About

Tanzeem Choudhury is an Associate Professor in the Information Science department at Cornell University. The page lists him among current graduate students and provides information about his students' research projects and theses, indicating his role as a faculty member guiding research in areas related to mobile sensing, human behavior inference, and activity recognition. His research focus includes smartphone sensing and inference of human behavior and context, as evidenced by the titles of his students' PhD theses. The page does not provide a detailed personal biography or a comprehensive overview of his research contributions beyond these topics. Therefore, the available information highlights his position, mentorship, and research interests in mobile sensing and human behavior analysis.

Research topics

• Computer Science
• Medicine
• Artificial Intelligence
• Machine Learning
• Psychiatry
• Psychology
• Political Science
• Computer Security
• Internal medicine
• Human–computer interaction
• Data Mining
• World Wide Web
• Telecommunications
• Family medicine
• Cognitive psychology
• Data science
• Embedded system
• Public relations
• Database
• Nursing
• Social psychology
• Neuroscience
• Clinical psychology
• Chemistry

Selected publications

• EDTT Dataset

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  datasetOpen accessSenior author

  EDTT is the public-facing EDTT dataset, a clinically grounded dataset of pro-ED TikTok posts annotated for multimodal features, curated through text-based retrieval, recommendation-based surfacing, and annotated by subject-matter experts. The current, public-facing EDTT dataset is small (but robustly human-annotated) and is available for immediate download and use. For privacy preservation, the full unpublished dataset includes private variables which are not publicly available, in accordance with the TikTok Research API Terms of Service. No identifiable information is included in the dataset to protect the privacy of TikTok users, and no attempts were made to identify individual users or access private data. Please contact us if you have any questions. This initial dataset was prepared for presentation at the 47th Annual Meeting & Scientific Sessions of the Society of Behavioral Medicine (SBM), held April 22–25, 2026 in Chicago, IL, USA.

  PublisherDOI

• Banbury Forum Consensus Statement on the Path Forward for Digital Mental Health Treatment

  FOCUS The Journal of Lifelong Learning in Psychiatry · 2025-07-01 · 1 citations

  article

  A major obstacle to mental health treatment for many Americans is accessibility: the United States faces a shortage of mental health providers, resulting in federally designated shortage areas. Although digital mental health treatments (DMHTs) are effective interventions for common mental disorders, they have not been widely adopted by the U.S. health care system. National and international expert stakeholders representing health care organizations, insurance companies and payers, employers, patients, researchers, policy makers, health economists, and DMHT companies and the investment community attended two Banbury Forum meetings. The Banbury Forum reviewed the evidence for DMHTs, identified the challenges to successful and sustainable implementation, investigated the factors that contributed to more successful implementation internationally, and developed the following recommendations: guided DMHTs should be offered to all patients experiencing common mental disorders, DMHT products and services should be reimbursable to support integration into the U.S. health care landscape, and an evidence standards framework should be developed to support decision makers in evaluating DMHTs. Reprinted from Psychiatr Serv 2021; 72:677–683, with permission from American Psychiatric Association. Copyright © 2021

  PublisherDOI

• SpectraPhone: A Smartphone Based Spectrometer for High-Resolution Urinalysis

  Research Square · 2025-07-30

  preprintOpen access
  PublisherDOI

• Forecasting Occupational Survivability of Rickshaw Pullers in a Changing Climate with Wearable Data

  Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2025-12-02

  articleOpen access

  While the vulnerability of cycle rickshaw pullers to extreme heat is well recognized, little effort has been devoted to modeling how their physiological biomarkers respond under such conditions. In this study, we collect real-time weather and physiological data using a wearable computing platform from 100 rickshaw pullers in Dhaka, Bangladesh. In parallel, we interview 12 additional rickshaw pullers to explore their knowledge, perceptions, and experiences related to climate change. We propose a Linear Gaussian Bayesian Network (LGBN)-based regression model that predicts key physiological biomarkers based on activity, weather, and demographic features. The model achieves normalized mean absolute error (NMAE) of 0.82, 0.47, 0.65, and 0.67, respectively, for the biomarker: skin temperature, relative cardiac cost, skin conductance response, and skin conductance level. Using climate model projections from 18 CMIP6 global climate models, we layer the LGBN on top of future climate forecasts to conduct a survivability analysis for both current (2023-2025) and future years (2026-2100). Based on the criteria T WEGT > 31.1° C and T skiin > 35°C, the analysis shows that a significant percentage of rickshaw pullers (32%) are already facing a high risk of heat-related illness or prolonged exposure to extreme heat (T WBGT > 31.1°C) during regular work hours. In future years, e.g., 2026–2030, based on the CMIP6-based climate models, this percentage can rise to 37 ±17% with an exposure duration of 11.9 ±2 minutes (68% of the trip duration) on average. A similar trend is found based on rickshaw pullers' skin temperature with exposure (T skin > 35°C) durations expanding from 11 minutes (64% of the trip duration) to 13 ± 2 minutes (73% of the trip duration) by 2026-2030. Finally, a Thematic Analysis of interview data provides qualitative insights that complement the current observation and model's predictions in the future. The findings reveal that rickshaw

  PublisherDOI

• Forecasting Occupational Survivability of Rickshaw Pullers in a Changing Climate with Wearable Data

  ArXiv.org · 2025-10-29

  preprintOpen access

  Cycle rickshaw pullers are highly vulnerable to extreme heat, yet little is known about how their physiological biomarkers respond under such conditions. This study collected real-time weather and physiological data using wearable sensors from 100 rickshaw pullers in Dhaka, Bangladesh. In addition, interviews with 12 pullers explored their knowledge, perceptions, and experiences related to climate change. We developed a Linear Gaussian Bayesian Network (LGBN) regression model to predict key physiological biomarkers based on activity, weather, and demographic features. The model achieved normalized mean absolute error values of 0.82, 0.47, 0.65, and 0.67 for skin temperature, relative cardiac cost, skin conductance response, and skin conductance level, respectively. Using projections from 18 CMIP6 climate models, we layered the LGBN on future climate forecasts to analyze survivability for current (2023-2025) and future years (2026-2100). Based on thresholds of WBGT above 31.1°C and skin temperature above 35°C, 32% of rickshaw pullers already face high heat exposure risk. By 2026-2030, this percentage may rise to 37% with average exposure lasting nearly 12 minutes, or about two-thirds of the trip duration. A thematic analysis of interviews complements these findings, showing that rickshaw pullers recognize their increasing climate vulnerability and express concern about its effects on health and occupational survivability.

  PublisherOA PDFDOI

• Reliability of AI Tools in Predicting Depression Risk Using Smartphone Sensed-Behavioral Data

  Biological Psychiatry · 2025-04-09

  article1st authorCorresponding
  PublisherDOI

• Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep Health

  2025-04-24 · 15 citations

  preprintOpen access

  Despite the prevalence of sleep-tracking devices, many individuals struggle to translate data into actionable improvements in sleep health. Current methods often provide data-driven suggestions but may not be feasible and adaptive to real-life constraints and individual contexts. We present HealthGuru, a novel large language model-powered chatbot to enhance sleep health through data-driven, theory-guided, and adaptive recommendations with conversational behavior change support. HealthGuru's multi-agent framework integrates wearable device data, contextual information, and a contextual multi-armed bandit model to suggest tailored sleep-enhancing activities. The system facilitates natural conversations while incorporating data-driven insights and theoretical behavior change techniques. Our eight-week in-the-wild deployment study with 16 participants compared HealthGuru to a baseline chatbot. Results show improved metrics like sleep duration and activity scores, higher quality responses, and increased user motivation for behavior change with HealthGuru. We also identify challenges and design considerations for personalization and user engagement in health chatbots.

  PublisherOA PDFDOI

• Designing Technologies for Value-based Mental Healthcare: Centering Clinicians' Perspectives on Outcomes Data Specification, Collection, and Use

  2025-04-24 · 1 citations

  preprintOpen accessSenior author

  Health information technologies are transforming how mental healthcare is paid for through value-based care programs, which tie payment to data quantifying care outcomes. But, it is unclear what outcomes data these technologies should store, how to engage users in data collection, and how outcomes data can improve care. Given these challenges, we conducted interviews with 30 U.S.-based mental health clinicians to explore the design space of health information technologies that support outcomes data specification, collection, and use in value-based mental healthcare. Our findings center clinicians' perspectives on aligning outcomes data for payment programs and care; opportunities for health technologies and personal devices to improve data collection; and considerations for using outcomes data to hold stakeholders including clinicians, health insurers, and social services financially accountable in value-based mental healthcare. We conclude with implications for future research designing and developing technologies supporting value-based care across stakeholders involved with mental health service delivery.

  PublisherDOI

• Large language models for the mental health community: framework for translating code to care

  The Lancet Digital Health · 2025-01-07 · 37 citations

  reviewOpen access

  Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation. In this Viewpoint, we propose a sociocultural-technical approach to address these challenges. We highlight five key areas for development: (1) building a global clinical repository to support LLMs training and testing, (2) designing ethical usage settings, (3) refining diagnostic categories, (4) integrating cultural considerations during development and deployment, and (5) promoting digital inclusivity to ensure equitable access. We emphasise the need for developing representative datasets, interpretable clinical decision support systems, and new roles such as digital navigators. Only through collaborative efforts across all stakeholders, unified by a sociocultural-technical framework, can we clinically deploy LLMs while ensuring equitable access and mitigating risks.

  PublisherOA PDFDOI

• 9 ^th International Workshop on Mental Health and Well-being: New Research Directions

  2024-09-22

  article

  Mental health and well-being influence overall health: suffering from a mental illness can create severe impairment and reduce quality of life. Ubiquitous computing technologies are beginning to play a central role in collecting clinically relevant behavioral and physiological information on mental health that can be used to detect symptoms early-on, deliver preventative interventions, and manage symptoms throughout the course of illness. Despite this potential, designing and translating ubiquitous technologies into mental healthcare is a complex process, and existing technologies have faced numerous challenges towards effective implementation. The goal of this workshop is to bring together researchers, practitioners, and industry professionals to identify, articulate, and address the challenges of designing and implementing ubiquitous computing technologies in mental healthcare. Given these challenges, we are adding a specific call for papers that inspire new research directions, with initial findings that are valuable to the community, but are not fully publishable or finished contributions. Following the success of this workshop for the last eight years, we aim to continue facilitating the UbiComp community in both the conceptualization, translation, and implementation of novel mental health sensing and intervention technologies.

  PublisherDOI

Recent grants

• CAREER: Enabling Community-Scale Modeling of Human Behavior and its Application to Healthcare

  NSF · $440k · 2011–2016

• CAREER: Enabling Community-Scale Modeling of Human Behavior and its Application to Healthcare

  NSF · $290k · 2009–2011

• Collaborative Research: HCC: MEDIUM: Body as Intervention: Toward Closed-Loop, Embodied Behavioral Health Interventions

  NSF · $687k · 2022–2026

• FW-HTF: Collaborative Research: An Embodied Intelligent Cognitive Assistant to Enhance Cognitive Performance of Shift Workers

  NSF · $590k · 2018–2021

Frequent coauthors

• Daniel A. Adler

  Cornell University

  50 shared

• Andrew T. Campbell

  University of Toledo

  44 shared

• David C. Mohr

  Northwestern University

  42 shared

• Fei Wang

  Boehringer Ingelheim (United States)

  40 shared

• Srijan Sen

  Cornell University

  38 shared

• Caitlin A. Stamatis

  Northwestern University

  38 shared

• Gabriel J. Aranovich

  Cornell University

  37 shared

• Saeed Abdullah

  31 shared

Labs

• Tanzeem Choudhury LabPI

  Not provided

Awards & honors

• NSF CAREER award (2008)
• TR35 award (2008)
• TED fellowship (2009)
• PopTech Science fellowship (2010)
• Kavli fellowship (2011)