Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.Comment: 27 pages, 8 figures, 1 tabl

The Role of iSchools in Medical Informatics

Similar to iSchools, healthcare is an interdisciplinary field that encompasses heterogeneous environments (e.g., hospitals, clinics, home) and perspectives. However, medical informatics research focuses mainly on technical solutions to narrow problems. Often, this leads to the design of systems that solve one problem but do not fit the general work practices in a setting. In response, there is a growing movement in the community to start investigating organizational and social issues that impact the design, implementation, and use of information technologies (Reddy and Bradner 2005; Kaplan and Shaw 2004). This trend will only increase as healthcare documentation in the US increasingly moves from paper-based to electronic systems. iSchools are uniquely positioned to benefit the medical informatics community due to our interdisciplinary foundation. Taking the theoretical paradigms and methods from various fields will allow us to address the interplay between people, healthcare technologies, and healthcare organizations. Some areas could include: ??? Design and evaluation mechanisms to account for the ways different stakeholders interact with systems. ??? Workflow analysis to understand how information is used for various activities in preparation for a systems implementation. ??? Developing and implementing systems that deliver the right information to the right people at the right time. iSchool research can also help expand the focus of the medical informatics community to examine other important issues such as the digital divide in healthcare and protecting patient privacy. By working together, the iSchools can train the next generation of researchers providing needed interdisciplinary insight into medical informatics issues and extend into new territories. Healthcare is characterized by multiple stakeholders and teams that are separated by time and space that are information-dependent; therefore, iSchool research in this field can also impact research in related areas. Besides being a rich setting for conducting research, healthcare impacts our lives on a daily basis. The hosts of the roundtable will discuss how iSchools can get involved to incorporate more interdisciplinary research in the medical informatics area. In particular, we will focus on the following questions: 1. What are the interesting medical informatics research problems from an iSchool perspective? 2. How can iSchool research impact the medical informatics field? 3. What are the challenges iSchools face in this field

iSchool Health and Medical Research Initiatives and Opportunities

The Annual iConference offers an important opportunity for information sharing among member iSchools. Proposed for iConference 09 is a Roundtable Discussion that provides a forum for information sharing about existing and planned iSchool initiatives, and discussion of opportunities related to Health and Medical Research. Because of the transdisciplinary scope of research problems and practical demands in relation to Health and Medical Research, initiatives in these areas offer significant opportunities for cross-disciplinary research collaboration and interdisciplinary partnership with other units in the development of professional training programs

Artificial intelligence in the work context

Artificial intelligence (AI) reconfigures work and organization, while work and organization shape AI. In this special issue, we explore these mutual transformations and how they play out across industries and occupations. We argue that, to truly appreciate this transformative power, the use of AI should be understood in relation to key dimensions of the work context. In this editorial, we discuss the sociotechnical dynamics of AI implementation, the research landscape of AI in the context of work, and key contextual factors on the macro- and micro-level that help understand the AI-work nexus. We then provide directions for future research at the intersection of work and AI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175924/1/asi24730_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175924/2/asi24730.pd