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What and Who

Data Science for Human Well-being

Tim Althoff
Stanford University
SWS Colloquium

Tim Althoff is a Ph.D. candidate in Computer Science in the Infolab at Stanford University
advised by Jure Leskovec. His research advances computational methods to improve human
well-being, combining techniques from Data Mining, Social Network Analysis, and Natural
Language Processing. Prior to his PhD, Tim obtained M.S. and B.S. degrees from Stanford
University and University of Kaiserslautern, Germany. He has received several fellowships and
awards including the SAP Stanford Graduate Fellowship, Fulbright scholarship, German
Academic Exchange Service scholarship, the German National Merit Foundation scholarship,
and a Best Paper Award by the International Medical Informatics Association. Tim’s research
has been covered internationally by news outlets including BBC, CNN, The Economist, The Wall Street Journal, and The New York Times.
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AG Audience

Date, Time and Location

Monday, 26 March 2018
60 Minutes


The popularity of wearable and mobile devices, including smartphones and smartwatches, has
generated an explosion of detailed behavioral data. These massive digital traces provide us with
an unparalleled opportunity to realize new types of scientific approaches that provide novel
insights about our lives, health, and happiness. However, gaining valuable insights from these
data requires new computational approaches that turn observational, scientifically “weak” data
into strong scientific results and can computationally test domain theories at scale.
In this talk, I will describe novel computational methods that leverage digital activity traces at
the scale of billions of actions taken by millions of people. These methods combine insights
from data mining, social network analysis, and natural language processing to generate
actionable insights about our physical and mental well-being. Specifically, I will describe how
massive digital activity traces reveal unknown health inequality around the world, and how
personalized predictive models can target personalized interventions to combat this inequality.
I will demonstrate that modelling how fast we are using search engines enables new types of
insights into sleep and cognitive performance. Further, I will describe how natural language
processing methods can help improve counseling services for millions of people in crisis.
I will conclude the talk by sketching interesting future directions for computational approaches
that leverage digital activity traces to better understand and improve human well-being.


Vera Schreiber
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Vera Schreiber, 03/27/2018 12:38 -- Created document.