Tim Althoff

About

Tim Althoff is an Assistant Professor in Computer Science at the University of Washington. His research aims to better understand and empower people through data by developing computational methods that leverage large-scale behavioral data to extract actionable insights about life, health, and happiness. His work combines techniques from data science, social network analysis, and natural language processing. He directs the Behavioral Data Science Group, which focuses on research related to mental health, misinformation, scientific reproducibility, and public health, including efforts to inform the COVID-19 response. Tim Althoff is actively seeking postdoctoral researchers and PhD students, particularly in areas such as neural representation learning, natural language processing with applications to psychology and mental health, causal inference, data science, and mobile health.

Research topics

• Computer Science
• Medicine
• Psychology
• Social psychology
• Internal medicine
• Social Science
• Political Science
• Sociology
• Nursing
• Software engineering

Selected publications

• Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support

  Nature Machine Intelligence · 2023 · 396 citations

  Senior authorCorresponding
  DOI

• Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

  Organizational Behavior and Human Decision Processes · 2021 · 133 citations

  In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency…

  PublisherDOI

• Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data

  Patterns · 2020 · 80 citations

  The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic…

  DOI

• Boba: Authoring and Visualizing Multiverse Analyses

  IEEE Transactions on Visualization and Computer Graphics · 2020 · 79 citations

  Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency. However, specifying a multiverse is demanding because analysts must manage myriad variants from a cross-product of analytic decisions, and the results require nuanced interpretation. We contribute Baba: an integrated domain-specific language (DSL) and visual analysis system for authoring and review…

  PublisherOA PDFDOI

• Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective

  Annual Review of Public Health · 2022 · 33 citations

  Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the conco…

  PublisherOA PDFDOI

Recent grants

• Enhancing Engagement with Digital Mental Health Care

  NIH · $873k · 2020–2024

• Enhancing Engagement with Digital Mental Health Care

  NIH · $2.3M · 2020–2025

Frequent coauthors

• Adam S. Miner

  33 shared

• Inna Wanyin Lin

  30 shared

• Jure Leskovec

  Stanford University

  29 shared

• Kevin Rushton

  Mental Health America

  21 shared

• Ashish Sharma

  UNSW Sydney

  20 shared

• David Wadden

  20 shared

• Theresa Nguyen

  18 shared

• Jina Suh

  17 shared

Education

• Ph.D.

  Stanford University

• M.S.

  Stanford University

• B.S.

  University of Kaiserslautern, Germany

Awards & honors

• SAP Stanford Graduate Fellowship
• Fulbright scholarship
• German Academic Exchange Service scholarship
• German National Merit Foundation scholarship
• Best Paper Award by the International Medical Informatics As…

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