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Understanding the genetic and molecular basis of disease can have a profound influence on the development of targeted, personalized therapies and disease risk management. However, the process of determining causal variants of diseases remains challenging and largely manual, requiring researchers to compile predictions from computational models, and synthesize annotations scattered across various databases. We will develop and optimize large language model (LLM)-based chatbots with programmatic access to diverse biological databases and tools that will enable researchers to obtain relevant information, and synthesize data to answer complex queries. Researchers will be able to interface with the chatbots through a web platform with data visualization capabilities and features specifically designed to facilitate AI-assisted variant prioritization. The integration of advances in generative AI and natural language processing with bioinformatics will empower researchers to efficiently analyze the functional effects of genetic variants, significantly advancing our ability to discover mechanisms of various genetic diseases.
Name | Role | School | Department |
Main PI | School of Medicine | Genetics |
Integration of AI in clinical decision-support tools promises to augment clinicians’ knowledge work. Despite significant AI advances, adoption of these tools in the real-world remains limited due to their poor alignment with the needs of clinicians and that of the decisional contexts. For AI to be successfully adopted in clinical practice, it is crucial to not only build accurate models but to also deliver AI support in a way that considers factors that matter for decision-making, such as context of decisions, the type of computational output, and individual differences in reasoning and judgment of decision-makers. Designing interfaces to deliver machine-learned outputs tailored to these different dimensions remains a complex and unsystematic endeavor. To address this gap, the project brings together experts in clinical decision-making, human-computer interaction, and psychology to systematically design and evaluate different modes of delivering AI support. Using a mixed-methods approach, it first involves clinicians in a co-design process to create salient design variations of decision-support mechanisms for two clinical tasks that include lab order and antibiotic selection. It then conducts an experimental evaluation of combinations of task and decision-support mechanisms to examine how different task-interface combinations impact clinicians' performance, confidence, and reasoning. Findings from the study will show what types of clinical decision-making tasks benefit from which types of AI support; and how different modes of delivering AI support influence performance and confidence. By systematically addressing limitations in existing AI integration in clinical practice, the project aims to bridge the gap between technological capability and practical usability, advancing how AI-assisted clinical decision-making is conceptualized and supported.
Name | Role | School | Department |
Main PI | School of Medicine | Biomedical Informatics Research | |
Co-PI | School of Medicine | Biomedical Informatics Research | |
Co-PI | School of Humanities and Sciences | Psychology |
This project works on developing tools that apply large language models (LLMs) to help experts analyze discrimination in the courtroom. We focus on prior research on two kinds of bias: framing of Black Americans with dehumanizing metaphors (for example body-centric language, or framing black people as inherently criminal), or language about the physical spaces associated with Black Americans. We will develop an LLM-based algorithm for identifying how courtroom actors frame various participants, drawing on our prior work using LLMs for detecting implicit meanings like dehumanization. And we will study the downstream effect of this biased language with a human subjects experiment that measures how mentions of racialized spaces colors the decisions of jurors. Our project aims to explore how LLMs can help with the critical, challenging task of understanding implicit language in the courtroom and ultimately improving access to justice for people of all demographics.
Name | Role | School | Department |
Main PI | School of Humanities and Sciences | Linguistics |
The Ocean covers 71% of the planet and makes up 99% of the habitable space on the planet; however, we know very little about Earth’s marine biodiversity and global genetic databases have significant gaps. Environmental DNA (eDNA - DNA shed by organisms into the environment) is increasingly being utilized to improve marine biodiversity monitoring. While eDNA is a promising way to monitor biodiversity trends, current methods are constrained by existing databases and human decision making in data processing. Leveraging AI, our project aims to develop a new approach that enables context-aware phylogenetic inference using multiple datasets, combined with a novel statistical algorithm for reference-free species identification.
Name | Role | School | Department |
Main PI | School of Sustainability | Oceans | |
Co-PI | School of Medicine | Biomedical Data Science |
Weather-related risks continue to stress insurance markets, one of the key tools available for both protecting households from the ravages of climate change and incentivizing adaptation strategies. Florida’s growing hurricane risks have demonstrated the necessity of functioning insurance markets. Los Angeles’s recent fires have reinforced the centrality of insurance markets for managing and adapting to a changing physical and political environment. As risks shift, understanding implications of pricing strategies, regulatory approaches, and market functions add substantial complexity to insurance operations and oversight. This project proposes to develop AI tools to bring speed, efficiency, and eventually insight to these processes.
In Florida, two key government actors engage with insurance markets: the Office of Insurance Regulation (OIR) and the Insurance Consumer Advocate (ICA.) Insurance companies frequently apply for regulatory approval for insurance rate changes in response to perceived risks, whether from hurricane frequency and severity, aggregate risks that impact reinsurance markets (i.e. insurance for insurance companies, a market function essential to Florida), competitive dynamics, and the litigation environment within Florida. OIR attempts to balance market stability, company solvency and consumer protections, while ICA focuses primarily on consumers. OIR and ICA work requires substantial manual processing and analysis. Currently, both OIR and ICA can face backlogs in their approval and oversight processes.
This project seeks to automate the initial data extraction and coding from insurance rate filings in support of ICA and OIR and to facilitate their analysis. The analysis of insurance filings is representative of challenging tasks where highly specialized expertise is required to interpret and process many similar documents of high complexity. Our research is to create a general processing system for document sets that captures experts’ key considerations, processes the document sets, helps them compare and contrast between documents. Specifically, we will study how our system can help OIR and ICA accelerate the rate approval process while maintaining accuracy. If successful, this project will aid consumer protections and improve efficiency of insurance market regulatory oversight.
Name | Role | School | Department |
Main PI | School of Engineering | Computer Science | |
Senior Research Scholar | School of Sustainability | Precourt Institute for Energy |
This project aims to improve urban drought management by developing AI-driven tools that enhance drought indicators and response strategies. As climate change increases the frequency and severity of droughts, cities face growing challenges in ensuring reliable water access while managing limited resources. Current drought indicators are infrequently tested and updated, limiting their effectiveness in guiding drought responses that mitigate socio-economic impacts. By integrating water resource systems modeling with reinforcement learning, this research will optimize drought indicators, adapt them to changing water supply and demand patterns, and create more effective drought response plans. The approach focuses on providing actionable indicators for urban water planners and providing insights for California drought policy.
Name | Role | School | Department |
Main PI | School of Engineering/School of Sustainability | Civil and Environmental Engineering | |
Co-PI | School of Engineering | Aeronautics and Astronautics |
Rapid advancements in artificial intelligence (AI) raise important concerns about cybersecurity risks. While existing work shows AI falls short of human expertise in cybersecurity, we aim to identify specific characteristics that could serve as indicators of emerging capabilities and risks by studying the gap between AI and peak human performance. We propose comparing world-class hackers, selected based on their track record in security research and top competitions, against AI systems in controlled environments. By analyzing how both humans and AI approach security challenges, we can map out the specific expertise, intuitive knowledge, and problem-solving approaches that make human experts effective but remain difficult for AI to replicate. Conversely, we seek to identify areas where AI's latent capabilities may offer unique advantages to better understand how experts may best utilize AI in their work. This detailed analysis will help develop better ways to evaluate AI systems in cybersecurity contexts, addressing a critical gap in evidence-based policymaking and laying the groundwork for future benchmarks and evaluation work.
Name | Role | School | Department |
Main PI | School of Law | Law School | |
Co-Pl | School of Engineering | Electrical Engineering | |
Co-Pl | School of Engineering | Computer Science |
Realistic practice and personalized feedback are key for psychotherapy trainees to effectively learn clinical helping skills. Simulation-based practice methods, such as roleplay with peers or Standardized Patients can offer interactive practice opportunities that complement theoretical knowledge. However, each practice method has its limitations. Moreover, while instructor-provided feedback on psychotherapy skills can significantly enhance therapists’ competence and quality , it is often time-consuming and costly for psychotherapy classroom instructors and supervisors to provide such feedback on every practice. Innovative training methods that are more cost-effective and scalable are therefore needed. Our project will develop CARE, an interactive training system leveraging Large Language Models (LLMs) to provide scalable practice and feedback in psychotherapy classrooms. In this proposed project, we work to accomplish this vision via two complementary research thrusts: (1) building the technical foundations and (2) deploying CARE in psychotherapy learning communities.
Name | Role | School | Department |
Main PI | School of Medicine | Psychiatry and Behavioral Sciences | |
Co-PI | School of Engineering | Computer Science |
Intercultural competence is a set of related skills that facilitate successful interaction with people of other cultures. Those partaking in intercultural exchange need to be aware of their partners’ behavioral and linguistic conventions, as well as the deeper interpretations orienting their partners’ behavioral choices. If communication augmented by Generative AI understands such cultural differences, it will be better equipped to make impactful use of visual and linguistic media. Although current large language models (LLMs) already contain implicit socio-cultural knowledge, these inferences may be entangled or superimposed. We propose developing a cross-cultural knowledge tool which (1) implements a dynamic methodology for collecting cultural knowledge, (2) will be useful for mapping cultural variance within and across cultures, and (3) overlayed with current LLMs, will facilitate constructive intercultural exchange.
Name | Role | School | Department |
Main PI | School of Engineering | Computer Science | |
Co-PI | Graduate School of Business | Graduate School of Business |
Proteins are central to many biological processes, yet analyzing and designing them often requires advanced expertise in structural biology and computational methods. We propose developing a multimodal AI framework that bridges protein structure and text modalities, enabling more intuitive interaction with protein structures data and protein design tools. Building upon existing generative protein structure and large language models, we aim to establish new evaluation metrics to systematically assess generated protein structures and automate the generation of human-readable descriptions from protein structures. We will conduct comprehensive evaluation of the quality and diversity of current protein structure model outputs, create a comprehensive dataset of protein structures paired with detailed textual explanations, and ultimately train an integrated protein structure-text generative model. If successful, this framework could offer interpretation to complex protein features, lower the barriers to computational structural biology and protein engineering. This effort will provide researchers, educators, and enthusiasts with accessible tools for exploring protein structures.
Name | Role | School | Department |
Main PI | School of Engineering | Bioengineering |
This project aims to create the first comprehensive textbook on how AI systems can learn from and align with human preferences and values. While there are many books on AI algorithms and human decision-making, there is currently no single resource that brings these fields together to teach how to build AI systems that effectively learn from human feedback and preferences. The proposed textbook will bridge this gap by combining insights from computer science, psychology, economics, and ethics. It will cover both technical approaches like reinforcement learning from human feedback (RLHF) and broader considerations around ethics, fairness, and responsible AI development. The book will include practical tutorials, real-world examples, and interdisciplinary exercises to help students and practitioners build AI systems that better serve human needs and values. A companion website will provide additional resources and maintain an engaged learning community. This project represents an important step toward ensuring AI systems are developed in ways that benefit society while respecting human values and preferences.
Name | Role | School | Department |
Main PI | School of Engineering | Computer Science | |
Co-Pl | Graduate School of Business | Graduate School of Business | |
Co-Pl | School of Humanities and Sciences | Psychology | |
Co-Pl | Graduate School of Business | Graduate School of Business | |
Co-Pl | School of Engineering | Computer Science | |
Co-Pl | School of Engineering | Computer Science |
Globally, over 310 million major surgeries occur annually, with outcomes heavily reliant on surgeon experience, leading to variability, inefficiencies, complications, and increased healthcare costs. To address this, we propose the concept of Surgeon-Machine Interfaces (SMI), integration of intelligent machine models in real-time to provide tailored experiential insights and augment surgeons' capabilities. Using microvascular decompression (MVD) surgery as a proof-of-concept, the platform quantifies key surgical and anatomical interactions, generates outcome-driving predictive metrics, and enables real-time deployment in the operating room. This project aims to enhance surgical decision-making to reduce variability in MVD surgeries and improve patient outcomes while advancing the quality of surgical care.
Name | Role | School | Department |
Main PI | School of Medicine | Neurosurgery | |
Co-PI | School of Medicine | Psychiatry and Behavioral Sciences |
Language models are aligned to the collective voice of many, resulting in outputs that align with no one in particular. This results in, for example, LLM-generated writing that feels correct but uninteresting, or that sounds plausible but doesn’t align with the user’s views, or otherwise doesn’t echo the user’s voice or intent. How might we develop algorithms and interactions that leverage the power of modern AI models in a way that maintains and even amplifies what is uniquely "you"? Our research is developing techniques to train personalized models from a small number of demonstrated examples, allowing you to construct a large number of bounded abstractions—LLM “brushes” that capture specific voices or elements that you might want to deploy. We are developing a set of interaction techniques for nonexpert end users to author, command, and combine these brushes as they use large language models.
Name | Role | School | Department |
Main PI | School of Engineering | Computer Science | |
Co-Pl | School of Engineering | Computer Science |
Radiology reports are detailed documents generated during routine clinical care when radiologists interpret complex medical images. The primary purpose of these reports is to facilitate effective communication between radiologists and referring physicians. Recently, patients have also had direct access to these reports, often receiving them even before their physicians can provide context and explanations. The specialized terminology and complexity inherent in radiology reports can make them difficult to understand, thereby increasing anxiety and uncertainty, particularly among patients with diverse educational backgrounds.
Improved comprehension of clinical reports has been associated with better patient engagement, reduced anxiety, and enhanced treatment outcomes. Recognizing the challenges patients face in understanding radiology reports, we aim to leverage large language models (LLMs) to develop an automated framework for generating and evaluating patient-friendly clinical reports. Our work focuses on three specific objectives: (1) devising innovative methods to generate accessible, patient-friendly clinical reports; (2) establishing robust evaluation and monitoring strategies to ensure the safety, quality, and effectiveness of LLM-generated summaries; and (3) clinically validating these automatically generated reports through engagement with all stakeholders, including patients.
Name | Role | School | Department |
Main PI | School of Medicine | Radiology | |
Co-PI | School of Medicine | Biomedical Data Science | |
Co-PI | School of Medicine | Medicine | |
Co-PI | School of Medicine | Radiology |
Our long-term goal is to equip robots with flexible and creative problem-solving capabilities in the physical world, such as rolling out dough with a wine bottle if no rolling pin is available. This will require robots to physically reason about the environment, preferably with as little exploration as possible. While robots are increasingly capable of manipulating a variety of objects, humans excel in their capacity to generalize their manipulation skills to novel objects, scenarios, or goals through innovative usage of objects and out-of-the-box thinking. On top of that, they often do so in only a handful of attempts. Currently, robots lack this creativity, flexibility, and fast adaptation which ultimately will be required when deploying them into environments they have never seen before (e.g. disaster zones or other planets). In this project, we will develop and test 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 (whether human or robot) 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 |
Main PI | School of Engineering | Computer Science | |
Co-PI | School of Engineering | Computer Science | |
Co-PI | School of Humanities and Sciences | Psychology |
High-quality teacher feedback is crucial for students’ literacy development and agency as critical independent writers. While recent advances in AI have led to various automated feedback tools, current large language models (LLMs) struggle to provide feedback that meets pedagogical standards across content, structural, and social-affective dimensions. This research proposes to 1) measure the quality of teacher-written and AI-generated feedback along research-based dimensions, 2) provide insights into how expert teachers balance complex pedagogical responsibilities, and 3) enhance automated feedback generation. We will develop a suite of automated benchmarks for feedback quality dimensions, leveraging expert annotations on a large dataset of feedback comments to train language models. We will then collect a RLHF dataset of expert teacher preferences to train both a standard black-box reward model and an interpretable reward model relating preferences to our benchmarks. Finally we will apply a novel reinforcement learning approach to optimize open-source LLMs to generate feedback that aligns with learned reward functions and captures the complex objectives of the task. The resulting systems have the potential to exceed human feedback capabilities and allow individual teachers to customize feedback priorities without additional training.
Name | Role | School | Department |
Main PI | Graduate School of Education | Graduate School of Education | |
Co-PI | Graduate School of Education | Graduate School of Education |
The development of a motion-capture glove using Fiber Bragg Grating (FBG) sensors offers a novel approach to capturing hand motion with exceptional precision and minimal interference. Designed to track intricate movements and tactile interactions, this project has the potential to advance research in robotics, surgery, and the arts.
An initial application focuses on piano performance, addressing ergonomic challenges faced by individuals with smaller hand spans, often women, who are at greater risk of injury due to the standardized design of the piano keyboard. By collecting high-resolution, multimodal data, this research aims to inform safer and more inclusive practices in music education and performance.
This work will generate a comprehensive dataset, integrating motion, tactile, and audio inputs, with applications extending to robotics, computer vision, and ergonomic musical instrument design. This initiative integrates advanced sensing technology with human-centered research to foster equity and inclusivity across multiple fields.
Name | Role | School | Department |
Main PI | School of Humanities and Sciences | Music | |
Co-PI | School of Engineering | Mechanical Engineering |
Social isolation and loneliness are growing challenges in American society, propelled by broad changes in our social fabric, underlying physical and mental health conditions, and compounded by population aging. The consequences for health and wellbeing are profound and on par with smoking a pack a day, and yet identifying persons facing social isolation and loneliness is challenging, and solutions are emerging. To support better intervention and research, we will conduct a phone survey to definitively identify persons with these conditions, and undertake a process to link those results to extensive health records and create an “electronic phenotype”. The phenotype will provide the means for systematically identifying a population at risk, and we will also use implementation science frameworks to query stakeholders at different levels of the health system regarding how to apply the phenotype in practice to conduct screening and ultimately improve support and services provided to affected persons.
Name | Role | School | Department |
Main PI | School of Medicine | Med/Primary Care and Population Health | |
Co-PI | Emory University/School of Medicine | Biomedical Informatics | |
Co-PI | School of Medicine | Med/Primary Care and Population Health |
Robotic dexterous manipulation has recently made significant progress through imitation learning from human demonstrations. Tools like the Universal Manipulation Interface (UMI) have greatly expanded the scale at which we can collect robot behavior data, but most rely solely on vision and miss key sensory signals such as touch and audio. In this project, we enhance UMI with CoinFT—a custom, coin-sized six-axis force-torque sensor at each fingertip—and add audio recording, creating UMIFT. This setup enables us to collect large, multimodal datasets encompassing vision, touch, and sound, allowing robots to learn manipulation skills that are more adaptable, precise, and safe. By leveraging these richer sensory cues, robots will be capable of reliably handling soft and fragile objects as well, representing a significant step forward in human-like dexterous manipulation.
Name | Role | School | Department |
Main PI | School of Engineering | Mechanical Engineering | |
Co-Pl | School of Engineering | Electrical Engineering |
Dengue is the most significant arbovirus globally, causing an estimated 210 million infections per year, leading to high rates of morbidity and considerable economic burdens. Dengue virus is transmitted from person to person via mosquito vectors that thrive in different environments. There are currently no preventative pharmaceutical interventions, such as vaccines, for Dengue control, so mosquito control is the primary disease management tool. Historically, large outbreaks of the disease predominantly occurred in urban and peri-urban areas, attributed to the primary vector, Aedes aegypti. Over the last decade, however, outbreaks have emerged in rural areas where a highly invasive secondary vector, Aedes albopictus, is also present. However, the precise habitat in rural areas, and thus targets for rural Dengue management, remain unknown. This project will pair mosquito and plant species ground surveys, drone imagery, and Meta’s Segment Anything Model to identify Ae. albopictus habitat and efficiently create fine (0.02m) resolution habitat maps. Drone imagery will then be combined with high resolution satellite imagery (0.5m) to generate dynamic regional risk maps that can be used for disease management. .
Name | Role | School | Department |
Main PI | School of Humanities and Sciences | Biology |
This proposal seeks to enable language models to learn specialized knowledge from extremely small datasets—on the order of a single textbook—by creating a new approach to data-efficient model adaptation. We will build an experimental platform that studies small-scale learning, employ synthetic data generation techniques to break current scaling-law constraints, and systematically analyze the scaling properties of adapted models. Our work will evaluate the resulting models on specialized domains (e.g., code and mathematics) to measure improvements in accuracy and the effects of potential issues such as hallucination and bias. In the long term, we aim to enable models to learn from human-like data efficiencies and broaden their impact beyond large-scale pretraining settings.
Name | Role | School | Department |
Main PI | School of Engineering | Computer Science |
Unlocking the mysteries of disease onset and progression necessitates cutting-edge algorithms that bridge the molecular and morphological intricacies of individual cells. Our project uses AI to align and analyze two powerful imaging modalities: Xenium, a technology that maps molecular information at subcellular resolution, and H&E staining, the gold-standard for visualizing tissue architecture. By combining these me3thodes, we aim to reveal finite relationships between molecular activity and tissue structure, offering unprecedented insights into cellular behavior in healthy and precancerous tissues.
This interdisciplinary approach leverages advanced AI models, such as Swin Transformers and VoxelMorph, to precisely align images and segment individual cells. By enhancing the accuracy of these techniques, we can better understand how cellular diversity and organization contribute to disease, paving the way for earlier diagnostics and personalized treatments. Our research integrates expertise in genetics, bioinformatics, pathology, and AI, creating a framework that will benefit both researchers and clinicians.
With support from expert pathologists, we will validate this approach to ensure it meets the highest standards of accuracy and usability. Ultimately, this work could transform diagnostic protocols, making them more precise and accessible while advancing our understanding of complex diseases.
Name | Role | School | Department |
Main PI | School of Medicine | Genetics |
A hallmark of human intelligence is the exquisite balance of engaging goal-driven control over complex aspects of tasks while relegating undemanding aspects to more automatic processes. Visual search is a canonical task in which human performance can be guided both by goal-driven and automatic systems. Classical studies of visual search behavior in humans has provided fundamental insights into the trade-off between goal-driven and automatic behavior. However, a major challenge has been to make image computable models of visual search behavior for natural images that capitalize on these insights. Moreover, testing to see whether fundamental insights learned from behavior tested with simple, highly controlled visual search arrays generalize to search in natural images is a nontrivial challenge. Here we propose to use deep learning models with aligned linguistic and visual representations to build models of human visual search in natural images which can account for both automatic and goal-driven behavior. We will test these models using visual search stimuli generated with deep image synthesis designed specifically to test the tradeoff between automatic and goal-driven behavior. Ultimately we expect that our data and models of visual search will form the basis for artificial intelligence systems capable of monitoring the balance of goal-driven and automatic behavior and optimize human performance through image synthesis of stimuli which promote optimal balance of these two forces.
Name | Role | School | Department |
Main PI | School of Humanities and Sciences | Psychology | |
Co-Pl | School of Engineering | Computer Science |
The construction industry is one of the most resource-intensive global sectors, often facing inefficiencies and waste. Despite advancements in technology, managers still rely on subjective visual assessments to monitor progress, leading to delays, cost overruns, and safety concerns. This project aims to develop a digital twin—a 4D queryable representation of construction sites that is generated by visual data (color and depth images) collected over time. Using advanced computer vision and machine learning models, the system will enable users of any expertise or skill to interact with the digital twin by querying it in natural language to obtain insights about progress, identify issues, and anticipate future outcomes. By integrating visual and linguistic models, the system will provide interpretable visual outputs in response to user queries, hence increasing transparency and explainability. This approach seeks to improve efficiency, enhance safety, and support sustainability in construction project management.
Name | Role | School | Department |
Main PI | School of Engineering/School of Sustainability | Civil and Environmental Engineering | |
Co-Pl | School of Engineering | Electrical Engineering |
This project aims to empower young people from disadvantaged backgrounds to use generative AI tools effectively, productively, and responsibly. While generative AI holds immense potential for enhancing productivity and solving real-world challenges, its improper use raises concerns about reliability and ethical considerations. To address these challenges, we will assess the current capacity of youth to engage with these tools, develop an educational curriculum emphasizing critical skills, sustainability, and ethical use, and rigorously evaluate its effectiveness through a randomized controlled trial involving 3,000 students across 150 classes. Partnering with a non-profit operating in rural India, we aim to build the capacity of underserved youth to harness generative AI in ways that benefit their personal and community development. The findings will provide valuable insights into fostering equitable access to AI education and its responsible use in developing country contexts.
Name | Role | School | Department |
Main PI | Graduate School of Education | Graduate School of Education |
We use critical theories of disability to examine the production and use of AI detection tools. First, we analyze how Human Computer Interaction and Natural Language Processing literature considers disability, such as through annotator recruitment, analyses of bias, and comparisons between people and models. Second, we conduct an algorithmic audit of several AI-detection tools to understand their treatment of disabled people. We focus specifically on the higher education context, and hope that our work will inform improved research directions and offer constructive considerations for the protection of disability rights with respect to AI.
Name | Role | School | Department |
Main PI | Graduate School of Education | Graduate School of Education |
Dynamic brain data, teeming with biological and functional insights, are becoming increasingly accessible through advanced measurements, providing a gateway to understanding the inner workings of the brain in living subjects. However, the vast size and intricate complexity of the data also pose a daunting challenge in reliably extracting meaningful information across various data sources. In this project we will introduce a generalizable self-supervised deep manifold learning for exploration of dynamic patterns. Unlike existing methods that extracting patterns directly from the input data as in the existing methods, the proposed brain cognitive network embedding (BCNE) seeks to capture the brain-state map by deciphering the temporospatial correlations within the data and subsequently applying manifold learning to this correlative representation. The performance of BCNE will be showcased through the analysis of several important dynamic brain datasets. We anticipate that BCNE will provide an effective tool for exploring general neuroscience inquiries.
Name | Role | School | Department |
Main PI | School of Medicine | Radiation Oncology - Radiation Physics |
Hearing loss significantly impacts individuals of all ages, particularly children, where early detection and monitoring are critical for optimal speech and language development. In the United States, 3 in every 1,000 children are born with hearing loss, and 1 in 7 children aged 6 to 19 experience some form of hearing loss. Current hearing assessment methods, such as the Ling Sound Test, are challenging for parents to administer consistently, while access to specialists is often limited. We propose the development of the first AI-powered digital Ling Sound Test in a web-based platform for daily, and long-term hearing monitoring at home. By addressing challenges such as uncontrolled home environments, background noise, and device variability, the research aims to deliver accurate, efficient, and robust hearing assessments. In addition to advanced statistical models, we will integrate a transformer-based model to refine hearing predictions and leverage a large language model (LLM) to provide personalized feedback. The gamified interface will be developed with the intention to further sustain participation for childrens across different age groups. By enabling accessible, accurate, and engaging at-home hearing assessments, Shuno aims to empower families and foster better outcomes for children with hearing challenges.
Name | Role | School | Department |
Main PI | School of Engineering | Computer Science |
AI will increasingly be used to mediate interpersonal communication, especially in work settings. As people interact on digital platforms, LLM-based technology will assist them in crafting more effective messages, and saving time and energy in the process. Such technologies already exist on the market; their use will likely proliferate dramatically in the near future. This study seeks to explore the implications of such technologies on culture and interactive productivity. Using an LLM-mediated interaction platform developed with the DSPy library, we will run a set of experiments in which pairs of individuals work together to perform a task. Participants will either be interacting with the assistance of an LLM-based intermediary, or without one. We will test how LLM-intermediation affects participants’ perceptions of the quality of interaction, their sense of identification and connection with their partners, and their psychological well-being.
Name | Role | School | Department |
Main PI | School of Humanities and Sciences | Linguistics | |
Co-PI | Graduate School of Business | Graduate School of Business |
Robot-assisted surgery (RAS) has had considerable success with well over 2,200,000 robot-assisted surgeries carried out in 2023 with Intuitive Surgical’s da Vinci Surgical System, which has an installed base of over 9,500 systems worldwide. In RAS, small incisions are made in the body and long, slender surgical instruments are inserted inside the patient’s abdomen along with a 3D camera. The surgeon teleoperates these instruments and the camera with two robotic arms from a remote console showing the 3D camera view. The patient-side robot instruments are controlled to follow the surgeon’s hand motions, with motion scaling for improved accuracy and filtering to remove hand tremor. The patient-side part of the latest systems has four robot arms; one of them holds the 3D camera and the remaining three hold the surgical instruments.
A significant portion of many surgical procedures is devoted to three-handed tasks. The repetitive, less demanding part of these tasks is usually performed using one dedicated robot arm. The surgeon then can use the remaining two robot arms to perform the more demanding parts of the task. Since a surgeon can only control two arms at a time, he/she must switch the robot’s control system to use one arm to perform one part and then switch the control system back to manipulate the other two arms as needed. This switching increases the cognitive load of the surgeon and was shown to cause negative outcomes such as collisions between the robot arms and tearing of the human tissue. This provides a huge opportunity to develop autonomous systems that can carry out the tasks of the extra robotic arm(s).
The goal of this work is to develop a collaborative autonomous system automating an extra robotic arm to work alongside the teleoperated arms by the surgeon in a parallel and coordinated fashion. This will realize the full potential of having more than two robotic arms in RAS. This is unlike previous work in this area where the autonomous auxiliary arms move alone while the teleoperated surgical instruments are idle. Our work will lead to a collaborative system where an automated extra robotic arm efficiently assists the main surgeon as they teleoperate other robotic arms. We hypothesize that our collaborative autonomous systems will lead to faster execution of tasks than the state-of-the-art systems in this area. In addition, we hypothesize that this collaborative system will reduce the cognitive load of the surgeons compared with the gold standard, where the main surgeon controls all the robotic arms. The knowledge and data generated from the proposed research will provide insights towards designing effective interactions between surgeons and autonomous systems.
Name | Role | School | Department |
Main PI | School of Engineering | Mechanical Engineering | |
Co-Pl | School of Medicine | Surgery |
What knowledge is endowed to us by genetics versus what is learned through experience? Recent advances in AI have rendered this ancient puzzle tractable. It is now possible to train a model akin to those that have shown sparks of human intelligence (e.g., ChatGPT) on the experiences of infants and evaluate whether it learns like an infant. What knowledge, if any, needs to be built in for the model to learn like an infant? To start to answer this question, we will collect the most comprehensive multi-dimensional dataset from a single infant (2-24 months). We will deeply survey their visual and auditory experiences via head-mounted cameras, we will measure their neural activity through awake fMRI, and assess their language comprehension through behavioral tasks. Utilizing this data, we will train a multi-modal model on the infant's experiential data and constrain it to have the same neural representations as measured by the infant. The model’s language acquisition will be evaluated against the child’s developmental milestones. Success will be evaluated based on whether the model learns language at the same rate the child does. In doing so, we will make substantial progress in understanding visual and language development in infancy, which may inform what goes awry in developmental disorders (e.g., autism).
Name | Role | School | Department |
Main PI | School of Humanities and Sciences | Psychology | |
Co-Pl | School of Humanities and Sciences | Psychology | |
Co-Pl | School of Humanities and Sciences | Psychology |
When asked what they most desire in life, the majority of people cite happiness or well-being. Beyond its intrinsic value, high well-being is associated with improved mental and physical health, career success, and stronger social relationships. Despite its critical importance for individuals and its broader societal impact, global subjective well-being ratings have stagnated, even as technology has advanced. We propose leveraging LLM-based well-being chatbots to offer more engaging, personalized, and potentially more effective interventions. Our approach begins by enhancing existing well-being interventions, such as writing a gratitude letter or reflecting on a positive life moment, by incorporating tailored guidance and feedback. In the next phase, we aim to democratize the development of new interventions by providing easy-to-use chatbot templates for other researchers. This will lower technical barriers and encourage a broader range of innovative solutions from both the scientific community and the public, addressing the historical lack of diversity in well-being interventions. Finally, we will assess the effectiveness of these diverse interventions through a well-powered chatbot competition, identifying the most effective strategies for different populations. Through this project, we aim to explore how emerging AI technologies can supplement, not replace, traditional mental health resources, by offering a low-barrier entry point for researchers to develop and test their own interventions, ultimately identifying the most effective strategies for enhancing well-being.
Name | Role | School | Department |
Main PI | School of Humanities and Sciences | Sociology | |
Co-Pl | School of Humanities and Sciences | Psychology |