About

Deborah Estrin is a Professor of Computer Science at Cornell Tech in New York City, where she holds The Robert V. Tishman Founder's Chair and serves as the Associate Dean for Impact. She is also an Affiliate Faculty member at Weill Cornell Medicine. Estrin founded the Public Interest Technology Initiative (PiTech) at Cornell Tech, which integrates meaningful public impact into students' training and future careers. Her current research focuses on digitally-enabled innovations that support patients and healthcare providers in optimizing clinical outcomes and quality of life. She leads strategic initiatives to enhance external engagement and public interest impact across the Cornell Tech faculty and student body, spanning areas such as digital health, extended reality (XR), and artificial intelligence (AI). Prior to joining Cornell University, Estrin was the Founding Director of the NSF Center for Embedded Networked Sensing (CENS) at UCLA, where she pioneered the development of mobile and wireless systems for real-time data collection and analysis about the physical world. She co-founded the non-profit startup Open mHealth and has served on scientific advisory boards for early-stage mobile health startups, as well as an Amazon Scholar. Estrin's distinguished honors include the ACM Athena Lecture (2006), Anita Borg Institute's Women of Vision Award for Innovation (2007), the IEEE Internet Award (2017), the MacArthur Fellowship (2018), and the IEEE John von Neumann Medal (2022). She is an elected member of the American Academy of Arts and Sciences, the National Academy of Engineering, and the National Academy of Medicine, and has received honorary doctorates from EPFL, Uppsala, and Concordia.

Research topics

• Computer Science
• Computer Security
• Medicine
• Political Science
• World Wide Web
• Business
• Medical education
• Multimedia
• Nursing
• Human–computer interaction
• Public relations
• Operating system

Selected publications

• Novel Artificial Intelligence Applications in Heart Transplantation

  Canadian Journal of Cardiology · 2025-11-20

  article
  PublisherDOI

• Predicting Cardiopulmonary Exercise Testing Performance in Patients Undergoing Transthoracic Echocardiography - An AI Based, Multimodal Model

  medRxiv · 2025-07-06 · 1 citations

  preprintOpen access

  Background and Aims: Transthoracic echocardiography (TTE) is a widely available tool for diagnosing and managing heart failure but has limited predictive value for survival. Cardiopulmonary exercise test (CPET) performance strongly correlates with survival in heart failure patients but is less accessible. We sought to develop an artificial intelligence (AI) algorithm using TTE and electronic medical records to predict CPET peak oxygen consumption (peak VO2) ≤ 14 mL/kg/min. Methods: An AI model was trained to predict peak VO2 ≤ 14 mL/kg/min from TTE images, structured TTE reports, demographics, medications, labs, and vitals. The training set included patients with a TTE within 6 months of a CPET. Performance was retrospectively tested in a held-out group from the development cohort and an external validation cohort. Results: 1,127 CPET studies paired with concomitant TTE were identified. The best performance was achieved by using all components (TTE images, all structured clinical data). The model performed well at predicting a peak VO2 ≤14 mL/kg/min, with an AUROC of 0.84 (development cohort) and 0.80 (external validation cohort). It performed consistently well using higher (≤18 mL/kg/min) and lower (≤12 mL/kg/min) cut-offs. Conclusions: This multimodal AI model effectively categorized patients into low and high risk predicted peak VO2, demonstrating the potential to identify previously unrecognized patients in need of advanced heart failure therapies where CPET is not available.

  PublisherOA PDFDOI

• An Artificial Intelligence Model to Detect Abnormal Ejection Fraction from Non-Contrast Chest Computed Tomography: The CT-LVEF study

  Research Square · 2025-02-05

  preprintOpen access
  PublisherOA PDFDOI

• Opportunities and Challenges for Augmented Reality in Family Caregiving: Qualitative Video Elicitation Study

  JMIR Formative Research · 2024-05-30 · 4 citations

  articleOpen accessSenior author

  BACKGROUND: Although family caregivers play a critical role in care delivery, research has shown that they face significant physical, emotional, and informational challenges. One promising avenue to address some of caregivers' unmet needs is via the design of digital technologies that support caregivers' complex portfolio of responsibilities. Augmented reality (AR) applications, specifically, offer new affordances to aid caregivers as they perform care tasks in the home. OBJECTIVE: This study explored how AR might assist family caregivers with the delivery of home-based cancer care. The specific objectives were to shed light on challenges caregivers face where AR might help, investigate opportunities for AR to support caregivers, and understand the risks of AR exacerbating caregiver burdens. METHODS: We conducted a qualitative video elicitation study with clinicians and caregivers. We created 3 video elicitations that offer ways in which AR might support caregivers as they perform often high-stakes, unfamiliar, and anxiety-inducing tasks in postsurgical cancer care: wound care, drain care, and rehabilitative exercise. The elicitations show functional AR applications built using Unity Technologies software and Microsoft Hololens2. Using elicitations enabled us to avoid rediscovering known usability issues with current AR technologies, allowing us to focus on high-level, substantive feedback on potential future roles for AR in caregiving. Moreover, it enabled nonintrusive exploration of the inherently sensitive in-home cancer care context. RESULTS: We recruited 22 participants for our study: 15 clinicians (eg, oncologists and nurses) and 7 family caregivers. Our findings shed light on clinicians' and caregivers' perceptions of current information and communication challenges caregivers face as they perform important physical care tasks as part of cancer treatment plans. Most significant was the need to provide better and ongoing support for execution of caregiving tasks in situ, when and where the tasks need to be performed. Such support needs to be tailored to the specific needs of the patient, to the stress-impaired capacities of the caregiver, and to the time-constrained communication availability of clinicians. We uncover opportunities for AR technologies to potentially increase caregiver confidence and reduce anxiety by supporting the capture and review of images and videos and by improving communication with clinicians. However, our findings also suggest ways in which, if not deployed carefully, AR technologies might exacerbate caregivers' already significant burdens. CONCLUSIONS: These findings can inform both the design of future AR devices, software, and applications and the design of caregiver support interventions based on already available technology and processes. Our study suggests that AR technologies and the affordances they provide (eg, tailored support, enhanced monitoring and task accuracy, and improved communications) should be considered as a part of an integrated care journey involving multiple stakeholders, changing information needs, and different communication channels that blend in-person and internet-based synchronous and asynchronous care, illness, and recovery.

  PublisherOA PDFDOI

• The Illusion of Empathy? Notes on Displays of Emotion in Human-Computer Interaction

  2024-05-11 · 60 citations

  articleOpen access

  From ELIZA to Alexa, Conversational Agents (CAs) have been deliberately designed to elicit or project empathy. Although empathy can help technology better serve human needs, it can also be deceptive and potentially exploitative. In this work, we characterize empathy in interactions with CAs, highlighting the importance of distinguishing evocations of empathy between two humans from ones between a human and a CA. To this end, we systematically prompt CAs backed by large language models (LLMs) to display empathy while conversing with, or about, 65 distinct human identities, and also compare how different LLMs display or model empathy. We find that CAs make value judgments about certain identities, and can be encouraging of identities related to harmful ideologies (e.g., Nazism and xenophobia). Moreover, a computational approach to understanding empathy reveals that despite their ability to display empathy, CAs do poorly when interpreting and exploring a user’s experience, contrasting with their human counterparts.

  PublisherOA PDFDOI

• Learning Disease Progression Models That Capture Health Disparities

  arXiv (Cornell University) · 2024-12-20

  preprintOpen access

  Disease progression models are widely used to inform the diagnosis and treatment of many progressive diseases. However, a significant limitation of existing models is that they do not account for health disparities that can bias the observed data. To address this, we develop an interpretable Bayesian disease progression model that captures three key health disparities: certain patient populations may (1) start receiving care only when their disease is more severe, (2) experience faster disease progression even while receiving care, or (3) receive follow-up care less frequently conditional on disease severity. We show theoretically and empirically that failing to account for any of these disparities can result in biased estimates of severity (e.g., underestimating severity for disadvantaged groups). On a dataset of heart failure patients, we show that our model can identify groups that face each type of health disparity, and that accounting for these disparities while inferring disease severity meaningfully shifts which patients are considered high-risk.

  PublisherOA PDFDOI

• Opportunities and Challenges for Augmented Reality in Family Caregiving: Qualitative Video Elicitation Study (Preprint)

  2024-01-31

  preprintOpen accessSenior author

  <sec> <title>BACKGROUND</title> Although family caregivers play a critical role in care delivery, research has shown that they face significant physical, emotional, and informational challenges. One promising avenue to address some of caregivers’ unmet needs is via the design of digital technologies that support caregivers’ complex portfolio of responsibilities. Augmented reality (AR) applications, specifically, offer new affordances to aid caregivers as they perform care tasks in the home. </sec> <sec> <title>OBJECTIVE</title> This study explored how AR might assist family caregivers with the delivery of home-based cancer care. The specific objectives were to shed light on challenges caregivers face where AR might help, investigate opportunities for AR to support caregivers, and understand the risks of AR exacerbating caregiver burdens. </sec> <sec> <title>METHODS</title> We conducted a qualitative video elicitation study with clinicians and caregivers. We created 3 video elicitations that offer ways in which AR might support caregivers as they perform often high-stakes, unfamiliar, and anxiety-inducing tasks in postsurgical cancer care: wound care, drain care, and rehabilitative exercise. The elicitations show functional AR applications built using Unity Technologies software and Microsoft Hololens2. Using elicitations enabled us to avoid rediscovering known usability issues with current AR technologies, allowing us to focus on high-level, substantive feedback on potential future roles for AR in caregiving. Moreover, it enabled nonintrusive exploration of the inherently sensitive in-home cancer care context. </sec> <sec> <title>RESULTS</title> We recruited 22 participants for our study: 15 clinicians (eg, oncologists and nurses) and 7 family caregivers. Our findings shed light on clinicians’ and caregivers’ perceptions of current information and communication challenges caregivers face as they perform important physical care tasks as part of cancer treatment plans. Most significant was the need to provide better and ongoing support for execution of caregiving tasks in situ, when and where the tasks need to be performed. Such support needs to be tailored to the specific needs of the patient, to the stress-impaired capacities of the caregiver, and to the time-constrained communication availability of clinicians. We uncover opportunities for AR technologies to potentially increase caregiver confidence and reduce anxiety by supporting the capture and review of images and videos and by improving communication with clinicians. However, our findings also suggest ways in which, if not deployed carefully, AR technologies might exacerbate caregivers’ already significant burdens. </sec> <sec> <title>CONCLUSIONS</title> These findings can inform both the design of future AR devices, software, and applications and the design of caregiver support interventions based on already available technology and processes. Our study suggests that AR technologies and the affordances they provide (eg, tailored support, enhanced monitoring and task accuracy, and improved communications) should be considered as a part of an integrated care journey involving multiple stakeholders, changing information needs, and different communication channels that blend in-person and internet-based synchronous and asynchronous care, illness, and recovery. </sec>

  PublisherOA PDFDOI

• Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare

  Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2024-11-21 · 8 citations

  articleOpen access

  Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges. Our findings motivate research on actionable sensing, as an alternative to detection research, studying how passive sensing can augment traditional mental health measures to support actions in clinical care. Specifically, we identify how passive sensing can support clinical actions by revealing patients' presenting problems for treatment and identifying targets for behavior change and symptom reduction, but passive data requires additional contextual information to be appropriately interpreted and used in care. We conclude by suggesting research at the intersection of actionable sensing and mental healthcare, to align technical research in ubiquitous computing with clinical actions and needs.

  PublisherOA PDFDOI

• Designing Voice-First Ambient Interfaces to Support Aging in Place

  2023-07-10 · 22 citations

  article

  We focus on the stories of five older adults who became voice assistant users through our study, and with whom we speculated about future interfaces through two design probes, one for health data reporting and one for positive reminiscing. We delivered a voice-first ambient interface (VFAI) to each participant, and closely observed participants’ journeys through periodic themed interviews (16 hours, 21 minutes of transcribed recordings), usage log reviews (4,657 entries), and phone and text support. Participants’ lived experiences impacted their perceptions and interactions with their VFAI, fueling rich insights about how to design for diverse needs. For example, while one participant saw increased potential in the VFAI after interacting with the design probe for health data reporting, another was skeptical of using it to communicate with her doctor. We contribute an in-depth exploration of VFAIs to support aging in place, implications for design, and areas for future work for tailoring VFAIs towards enabling continuity of care in people’s homes.

  PublisherDOI

• Perceptions About Augmented Reality in Remote Medical Care: Interview Study of Emergency Telemedicine Providers

  JMIR Formative Research · 2023-02-16 · 29 citations

  articleOpen access

  Background Augmented reality (AR) and virtual reality (VR) have increasingly appeared in the medical literature in the past decade, with AR recently being studied for its potential role in remote health care delivery and communication. Recent literature describes AR’s implementation in real-time telemedicine contexts across multiple specialties and settings, with remote emergency services in particular using AR to enhance disaster support and simulation education. Despite the introduction of AR in the medical literature and its potential to shape the future of remote medical services, studies have yet to investigate the perspectives of telemedicine providers regarding this novel technology. Objective This study aimed to understand the applications and challenges of AR in telemedicine anticipated by emergency medicine providers with a range of experiences in using telemedicine and AR or VR technology. Methods Across 10 academic medical institutions, 21 emergency medicine providers with variable exposures to telemedicine and AR or VR technology were recruited for semistructured interviews via snowball sampling. The interview questions focused on various potential uses of AR, anticipated obstacles that prevent its implementation in the telemedicine area, and how providers and patients might respond to its introduction. We included video demonstrations of a prototype using AR during the interviews to elicit more informed and complete insights regarding AR’s potential in remote health care. Interviews were transcribed and analyzed via thematic coding. Results Our study identified 2 major areas of use for AR in telemedicine. First, AR is perceived to facilitate information gathering by enhancing observational tasks such as visual examination and granting simultaneous access to data and remote experts. Second, AR is anticipated to supplement distance learning of both minor and major procedures and nonprocedural skills such as cue recognition and empathy for patients and trainees. AR may also supplement long-distance education programs and thereby support less specialized medical facilities. However, the addition of AR may exacerbate the preexisting financial, structural, and literacy barriers to telemedicine. Providers seek value demonstrated by extensive research on the clinical outcome, satisfaction, and financial benefits of AR. They also seek institutional support and early training before adopting novel tools such as AR. Although an overall mixed reception is anticipated, consumer adoption and awareness are key components in AR’s adoption. Conclusions AR has the potential to enhance the ability to gather observational and medical information, which would serve a diverse set of applications in remote health care delivery and education. However, AR faces obstacles similar to those faced by the current telemedicine technology, such as lack of access, infrastructure, and familiarity. This paper discusses the potential areas of investigation that would inform future studies and approaches to implementing AR in telemedicine.

  PublisherOA PDFDOI

Recent grants

• Center for Embedded Networked Sensing (CENS)

  NSF · $39.4M · 2002–2013

• SCH: INT: Novel Techniques for Patient-centric Disease Management using Automatically Inferred Behavioral Biomarkers and Sensor-Supported Contextual Self-Report

  NSF · $2.0M · 2013–2018

• Ethics Education for Participatory Urban Sensing

  NSF · $298k · 2008–2012

• CHS: Medium: Immersive Recommendation Systems: User-Centric Recommendation Models and Applications

  NSF · $1.2M · 2017–2022

Frequent coauthors

• Kristofer S. J. Pister

  University of California, Berkeley

  101 shared

• Phil Gibbons

  University of California, Berkeley

  100 shared

• Viktor K. Prasanna

  100 shared

• Sajal K. Das

  100 shared

• Christos H. Papadimitriou

  Columbia University

  100 shared

• Josep Dı́az

  100 shared

• Habib M. Ammari

  Texas A&M University – Kingsville

  100 shared

• Sotiris Nikoletseas

  100 shared

Labs

• Deborah Estrin's LabPI

  Research activities focus on digitally-enabled innovations that support patients and providers in optimizing clinical outcomes and quality of life.

Education

• PhD, EECS

  Massachusetts Institute of Technology

  1985

Awards & honors

• IEEE Internet Award
• MacArthur Fellowship
• IEEE John von Neumann Medal
• Elected Member of the American Academy of Arts and Sciences
• Elected Member of the National Academy of Engineering