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Comfortable Computing, Communal Computing, Content Consumption and the Creativity of Curation & Collage

Abstract

The last two years have seen an explosion of growth in tablet computing. In this talk I discuss a qualitative interview-based study of 22 tablet owners living in the San Francisco Bay Area in which we tried to understand the role of tablet computing in their everyday lives. I use findings from this study to explore three key areas for research in computing: comfortable computing, recognizing and exploring the impact of computing environment on usage and vice versa; communal computing, the role of computing technologies and practices within the family; and content consumption, exploring the changing nature of creativity in a keyboard-less world.

About Mr. Kaye

His research explores the social, cultural, and Technological effects of technology on people, and how people impact those technologies.  These studies have recently included studies of families’ values and technology choices, visualizations of Twitter and publications, and the use of NFC-enabled phones to help track clean water supplies in Haiti.  His previous work has included ethnographic, cultural, critical and technological studies of grassroots creative leisure practices such as hacking and tinkering, academics' archiving practices, couples in long distance relationships, the role of women in computing, computerized smell output, and smart homes and kitchens.  He has a Ph.D in Information Science from Cornell, a Master’s degree in Media Arts & Sciences and a B.S. in Cognitive Science, both from MIT, and is a Consulting Assistant Professor at Stanford.

BrainGrid: Building a GPU Infrastructure for Large Neural Simulations (Faculty Research Talk)

Abstract

The advent of relatively inexpensive graphics processing units (GPUs) that are capable of general-purpose computing has brought supercomputer-level performance to the desktop. However, taking advantage of this hardware to achieve significant speedup is a non-trivial exercise, with typical naive "porting" of software often yielding only a 2-4 times speedup.

In this talk, I review work in the UWB Biocomputing Laboratory on large-scale, long-duration neural simulation that has achieved over 20 times speedup for these demanding applications. I then outline the next stage of our work: developing reusable tools to help biologists and computational scientists move their projects to GPUs.

About Dr. Stiber

Dr. Stiber received a BS in Computer Science and a BS in Electrical Engineering from Washington University, Saint Louis, in 1983, and his MS and PhD in Computer Science from the University of California, Los Angeles, where he was a Research and a Teaching Assistant. He has held positions with Texas Instruments (Dallas, Texas), Philips (Eindhoven, Netherlands), and the IBM Los Angeles Scientific Center. He was an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science & Technology during 1992-96 and a Research Assistant Professor in the Department of Molecular and Cell Biology at the University of California, Berkeley in 1996-97. Dr. Stiber is a frequent visitor to the Department of Biophysical Engineering at Osaka University (Japan). His research interests include: scientific data management and visualization, computational neuroscience, biocomputing, neuroinformatics, simulation, scientific computing, neural networks, autonomous systems, computer graphics, computer vision, nonlinear dynamics, and complex systems.

Dr. Stiber is on the executive committee of the Seattle chapter of the IEEE Computer Society, has served on organizing committees, chaired sessions, and reviewed papers for neural network and computational neuroscience conferences, and is a reviewer for Physica D, The Journal of Computational Neuroscience, and the Bulletin of Mathematical Biology.

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The Netflix Prize: How a public contest and dedicated amateurs changed the science of recommender systems.

Abstract

The Netflix Prize was the most widely followed machine learning competition in history. It offered a prize of $1,000,000 to anyone who could build a recommender system with an error rate at least 10% better than Netflix's internal system. Thousands of teams took part, and the contest lasted nearly three years. The speaker, a long-time active participant, will give an insider's perspective on two stories:

1. The machine learning approaches used during the competition, and how they substantially advanced the science of recommender systems, and
2. The unexpected emergence of two teams with winning scores, and their wild race to the finish.

About Dr. Howbert

Jeff Howbert received a BA in English from Stanford Univ. in 1977 and a PhD in Synthetic Organic Chemistry from Harvard Univ. in 1983. Over the ensuing 25 years, he led medicinal chemistry and drug discovery efforts at a large pharmaceutical company and several small biotech companies. He holds 41 US patents and is responsible for the entry of 6 compounds into clinical development. After earning a MS in Computer Science from Univ. of Washington in 2008, he began a second career in computational biology, with an emphasis on machine learning. He presently works in several labs on building predictive models for diverse biomedical problems, including seizure risk, cardiovascular biomarker discovery, and proteomic analysis. He is also currently teaching a course at UW Bothell on machine learning.

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Traceability and Provenance: Establishing Connections (Faculty Research Talk)

Abstract

Related information is often found in different locations and represented in different formats. Explicitly connecting these related information (referred as traceability or provenance) is useful in different domains since doing so provides an individual with information to support a particular task. For instance, in software engineering, relating the design to requirements aids in ensuring that the system to be developed meets customer requirements. In e-Science, identifying the relationships between intermediate data sets supports the repeatability of experiments. This talk will cover current projects within these two threads of research.

About Dr. Asuncion

Dr. Asuncion received her Ph.D. in computer science from the University of California, Irvine, in 2009. Prior to coming to UW Bothell, she was a Postdoctoral Researcher in the Institute for Software Research at the University of California, Irvine. She has also worked in industry in a variety of roles: as a software engineer at Unisys Corporation and as a traceability engineer at Wonderware Corporation where she designed a successful in-house traceability system.

Her research emphasis is on traceability and she has developed a novel software traceability approach that automatically links distributed and heterogeneous information. She has investigated the tracing of software license conflicts in heterogeneously composed software systems. Dr. Asuncion is also interested in investigating the traceability challenges in other domains such as e-Science and health care.

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Petabytes and Terawatts

Title

Petabytes and Terawatts: An overview of Hadoop and its ecosystem for processing today's big data needs

Abstract

Apache Hadoop has become the standard for processing previously unimaginable amounts of data on cheap, accessible hardware. This survey will cover what Hadoop is, how it works, the plethora of projects that have grown up around it, and how you too can get involved.

About Jakob

Jakob received a B.S. in Computing and Software Systems at the University of Washington Bothell. He is currently employed at LinkedIn working on integrating Hadoop into LinkedIn's data analysis pipeline.

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The University of Washington is committed to providing equal opportunity and reasonable accommodation in its services, programs, activities, education and employment for individuals with disabilities. To inquire about disability accommodations, please contact Rosa Lundborg at Disability Support Services at least ten days prior to the event at 425.352.5307, TDD 425.352.5303, FAX 425.352.5455, or email dss@uwb.edu.