Henry Kautz

Henry Kautz
University of Rochester | UR · Department of Computer Science

PhD

About

271
Publications
37,217
Reads
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22,312
Citations
Introduction
Henry Kautz is the Robin & Tim Wentworth Director of the Goergen Institute for Data Science and Professor in the Department of Computer Science at the University of Rochester. He has served as department head at AT&T Bell Labs in Murray Hill, NJ, and as a full professor at the University of Washington, Seattle. In 2010 he was elected President of the Association for Advancement of Artificial Intelligence (AAAI).

Publications

Publications (271)
Article
Full-text available
Given the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. While the accessibility of location-enabled devices as well as large-scale datasets of human activities, has fueled significant advances in our understanding, there is litt...
Article
Full-text available
Objective We developed a digital scribe for automatic medical documentation by utilizing elements of patient-centered communication. Excessive time spent on medical documentation may contribute to physician burnout. Patient-centered communication may improve patient satisfaction, reduce malpractice rates, and decrease diagnostic testing expenses. W...
Preprint
Given the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. While the accessibility of location-enabled devices as well as large-scale datasets of human activities, has fueled significant advances in our understanding, there is litt...
Article
Intimate partner violence is a public health problem with increasing prevalence and harmful influence to both individuals and society. Automated screening for intimate partner violence is still an unsolved problem in academic research and practical applications. Current detection methods use self-reporting scales and in-person interviews, which are...
Article
Background Depression and anxiety disorders among the global population have worsened during the COVID-19 pandemic. Yet, current methods for screening these two issues rely on in-person interviews, which can be expensive, time-consuming, and blocked by social stigma and quarantines. Meanwhile, how individuals engage with online platforms such as Go...
Preprint
Depressive disorder is one of the most prevalent mental illnesses among the global population. However, traditional screening methods require exacting in-person interviews and may fail to provide immediate interventions. In this work, we leverage ubiquitous personal longitudinal Google Search and YouTube engagement logs to detect individuals with d...
Preprint
BACKGROUND Mental health problems among the global population are worsened during the coronavirus disease (COVID-19). Yet, current methods for screening mental health issues rely on in-person interviews, which can be expensive, time-consuming, blocked by social stigmas and quarantines. Meanwhile, how individuals engage with online platforms such as...
Preprint
Background Mental health problems among the global population are worsened during the coronavirus disease (COVID-19). Yet, current methods for screening mental health issues rely on in-person interviews, which can be expensive, time-consuming, blocked by social stigmas and quarantines. Meanwhile, how individuals engage with online platforms such as...
Preprint
Full-text available
This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of...
Preprint
Anxiety disorder is one of the most prevalent mental health conditions, arising from complex interactions of biological and environmental factors and severely interfering one's ability to lead normal life activities. Current methods for detecting anxiety heavily rely on in-person interviews. Yet, such mental health assessments and surveys can be ex...
Article
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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 peo...
Preprint
Full-text available
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 peo...
Preprint
Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where they rate the funniness of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding and educational, keeping play...
Article
Background: The identification of surgical site infections for infection surveillance in hospitals depends on the manual abstraction of medical records and, for research purposes, depends mainly on the use of administrative or claims data. The objective of this study was to determine whether automating the abstraction process with natural language...
Conference Paper
Online behavior leaves a digital footprint that can be analyzed to reveal our cognitive and psychological state through time. Recognizing these subtle cues can help identify different aspects of mental health, such as low self-esteem, depression, and anxiety. Google's web search engine, used daily by millions of people, logs every search query made...
Preprint
Online news has made dissemination of information a faster and more efficient process. Additionally, the shift from a print medium to an online interface has enabled user interactions, creating a space to mutually understand the reader responses generated by the consumption of news articles. Intermittently, the positive environment is transformed i...
Conference Paper
Full-text available
Is there a relationship between urban neighborhood safety and helpful or supportive user networks on Twitter? An interdisciplinary, community-partnered team analyzed one year (2013-2014) of geo-tagged tweets from a 16-county region to generate a network of users who expressed gratefulness to one another. Call counts to 911 (2013-2015) around locati...
Article
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OBJECTIVES/SPECIFIC AIMS: This study sought to develop a mHealth application which was capable of predicting the spread of infectious diseases during the height of the Ebola outbreak in Lagos, Nigeria. Following the success of this primary task, the research then sought to understand behavioral health issues which are indicative of chronic diseases...
Article
Full-text available
Foodborne illness afflicts 48 million people annually in the US alone. More than 128,000 are hospitalized and 3000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitc...
Article
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Background Equipping members of a target population to deliver effective public health messaging to peers is an established approach in health promotion. The Sources of Strength program has demonstrated the promise of this approach for “upstream” youth suicide prevention. Text messaging is a well-established medium for promoting behavior change and...
Article
Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous (and expensive) Global Positioning System (GPS) data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointing where people live at scale. We revisit this resear...
Article
Nearly all previous work on geo-locating latent states and activities from social media confounds general discussions about activities, self-reports of users participating in those activities at times in the past or future, and self-reports made at the immediate time and place the activity occurs. Activities, such as alcohol consumption, may occur...
Article
Lifestyles are a valuable model for understanding individuals' physical and mental lives, comparing social groups, and making recommendations for improving people's lives. In this paper, we examine and compare lifestyle behaviors of people living in cities of different sizes, utilizing freely available social media data as a large-scale, low-cost a...
Article
Full-text available
Working adults spend nearly one third of their daily time at their jobs. In this paper, we study job-related social media discourse from a community of users. We use both crowdsourcing and local expertise to train a classifier to detect job-related messages on Twitter. Additionally, we analyze the linguistic differences in a job-related corpus of t...
Conference Paper
We present a real-time interface which allows for contact tracing using ubiquitous sensors present in the Node smartphone application on Android phones. The initial application seeks to aid in the prevention of infectious diseases in Lagos, Nigeria through installing the application on up to 100 smartphones. In our demo we demonstrate how anonymous...
Conference Paper
Full-text available
We address the problem of automatically aligning natural language sentences with corresponding video segments without any direct supervision. Most existing algorithms for integrating language with videos rely on hand-aligned parallel data, where each natural language sentence is manually aligned with its corresponding image or video segment. Recent...
Article
Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide. With the widespread adoption of social media and mobile devices, and rapid advances in artificial intelligence, a unique opportunity arises for tackling mental health problems. In this study, we investigate...
Article
Precise home location detection has been actively studied in the past few years. It is indispensable in the researching fields such as personalized marketing and disease propagation. Since the last few decades, the rapid growth of geotagged multimedia database from online social networks provides a valuable opportunity to predict people's home loca...
Conference Paper
Full-text available
We propose an unsupervised learning algorithm for automatically inferring the mappings between English nouns and corresponding video objects. Given a sequence of natural language instructions and an un-aligned video recording, we simultaneously align each instruction to its corresponding video segment, and also align nouns in each instruction to th...
Article
Given the pace of discovery in medicine, accessing the literature to make informed decisions at the point of care has become increasingly difficult. Although the Internet creates unprecedented access to information, gaps in the medical literature and inefficient searches often leave healthcare providers' questions unanswered. Advances in social com...
Article
Full-text available
We present a formalization of the Virtual Trans- portation Company (VTC) problem and study its structure and computational complexity, focusing on the job allocation component. We propose two different notions of fairness for job allocation. The problem domain has a rich underlying structure with complexity properties ranging from polynomi- ally so...
Conference Paper
We present a general framework for complex event recognition that is well-suited for integrating information that varies widely in detail and granularity. Consider the scenario of an agent in an instrumented space performing a complex task while describing what he is doing in a natural manner. The system takes in a variety of information, including...
Article
We propose modal Markov logic as an extension of propositional Markov logic to reason under the principle of maximum entropy for modal logics K45, KD45, and S5. Analogous to propositional Markov logic, the knowledge base consists of weighted formulas, whose weights are learned from data. However, in contrast to Markov logic, in our framework we use...
Article
Purpose: We investigated the current use of off-the-shelf cognitive support technologies (CSTs) by individuals with traumatic brain injury (TBI), the challenges they and their caregivers face when using these technologies, the functional areas where support is needed, and their current experience in learning new technologies. Method: We conducte...
Conference Paper
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 l...
Conference Paper
Monitoring and forecast of global spread of infectious diseases is difficult, mainly due to lack of fine-grained 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 contribut...
Conference Paper
Systems that automatically recognize human activities offer the potential of timely, task-relevant information and support. For example, prompting systems can help keep people with cognitive disabilities on track and surveillance systems can warn of activities of concern. Current automatic systems are difficult to deploy because they cannot identif...
Conference Paper
Research in computational epidemiology to date has concentrated on estimating summary statistics of populations and simulated scenarios of disease outbreaks. Detailed studies have been limited to small domains, as scaling the methods involved poses considerable challenges. By contrast, we model the associations of a large collection of social and e...
Article
Full-text available
We are developing a general framework for using learned Bayesian models for decision-theoretic control of search and reasoningalgorithms. We illustrate the approach on the specific task of controlling both general and domain-specific solvers on a hard class of structured constraint satisfaction problems. A successful strategyfor reducing the high (...
Article
Modal Markov Logic for a single agent has previously been proposed as an extension to propositional Markov logic. While the framework allowed reasoning under the principle of maximum entropy for various modal logics, it is not feasible to apply its counting based inference to reason about the beliefs and knowledge of multiple agents due to magnitud...
Article
We present a robust framework for complex event recognition that is well-suited for integrating information that varies widely in detail and granularity. Consider the scenario of an agent in an instrumented space performing a complex task while describing what he is doing in a natural manner. The system takes in a variety of information, including...
Article
The improvement of energy efficiency in our society has become an urgent issue for sustainability under global warming. The authors present research issues on sensor-based smart environments for energyaware intelligence, and showcase a study of algorithms for monitoring human activities that provides the context awareness to the smart environments....
Conference Paper
Full-text available
Markov logic is a rich language that allows one to specify a knowledge base as a set of weighted first-order logic formulas, and to define a probability distribution over truth assignments to ground atoms using this knowledge base. Usually, the weight of a formula cannot be related to the probability of the formula without taking into account the w...
Article
Full-text available
This paper presents a model of interactive activity recognition and prompting for use in an assistive system for persons with cognitive disabilities. The system can determine the user's state by interpreting sensor data and/or by explicitly querying the user, and can prompt the user to begin, resume, or end tasks. The objective of the system is to...
Conference Paper
Full-text available
Location plays an essential role in our lives, bridging our online and offline worlds. This paper explores the interplay between people's location, interactions, and their 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 netw...
Article
Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individu-als or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted ex...
Article
Full-text available
Research in computational epidemiology to date has concentrated on coarse-grained statistical analysis of populations, often synthetic ones. By contrast, this paper focuses on fine-grained modeling of the spread of infectious diseases throughout a large real-world social network. Specifically, we study the roles that social ties and interactions be...
Article
Full-text available
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 fine-grained prediction of the health of specific people from...
Article
We are developing a testbed for learning by demonstration combining spoken language and sensor data in a natural real-world environment. Microsoft Kinect RGB-Depth cameras allow us to infer high-level visual features, such as the relative position of objects in space, with greater precision and less training than required by traditional systems. Sp...
Chapter
Full-text available
This chapter takes on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain that involves many distinct cooperative and competitive joint activities. We mod...
Chapter
The improvement of energy efficiency in our society has become an urgent issue for sustainability under global warming. The authors present research issues on sensor-based smart environments for energy-aware intelligence, and showcase a study of algorithms for monitoring human activities that provides the context awareness to the smart environments...
Conference Paper
Many real world problems can be modeled using a combination of hard and soft constraints. Markov Logic is a highly expressive language which represents the underlying constraints by attaching realvalued weights to formulas in first order logic. The weight of a formula represents the strength of the corresponding constraint. Hard constraints are rep...
Conference Paper
www.brainaid.com We present a model of interactive activity recognition and prompting for use in an assistive system for persons with cognitive disabilities. The system can determine the user’s state by interpreting sensor data and/or by explicitly querying the user, and can prompt the user to begin or end tasks. The objective of the system is to h...
Article
Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of in-dividuals. We, in contrast, take on the task of understand-ing human interactions, attempted interactions, and int...
Article
AAAI’s leadership underwent a major change in March of this year. Martha Pollack, who had been serving as AAAI president since July 2009 resigned her position, and Henry Kautz, who had been serving as AAAI president-elect assumed the duties and responsibilities of the president. As stipu- lated in the AAAI bylaws, Kautz will serve in this capacity...
Article
Full-text available
A special track on directions in artificial intelligence at a Microsoft Research Faculty Summit included a panel discussion on key challenges and opportunities ahead in AI theory and practice. This article captures the conversation among eight leading researchers.
Conference Paper
Full-text available
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 mod- eling 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 recognizin...
Article
The papers in this proceedings present the latest advances in the field of automated planning and scheduling The accepted papers cover the entire range of planning and scheduling and involve 22 specific research fields, with an emphasis in planning and scheduling under uncertainty, search algorithms, domain independent classical planning, robot pla...
Conference Paper
We present an activity recognition feature inspired by human psychophysical performance. This feature is based on the velocity history of tracked keypoints. We present a generative mixture model for video sequences using this feature, and show that it performs comparably to local spatio-temporal features on the KTH activity recognition dataset. In...
Conference Paper
Full-text available
Individuals with cognitive impairments are often prevented from independently living, working, and fully participating in their community due to wayfinding concerns. We conducted two user studies of a mobile wayfinding aid designed to support such individuals. The first study examined usability issues related to wayfinding outdoors. The results wer...
Article
Full-text available
Most image annotation systems consider a single photo at a time and label photos individually. In this work, we focus on collections of personal photos and exploit the contextual information naturally implied by the associated GPS and time metadata. First, we employ a constrained clustering method to partition a photo collection into event-based su...
Conference Paper
Full-text available
Individuals with cognitive impairments would prefer to live independently, however issues in wayfinding prevent many from fully living, working, and participating in their com­ munity. Our research has focused on designing, prototyp­ ing, and evaluating a mobile wayfinding system to aid such individuals. Building on the feedback gathered from po­ t...
Article
Research on incomplete algorithms for satisfiability testing lead to some of the first scalable SAT solvers in the early 1990's. Unlike systematic solvers often based on an exhaustive branching and backtracking search, incomplete methods are generally based on stochastic local search. On problems from a variety of domains, such incomplete methods f...