Anind K. Dey

Affiliations: 
2001-2004 Intel Research 
 2005-2018 Human-Computer Interaction Institute Carnegie Mellon University, Pittsburgh, PA 
 2018- Information School University of Washington, Seattle, Seattle, WA 
Area:
Computer Science, Human-Computer Interaction, Mobile Computing, Machine Learning, Ubiquitous Computing
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"Anind Dey"

Parents

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Gregory D. Abowd grad student 1995-2000 Georgia Tech (Computer Science Tree)
 (Thesis: Providing Architectural Support for Building Context-Aware Applications)

Children

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Brian D. Ziebart grad student 2004-2010 University of Illinois, Chicago
Ian A. Li grad student 2011 Google
Scott Davidoff grad student 2004-2011 NASA Jet Propulsion Laboratory
Matthew Lee grad student 2012 FXPAL
Brian Y. Lim grad student 2012 National University of Singapore
Stephanie L. Rosenthal grad student 2012 Carnegie Mellon
Senaka Buthpitiya grad student 2014 Google
Gabriela Marcu grad student 2014 University of Michigan
Christian Koehler grad student 2015 Samsung
Adrian de Freitas grad student 2017 Air Force
Nikola Banovic grad student 2012-2018 Carnegie Mellon
Momin Malik grad student 2019 Harvard College
Brandon Taylor grad student 2019
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Publications

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Nurius PS, Sefidgar YS, Kuehn KS, et al. (2021) Distress among undergraduates: Marginality, stressors and resilience resources. Journal of American College Health : J of Ach. 1-9
Morris ME, Kuehn KS, Brown J, et al. (2021) College from home during COVID-19: A mixed-methods study of heterogeneous experiences. Plos One. 16: e0251580
Low CA, Li M, Vega J, et al. (2021) Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study. Jmir Cancer. 7: e27975
Low CA, Danko M, Durica KC, et al. (2020) A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Usability and Feasibility Study. Jmir Perioperative Medicine. 3: e17292
Chung T, Bae SW, Mun EY, et al. (2020) Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study. Jmir Mhealth and Uhealth. 8: e16240
Doryab A, Villalba DK, Chikersal P, et al. (2019) Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data. Jmir Mhealth and Uhealth. 7: e13209
Low CA, Dey AK, Ferreira D, et al. (2017) Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study. Journal of Medical Internet Research. 19: e420
Bae S, Chung T, Ferreira D, et al. (2017) Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addictive Behaviors
Crooks A, Schechtner K, Dey AK, et al. (2017) Creating Smart Buildings and Cities Ieee Pervasive Computing. 16: 23-25
Arnrich B, Ersoy C, Mayora O, et al. (2016) Wearable Therapy - Detecting Information from Wearables and Mobiles that are Relevant to Clinical and Self-directed Therapy. Methods of Information in Medicine
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