“A Report from an Online Course on Global Disruption and Information Technology” accepted for publication in LIMITS 2016 Bill Tomlinson, Bonnie Nardi and I piloted an online undergraduate course with UC Irvine centered on the idea of “Global Disruption and Information Technology.” We wanted to give students a framework to think about climate change, peak…
In 2010 Haiti experienced a catastrophic earthquake that destroyed a substantial amount of infrastructure in the capital of Port-au- Prince. Limited national resources and widespread poverty have made the rebuilding slow and piecemeal. Five years later that infrastructure is still unevenly repaired and maintained. Nevertheless, the Haitian people have, by necessity, continued to adapt in…
The proliferation of sensors in the world has created increased opportunities for context-aware applications. However, it is often cumbersome to capitalize on these opportunities due to the difficulties inherent in collecting, fusing, and reasoning with data from a heterogeneous set of distributed sensors. The fabric that connects sensors lacks resilience and fault tolerance in the…
Computer Science, as a single discipline, can no longer speak to the broad relevance of digital technologies in society. The Department of Informatics in the Donald Bren School of Information and Computer Sciences at the University of California, Irvine, serves as the institutional home for research on relationships between technological, organizational, and social aspects of…
We describe the design of the NomaticBubbles, a visualization that provides cues of communal whereabouts. Unlike most location displays showing whereabouts on a geographical map, the NomaticBubbles depicts historical and aggregate traces of participants’ whereabouts in an abstract and ambiguous manner. We describe the design of the NomaticBubbles, and discuss some early experiences and feedback…
In this paper we discuss a system that can learn personal maps customized for each user and infer his daily activities and movements from raw GPS data. The system uses discriminative and generative models for different parts of this task. A discriminative relational Markov network is used to extract significant places and label them; a…
Many recent studies have underscored the applicability to healthcare of a system able to observe and understand day-to-day human activities. The Guide project is aimed at building just such a system. The project combines novel sensing technology, expressive but scalable learners and unsupervised mining of activity models from the web to address the problem. An…
One the best qualitative and quantitative tools that elder–care specialists have to monitor the health of elderly individuals is Activity of Daily Living (ADL) tracking [1,2]. By watching the frequency and competency with which an individual can cook, clean the house, engage in socializing, etc, short– and long– term changes in health can be identified.…
This paper presents an overview of the research on learning statistical models from relational data being carried out at the University of Washington. Our work falls into five main directions: learning models of social networks; learning models of sequential relational processes; scaling up statistical relational learning to massive data sources; learning for knowledge integration;…
Assisted Cognition systems provide active cognitive aids for people with reduced memory and problem-solving abilities due to Alzheimer’s Disease or other disorders. Two concrete examples of the Assisted Cognition systems we are developing are an ACTIVITY COMPASS that helps reduce spatial disorientation both inside and outside the home, and an ADL PROMPTER that helps patients…
In this paper, we introduce the Activity Compass, a cognitive aid for early-stage Alzheimer’s patients. This device has a simple user interface based on the metaphor of a traditional navigation compass. By following an arrow and an icon, users who are disoriented or forgetful are assisted in reaching their destination. A server-based AI engine learns…
Stochastic search algorithms have proven to be very fast at solving many satisfiability problems [2,3,8]. The nature of their search requires careful parameter tuning to maximize performance, but depending on the problem and the details of the stochastic algorithm, the correct tuning may be difficult to ascertain [9]. In this paper we introduce Auto-Walksat, a…