Adam Sadilek :: Research


Research Overview

I develop scalable machine learning techniques that help us understand and predict emergent global processes, such as disease epidemics and crowd package delivery. The goal is to model human behavior from our day-to-day interactions recorded online, rather than relying on surveys. For example, we have shown that from this real-time data, we can calculate the likelihood that you will catch a cold tomorrow on the basis of who you met and what they recently tweeted about.

Working with Henry Kautz, Eric Horvitz, John Krumm, Jeff Bigham, Walter Lasecki and others, we found that organic sensor networks—made up of people running around with their smartphones and occasionally posting to online social media— are an important complement to the explicit social networks we create manually. Location plays a key role here.

Applying a combination of machine learning and human computation, we have leveraged organic sensor networks to predict the spread of flu between specific individuals (project GermTracker), and to identify restaurants with a high risk of food poisoning (project nEmesis).

News coverage of our research includes New York Times, Wired, Gizmodo, NPR, MIT Technology Review, IEEE Spectrum, CNN, The Guardian, and New Scientist.

Along with my studies, I have done research & development work at eBay Research Labs, Google, and Microsoft Research. Now I work at Google in Mountain View, CA.

Projects

project icon Predicting Spread of Disease from Social Media

Given that three of your friends have flu-like symptoms, and that you have recently met eight people, possibly strangers, who complained about having runny noses and headaches, what is the probability that you will soon become ill as well? Our models enable you to see the spread of infectious diseases, such as flu, throughout a real-life population observed through online social media.

We apply machine learning and natural language understanding techniques to determine the health state of Twitter users at any given time. Since a large fraction of tweets is geo-tagged, we can plot them on a map, and observe how sick and healthy people interact. Our model then predicts if and when an individual will fall ill with high accuracy, thereby improving our understanding of the emergence of global epidemics from people's day-to-day interactions.

This video interview provides a quick overview of our work in the health space:


The following video shows a heatmap visualization of the prevalence of flu in New York City, as observed through public Twitter data. The more red an area is, the more people are afflicted by flu at that location. We show emergent aggregate patterns in real-time, with second-by-second resolution. By contrast, previous state-of-the-art methods (including Google Flu Trends and government data) entail time lags from days to weeks.


You can explore health patterns with our web application at GermTracker.

The fine-grained epidemiological models we show here are just one instance of the general class of problems that our system solves. Other domains include understanding public sentiment, the diffusion of information throughout a population, and predicting customer behavior.

By augmenting existing datasets with real-time insights and cues from social media, we are able to connect the dots, visualize patterns, and refine models based on user feedback.


project icon Learning about Human Behavior from Social Media

project icon Location-Based Modeling of Complex Multi-Agent Activities


Publications