathematical models, provides a novel opportunity to augment our current capacities and harness the power of AI for social good. SciML allows us to train from the small data characteristic of the outset of outbreak, extrapolate outside of the original data sets, as is essential as outbreaks become epidemics and/or pandemics, and interpret our results back into general biological and social mechanisms, as is necessary for developing more generalizable insights.

    Name

    Role

    School

    Department

    James Holland Jones

    Main PI

    School of Sustainability

    Earth System Science

    Michele Gelfand

    Co-PI

    Graduate School of Business

    Graduate School of Business

    Margaret Levi

    Co-PI

    School of Humanities and Sciences

    Political Science

    Erin Mordecai

    Co-PI

    School of Humanities and Sciences

    Biology

  • Machine Learning has had substantial impact on diverse sectors of the industry, science and society. However, despite ML growth pervasive growth, the participation of historically under-represented groups in the ML workforce and research is extremely low. Studies have suggested that the problems of racial bias in ML systems arise due to the lack of diversity in the environment that they are shaped in including the non-representative nature of the people who develop these systems. These issues are amplified in the realm of Scientific Machine Learning (SciML) which is generally described as Machine Learning applied to data generated by physical experiments and simulations. Physical sciences students from under-represented groups are affected by limited access to SciML teaching and training. We will initiate a joint SLAC-ICME program, funded by the HAI seed grant, that makes Scientific Machine Learning accessible to students from historically under-represented groups. In addition to SciML teaching through workshops, the program will offer follow-up training and internships to enable students to apply their knowledge under the guidance of leading experts. Finally, it will provide mentorship and community building, so that students can have fruitful careers and become change-agents.

    Name

    Role

    School

    Department

    Gianluca Iaccarino

    Main PI

    School of Engineering

    Mechanical Engineering

  • The operating room is a highly demanding environment in which surgeons must process vast amounts of information to make timely and informed decisions that can greatly impact surgical outcomes. However, prolonged working hours coupled with the well-established effects of fatigue on cognitive and technical performance can lead to a significant potential for error, with detrimental consequences to patient health. To mitigate these human failings, we focus on developing SuTr, a foundational vision model for surgery with the ability to provide real-time intraoperative guidance and analysis. By leveraging the latest advancements in artificial intelligence and machine learning, SuTr aims to gain a holistic understanding of the operative scene to provide actionable insights into enhancing surgical precision and techniques, with the goal of decreasing error rates and complications, and paving the way towards autonomous robotic surgery.

    Name

    Role

    School

    Department

    William Hiesinger

    Main PI

    School of Medicine

    Cardiothoracic Surgery

  • The Technology & Racial Equity Field Incubator will convene Stanford faculty to map the problem space at the intersection of technology and racial equity and identify key interventions to build this nascent field of research. We understand race and technology as vectors that cut across academic disciplines. Given the ubiquity of technology and the fact that racial inequality runs through the fabric of our society, these issues will inevitably intersect with every field of study. The Technology & Racial Equity Field Incubator is designed to bring new voices into the conversation and uncover research questions that are not currently being addressed. By convening faculty from the Schools of Engineering, Humanities & Sciences, Medicine, Business, Education and Law, this program will catalyze interdisciplinary partnerships and provide training, resources, and thought-partnership to empower faculty so they can become leaders in the emerging field of race and technology.

    Name

    Role

    School

    Department

    Alfredo Artiles

    Main PI

    School of Humanities and Sciences

    Center for Comparative Studies in Race and Ethnicity

    Michele Elam

    Co-PI

    School of Humanities and Sciences

    English

    David Kim

    Co-PI

    School of Humanities and Sciences

    Center for Comparative Studies in Race and Ethnicity

    Irene Lo

    Co-PI

    Management Science and Engineering

    School of Engineering

    Londa Schiebinger

    Co-PI

    School of Humanities and Sciences

    History

  • Sharing neuroimaging data is a scientific imperative to enhance transparency and reproducibility in neuroimaging research and accelerate discoveries on how the brain functions in both the healthy and the disease states. Yet, at the same time, data sharing requires rigorous privacy measures to safeguard subjects’ identities and other sensitive information. Existing methods, such as partially removing or blurring facial features in structural MR images, along with deleting direct individual identifiers from metadata, have been considered to provide sufficient privacy protection for neuroimaging data. However, recent studies have shown that it is possible to reidentify subjects by matching facial images reconstructed from structural images with photos of each individual using advanced face recognition. Our project aims to develop a technical measure to counteract this novel threat to privacy and examine the impact of the increased risk to data privacy on neuroimaging research and open science practice. We will first design a new defacing technique that can fool the face recognition algorithm by adding small adversarial perturbations on the face surface of structural MR images. We will then investigate the ethical and regulatory implications of the increased reidentification risk in neuroimaging data sharing and examine policy strategies to better protect neuroimaging data. This interdisciplinary project will inform neuroscientists, ethicists, policymakers, and the public, as potential subjects, about the privacy risk associated with neuroimaging data posed by advanced AI algorithms. Further, this project will lay the groundwork for developing technical and regulatory safeguards to address the risk.

    Name

    Role

    School

    Department

    Russell Poldrack

    Main PI

    School of Humanities and Sciences

    Psychology

    Oluwasanmi Koyejo

    Co-PI

    School of Engineering

    Computer Science

  • Contracts are a ubiquitous legal artifact. When we purchase health insurance, move homes, accept a Terms of Service agreement, or start a new job, we sign contracts along the way. Contracts are encased in natural language. Accordingly, contractual terms are inherently vague and context-dependent. This is both a bug and a feature. Disputes may arise between contracting parties due to divergent interpretations of vague terms such as reasonable, good faith, or pollution. Regardless of how such disputes are resolved, and even if a dispute never makes its way to court, both sides may incur costs in the form of time, energy, and money. In practice, most people lack the legal resources necessary to effectively draft, interpret, and analyze natural language contracts. Computational law offers the promise of democratizing access to such resources through the development of widely-available assistive analytic tools. Beyond such tools, a further class of solutions known as “computable contracts” holds the promise of reducing the incidence of contractual disputes by encoding contractual terms in unambiguous formal computer code rather than natural language. However, both assistive computational tools and computable contract infrastructures are, at present, hindered by a limited understanding of the prevalence and role of linguistic vagueness in contract drafting and interpretation. Researchers in this space have only scratched the surface when it comes to reformulating vague natural language in computable code.

    Our project will support the development of a) the next generation of computational tools that can assist in the drafting, interpretation, and analysis of contracts; and b) computable contract infrastructure capable of addressing linguistic ambiguity beyond the structural level. We envision open-source tools that simulate how humans use and interpret vague natural language, including via the strategic use of vagueness when social conditions permit. Our project also aims to empower individuals – particularly those without access to conventional legal resources – to make informed legal decisions with the assistance of responsible and carefully produced computational aides. This project will not only be an interdisciplinary venture, but also one that is possible through industry collaborations with AXA, DLA Piper, Lex Machina, and Casetext.

    Name

    Role

    School

    Department

    Judith Degen

    Main PI

    School of Humanities and Sciences

    Linguistics

    Cleo Condoravdi

    Co-PI

    School of Humanities and Sciences

    Linguistics

    Michael Genesereth

    Co-PI

    School of Engineering

    Computer Science

    Thomas Icard

    Co-PI

    School of Humanities and Sciences

    Philosophy

    Julian Nyarko

    Co-PI

    Law School

    Law School

One additional project to be announced soon.