About

Gregory D. Abowd is the dean of Northeastern University’s College of Engineering and an ECE professor. His research has focused on ubiquitous computing, a field that explores the growth of computing beyond traditional desktop environments, emphasizing its application and social impacts in everyday life. His work has particularly addressed how emerging technologies can be integrated into classrooms, homes, and healthcare settings to create seamless and meaningful experiences. Notably, he founded the 'Classroom 2000' project to study the integration of computing capabilities into educational environments and established the 'Aware Home Research Initiative' in 1998 to develop technology that perceives and assists home occupants. More recently, Abowd has been involved in sustainable electronics, aiming to make computing more environmentally friendly by rethinking manufacturing and disposal processes, such as developing alternative soldering methods and creating tools for consumers to assess the environmental impact of electronic products. His contributions have been recognized with awards like the Lifetime Research Award from the ACM’s Special Interest Group for Computer-Human Interaction. In 2025, he was elected a Member of the American Academy of Arts and Sciences, an honor that highlights his significant impact in the field and provides opportunities to recognize other distinguished individuals.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Psychology
• Telecommunications
• Data Mining
• Engineering
• Embedded system
• Optoelectronics
• Electrical engineering
• Computer vision
• Management
• Physics
• Real-time computing
• Developmental psychology
• Materials science
• Cognitive science
• Algorithm
• Electronic engineering
• Database
• Data science
• Knowledge management

Selected publications

• Bioinspired Camouflage Fibers with Computer Vision-Guided Chromatic Adaptation

  ACS Nano · 2025-05-16 · 2 citations

  article

  The development of intelligent camouflage systems demands advanced materials and control strategies for dynamic environmental adaptation. Here, we demonstrate a bioinspired camouflage system using hydroxypropyl cellulose (HPC), a cellulose derivative that forms cholesteric liquid crystals with mechanically tunable structural colors. By integrating HPC fibers with computer vision-assisted control, we achieve autonomous color matching with the environment through precise mechanical manipulation. Our system employs computer vision and a custom-designed wavelength-value (WV) mapping algorithm to analyze surroundings and control fiber tension, enabling direct modulation of the reflected wavelength. The closed-loop control system achieves color matching with less than 5% error at room temperature and maintains over 95% accuracy across temperatures from 15 to 35 °C. The HPC fibers exhibit reversible color transitions spanning the visible spectrum (400-700 nm). This integration of sustainable biomaterials with computer vision-guided mechanical control demonstrates an alternative approach for advanced camouflage applications, including military concealment and anticounterfeiting technologies.

  PublisherDOI

• Living Sustainability: In-Context Interactive Environmental Impact Communication

  Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2025-09-03 · 1 citations

  articleOpen access

  Climate change demands urgent action, yet understanding the environmental impact (EI) of everyday objects and activities remains challenging for the general public. While Life Cycle Assessment (LCA) offers a comprehensive framework for EI analysis, its traditional implementation requires extensive domain expertise, structured input data, and significant time investment, creating barriers for non-experts seeking real-time sustainability insights. Here we present the first autonomous sustainability assessment tool that bridges this gap by transforming unstructured natural language descriptions into in-context, interactive EI visualizations. Our approach combines language modeling and AI agents, and achieves >97% accuracy in transforming natural language into a data abstraction designed for simplified LCA modeling. The system employs a non-parametric datastore to integrate proprietary LCA databases while maintaining data source attribution and allowing personalized source management. We demonstrate through case studies that our system achieves results within 11% of traditional full LCA, while accelerating from hours of expert time to real-time. We conducted a formative elicitation study (N=6) to inform the design objectives of such EI communication augmentation tools. We implemented and deployed the tool as a Chromium browser extension and further evaluated it through a user study (N=12). This work represents a significant step toward democratizing access to environmental impact information for the general public with zero LCA expertise.

  PublisherOA PDFDOI

• Towards Autonomous Sustainability Assessment via Multimodal AI Agents

  ArXiv.org · 2025-07-22 · 1 citations

  preprintOpen access

  Interest in sustainability information has surged in recent years. However, the data required for a life cycle assessment (LCA) that maps the materials and processes from product manufacturing to disposal into environmental impacts (EI) are often unavailable. Here we reimagine conventional LCA by introducing multimodal AI agents that emulate interactions between LCA experts and stakeholders like product managers and engineers to calculate the cradle-to-gate (production) carbon emissions of electronic devices. The AI agents iteratively generate a detailed life-cycle inventory leveraging a custom data abstraction and software tools that extract information from online text and images from repair communities and government certifications. This approach reduces weeks or months of expert time to under one minute and closes data availability gaps while yielding carbon footprint estimates within 19% of expert LCAs with zero proprietary data. Additionally, we develop a method to directly estimate EI by comparing an input to a cluster of products with similar descriptions and known carbon footprints. This runs in 3 ms on a laptop with a MAPE of 12.28% on electronic products. Further, we develop a data-driven method to generate emission factors. We use the properties of an unknown material to represent it as a weighted sum of emission factors for similar materials. Compared to human experts picking the closest LCA database entry, this improves MAPE by 120.26%. We analyze the data and compute scaling of this approach and discuss its implications for future LCA workflows.

  PublisherOA PDFDOI

• IntiVisor: A Visual Analytics System for Interaction Log Analysis

  IEEE Transactions on Visualization and Computer Graphics · 2024-02-28 · 8 citations

  article

  Application developers frequently augment their code to produce event logs of specific operations performed by their users. Subsequent analysis of these event logs can help provide insight about the users' behavior relative to its intended use. The analysis process typically includes both event organization and pattern discovery activities. However, most existing visual analytics systems for interaction log analysis excel at supporting pattern discovery and overlook the importance of flexible event organization. This omission limits the practical application of these systems. Therefore, we developed a novel visual analytics system called IntiVisor that implements the entire end-to-end interaction analysis approach. An evaluation of the system with interaction data from four visualization applications showed the value and importance of supporting event organization in interaction log analysis.

  PublisherDOI

• Recy-ctronics: Designing Fully Recyclable Electronics With Varied Form Factors

  arXiv (Cornell University) · 2024-06-13 · 2 citations

  preprintOpen access

  For today's electronics manufacturing process, the emphasis on stable functionality, durability, and fixed physical forms is designed to ensure long-term usability. However, this focus on robustness and permanence complicates the disassembly and recycling processes, leading to significant environmental repercussions. In this paper, we present three approaches that leverage easily recyclable materials-specifically, polyvinyl alcohol (PVA) and liquid metal (LM)-alongside accessible manufacturing techniques to produce electronic components and systems with versatile form factors. Our work centers on the development of recyclable electronics through three methods: 1) creating sheet electronics by screen printing LM traces on PVA substrates; 2) developing foam-based electronics by immersing mechanically stirred PVA foam into an LM solution; and 3) fabricating recyclable electronic tubes by injecting LM into mold cast PVA tubes, which can then be woven into various structures. To further assess the sustainability of our proposed methods, we conducted a life cycle assessment (LCA) to evaluate the environmental impact of our recyclable electronics in comparison to their conventional counterparts.

  PublisherOA PDFDOI

• Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers

  2024-05-11 · 17 citations

  articleOpen access

  Algorithmic estimations of worker behavior are gaining popularity. Passive Sensing–enabled AI (PSAI) systems leverage behavioral traces from workers’ digital tools to infer their experience. Despite their conceptual promise, the practical designs of these systems elicit tensions that lead to workers resisting adoption. This paper teases apart the monolithic representation of PSAI by investigating system components that maximize value and mitigate concerns. We conducted an interactive online survey using the Experimental Vignette Method. Using Linear Mixed-effects Models we found that PSAI systems were more acceptable when sensing digital time use or physical activity, instead of visual modes. Inferences using language were only acceptable in work-restricted contexts. Compared to insights into performance, workers preferred insights into mental wellbeing. However, they resisted systems that automatically forwarded these insights to others. Our findings provide a template to reflect on existing systems and plan future implementations of PSAI to be more worker-centered.

  PublisherOA PDFDOI

• Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment

  2024-05-11 · 12 citations

  articleOpen access

  Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.

  PublisherOA PDFDOI

• Leveraging Social Media to Predict COVID-19–Induced Disruptions to Mental Well-Being Among University Students: Modeling Study

  JMIR Formative Research · 2024-04-11 · 5 citations

  articleOpen access

  BACKGROUND: Large-scale crisis events such as COVID-19 often have secondary impacts on individuals' mental well-being. University students are particularly vulnerable to such impacts. Traditional survey-based methods to identify those in need of support do not scale over large populations and they do not provide timely insights. We pursue an alternative approach through social media data and machine learning. Our models aim to complement surveys and provide early, precise, and objective predictions of students disrupted by COVID-19. OBJECTIVE: This study aims to demonstrate the feasibility of language on private social media as an indicator of crisis-induced disruption to mental well-being. METHODS: We modeled 4124 Facebook posts provided by 43 undergraduate students, spanning over 2 years. We extracted temporal trends in the psycholinguistic attributes of their posts and comments. These trends were used as features to predict how COVID-19 disrupted their mental well-being. RESULTS: The social media-enabled model had an F1-score of 0.79, which was a 39% improvement over a model trained on the self-reported mental state of the participant. The features we used showed promise in predicting other mental states such as anxiety, depression, social, isolation, and suicidal behavior (F1-scores varied between 0.85 and 0.93). We also found that selecting the windows of time 7 months after the COVID-19-induced lockdown presented better results, therefore, paving the way for data minimization. CONCLUSIONS: We predicted COVID-19-induced disruptions to mental well-being by developing a machine learning model that leveraged language on private social media. The language in these posts described psycholinguistic trends in students' online behavior. These longitudinal trends helped predict mental well-being disruption better than models trained on correlated mental health questionnaires. Our work inspires further research into the potential applications of early, precise, and automatic warnings for individuals concerned about their mental health in times of crisis.

  PublisherDOI

• Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research

  Proceedings of the ACM on Human-Computer Interaction · 2024-04-17 · 17 citations

  article

  In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.

  PublisherDOI

• Transient Internet of Things: Redesigning the Lifetime of Electronics for a More Sustainable Networked Environment

  ACM SIGEnergy Energy Informatics Review · 2024-12-01

  article

  Mark Weiser predicted in 1991 that computing would lead to individuals interacting with countless computing devices, seamlessly integrating them into their daily lives until they disappear into the background [42]. However, achieving this seamless integration while addressing the associated environmental concerns is challenging. Trillions of smart devices with varied capabilities and form-factor are needed to build a networked environment of this magnitude. Yet, conventional computing paradigms require plastic housings, PCB boards, and rare-earth minerals, coupled with hazardous waste, and challenging reclamation and recycling, leading to significant e-waste. The current linear lifecycle design of electronic devices does not allow circulation among different life stages, neglecting features like recyclability and repairability during the design process. In this position paper, we present the concept of computational materials designed for transiency as a substitute for current devices. We envision that not all devices must be designed with performance, robustness, or even longevity as the sole goal. We detail computer systems challenges to the circular economy of computational materials and provide strategies and sketches of tools to assess a device's entire lifetime environmental impact.

  PublisherDOI

Recent grants

• ITR/SY: The Aware Home: Sustaining the Quality of Life for an Aging Population

  NSF · $1.6M · 2001–2007

Frequent coauthors

• Julie A. Kientz

  University of Washington

  52 shared

• Gillian R. Hayes

  University of California, Irvine

  49 shared

• Munmun De Choudhury

  37 shared

• Rosa I. Arriaga

  Georgia Institute of Technology

  34 shared

• Thad Starner

  Georgia Institute of Technology

  31 shared

• Shwetak Patel

  University of Washington

  29 shared

• Anind K. Dey

  28 shared

• Khai N. Truong

  27 shared

Labs

• Abowd Elected Member of American Academy of Arts and SciencesPI

Education

• D.Phil. in Computation, Programming Research Group

  University of Oxford

  1991

• M.Sc. in Computation, Programming Research Group

  University of Oxford

  1987

• B.S., Mathematics

  University of Notre Dame

  1986

Awards & honors

• Lifetime Research Award from the Association for Computing M…
• Member of the American Academy of Arts and Sciences