Arrow Research search

Author name cluster

Adam Sadilek

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers
1 author row

Possible papers

9

AAAI Conference 2022 Conference Paper

CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting

  • Lijing Wang
  • Aniruddha Adiga
  • Jiangzhuo Chen
  • Adam Sadilek
  • Srinivasan Venkatramanan
  • Madhav Marathe

Infectious disease forecasting has been a key focus in the recent past owing to the COVID-19 pandemic and has proved to be an important tool in controlling the pandemic. With the advent of reliable spatiotemporal data, graph neural network models have been able to successfully model the interrelation between the cross-region signals to produce quality forecasts, but like most deep-learning models they do not explicitly incorporate the underlying causal mechanisms. In this work, we employ a causal mechanistic model to guide the learning of the graph embeddings and propose a novel learning framework – Causal-based Graph Neural Network (CausalGNN) that learns spatiotemporal embedding in a latent space where graph input features and epidemiological context are combined via a mutually learning mechanism using graph-based non-linear transformations. We design an attention-based dynamic GNN module to capture spatial and temporal disease dynamics. A causal module is added to the framework to provide epidemiological context for node embedding via ordinary differential equations. Extensive experiments on forecasting daily new cases of COVID-19 at global, US state, and US county levels show that the proposed method outperforms a broad range of baselines. The learned model which incorporates epidemiological context organizes the embedding in an efficient way by keeping the parameter size small leading to robust and accurate forecasting performance across various datasets.

PDF Details

AAAI Conference 2020 Conference Paper

Inferring Nighttime Satellite Imagery from Human Mobility

  • Brian Dickinson
  • Gourab Ghoshal
  • Xerxes Dotiwalla
  • Adam Sadilek
  • Henry Kautz

Nighttime lights satellite imagery has been used for decades as a uniform, global source of data for studying a wide range of socioeconomic factors. Recently, another more terrestrial source is producing data with similarly uniform global coverage: anonymous and aggregated smart phone location. This data, which measures the movement patterns of people and populations rather than the light they produce, could prove just as valuable in decades to come. In fact, since human mobility is far more directly related to the socioeconomic variables being predicted, it has an even greater potential. Additionally, since cell phone locations can be aggregated in real time while preserving individual user privacy, it will be possible to conduct studies that would previously have been impossible because they require data from the present. Of course, it will take quite some time to establish the new techniques necessary to apply human mobility data to problems traditionally studied with satellite imagery and to conceptualize and develop new real time applications. In this study we demonstrate that it is possible to accelerate this process by inferring artificial nighttime satellite imagery from human mobility data, while maintaining a strong differential privacy guarantee. We also show that these artificial maps can be used to infer socioeconomic variables, often with greater accuracy than using actual satellite imagery. Along the way, we find that the relationship between mobility and light emissions is both nonlinear and varies considerably around the globe. Finally, we show that models based on human mobility can significantly improve our understanding of society at a global scale.

PDF Details

IJCAI Conference 2013 Conference Paper

Modeling the Interplay of People's Location, Interactions, and Social Ties

  • Adam Sadilek
  • Henry Kautz
  • Jeffrey P. Bigham

Location plays an essential role in our lives, bridging our online and offline worlds. This paper explores the interplay of people’s location, interactions, and social ties within a large real-world dataset. We present and evaluate Flap, a system that solves two intimately related tasks: link and location prediction in online social networks. For link prediction, Flap infers social ties by considering patterns in friendship formation, the content of people’s messages, and user location. We show that while each component is a weak predictor of friendship alone, combining them results in a strong model—accurately identifying the majority of friendships. For location prediction, Flap implements a scalable probabilistic model of human mobility, where we treat users with known GPS positions as noisy sensors of the location of their friends. We explore supervised and unsupervised learning scenarios, and focus on the efficiency of both learning and inference. We evaluate Flap on a large sample of highly active users from two distinct geographical areas and show that it (1) reconstructs the entire friendship graph with high accuracy even when no edges are given; and (2) infers people’s fine-grained location, even when they keep their data private and we can only access the location of their friends. Our models significantly outperform current approaches to either task.

PDF Details DOI

IJCAI Conference 2013 Conference Paper

Towards Understanding Global Spread of Disease from Everyday Interpersonal Interactions

  • Sean Brennan
  • Adam Sadilek
  • Henry Kautz

Monitoring and forecast of global spread of infectious diseases is difficult, mainly due to lack of finegrained and timely data. Previous work in computational epidemiology has shown that mining data from the web can improve the predictability of high-level aggregate patterns of epidemics. By contrast, this paper explores how individuals contribute to the global spread of disease. We consider the important task of predicting the prevalence of flulike illness in a given city based on interpersonal interactions of the city’s residents with the outside world. We use the geo-tagged status updates of traveling Twitter users to infer properties of the flow of individuals between cities. While previous research considered only the raw volume of passengers, we estimate a number of latent variables, including the number of sick (symptomatic) travelers and the number of sick individuals to whom each traveler was exposed. We show that AI techniques provide insights into the mechanisms of disease spread and significantly improve predictability of future flu outbreaks. Our experiments involve over 51, 000 individuals traveling between 75 cities prior and during a severe ongoing flu epidemic (October 2012 - January 2013). Our model leverages the text and interpersonal interactions recorded in over 6. 5 million online status updates without any active user participation, enabling scalable public health applications.

PDF Details DOI

IJCAI Conference 2013 Conference Paper

Towards Understanding Global Spread of Disease from Everyday Interpersonal Interactions

  • Sean Brennan
  • Adam Sadilek
  • Henry Kautz

This paper proposes a new algorithm to compute the resilience of a social system or an ecosystem when it is defined in the framework of the mathematical viability theory. It is applied to the problem of language coexistence: Although bilingual societies do exist, many languages have disappeared and some seem endangered presently. Mathematical models of language competition generally conclude that one language will disappear, except when the relative prestige of the languages can be modified. The viability theory provides concepts and tools that are suitable to study the resilience, but with severe computational limits since it uses extensive search on regular grids. The method we propose considers the computation of the viability output sets as an active learning problem with the objective of restraining the number of calls to the model and information storage. We adapt a kd-tree algorithm to approximate the level sets of the resilience value. We prove that this algorithm converges to the output sets defined by the viability theory (viability kernel and capture basin). The resilience value we compute can then be used to propose a policy of action in risky situations such as migration flows.

PDF Details DOI

AAAI Conference 2012 Conference Paper

Far Out: Predicting Long-Term Human Mobility

  • Adam Sadilek
  • John Krumm

Much work has been done on predicting where is one going to be in the immediate future, typically within the next hour. By contrast, we address the open problem of predicting human mobility far into the future, a scale of months and years. We propose an efficient nonparametric method that extracts significant and robust patterns in location data, learns their associations with contextual features (such as day of week), and subsequently leverages this information to predict the most likely location at any given time in the future. The entire process is formulated in a principled way as an eigendecomposition problem. Evaluation on a massive dataset with more than 32, 000 days worth of GPS data across 703 diverse subjects shows that our model predicts the correct location with high accuracy, even years into the future. This result opens a number of interesting avenues for future research and applications.

PDF Details

AAAI Conference 2012 Conference Paper

Predicting Disease Transmission from Geo-Tagged Micro-Blog Data

  • Adam Sadilek
  • Henry Kautz
  • Vincent Silenzio

Researchers have begun to mine social network data in order to predict a variety of social, economic, and health related phenomena. While previous work has focused on predicting aggregate properties, such as the prevalence of seasonal influenza in a given country, we consider the task of finegrained prediction of the health of specific people from noisy and incomplete data. We construct a probabilistic model that can predict if and when an individual will fall ill with high precision and good recall on the basis of his social ties and co-locations with other people, as revealed by their Twitter posts. Our model is highly scalable and can be used to predict general dynamic properties of individuals in large realworld social networks. These results provide a foundation for research on fundamental questions of public health, including the identification of non-cooperative disease carriers (“Typhoid Marys”), adaptive vaccination policies, and our understanding of the emergence of global epidemics from day-today interpersonal interactions.

PDF Details

AAAI Conference 2010 Conference Paper

Recognizing Multi-Agent Activities from GPS Data

  • Adam Sadilek
  • Henry Kautz

Recent research has shown that surprisingly rich models of human behavior can be learned from GPS (positional) data. However, most research to date has concentrated on modeling single individuals or aggregate statistical properties of groups of people. Given noisy real-world GPS data, we—in contrast—consider the problem of modeling and recognizing activities that involve multiple related individuals playing a variety of roles. Our test domain is the game of capture the flag—an outdoor game that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as capturing a player. Our model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. We compare our unified approach with three alternatives (both probabilistic and nonprobabilistic) where either the denoising of the GPS data and the detection of the high-level activities are strictly separated, or the states of the players are not considered, or both. We show that the unified approach with the time window spanning the entire game, although more computationally costly, is significantly more accurate.

PDF Details
v2026.09.13