Brian Fisher: A psychologist with a Erdos number of 2, Brian is a Fellow of the Psychonomics Society, member of the VAST steering committee, VIS Executive Committee, and Visualization Pioneers group. By day he is Associate Professor in the School of Interactive Arts and Technology and Program in Cognitive Science at Simon Fraser University. At the University of British Columbia he is Associate Director of the Media And Graphics Interdisciplinary Centre, and member of the Brain Research Centre and Institute for Computing, Information and Cognitive Systems. His research includes laboratory studies of human cognitive architecture and individ- ual differences, and applications in emergency management, aircraft safety, and public health. In collaboration with VIS colleagues he has presented symposia on visual analytics to the Cognitive Science Society, Association for Psychological Science, and the Psychonomics society with the goal of building more effective interdisciplinary research in visual information systems and their use in analysis, collaboration, and interpersonal communication. He was keynote speaker for EuroVAST, the NATO Visualisation Network of Experts, and the Scalable VA DFG Priority Program meeting.
Here are current projects, there are others that are too early to add and some number of completed ones that are probably not of interest.
VA for public health
Canadian Environmental Health Atlas: This project combines health sciences, visual analytics, computer sciences, communication and the environment. Chronic disease has surpassed communicable disease as the leading cause of death globally. The scientific community increasingly recognizes that environmental influences are the major risk factors for chronic disease and premature death, but most people do not appreciate that translating this rapidly emerging science has the potential to dramatically reduce health care costs, prevent disease and disability, and enhance the health of Canadians and the rest of the world. We use visual analytics, a translational approach to the development of information systems to combine the cognitive science of human decision-making with the development of highly interactive visualization systems to support information processing in public health. These tools will enable a diversity of stakeholders to understand and evaluate the implication of environmental factors on health, and make informed individual, governmental, and commercial decisions to improve public health.
BC Child Injury Research And Prevention Network: This project is sponsored by the Canadian Institute for Health Research (CIHR) and the Public Health Agency of Canada (PHAC) grants held by the British Columbia Injury Research and Prevention Unit (BCIRPU) at the BC Children’s Hospital. Injuries kill more Canadian children and youth aged 14 and under than all diseases combined. Injury constitutes the leading cause of death among children and youth in Canada and the leading cause of hospitalization among 10 to 14 year olds. We provide injury researchers, epidemiologists, injury prevention practitioners and policy makers with an interactive visualization system to help stakeholders explore heterogeneous and complex injury datasets, synthesize critical information and develop fundamental policies and programs to strengthen the Child and Youth injury surveillance, research and prevention.
VA for Aircraft Safety & Reliability: This project is conducted in partnership with The Boeing Company, Aeroinfo, and MITACS. Our “translational science” approach begins with work onsite at a company or agency to understand and support analysis and decision making in that organization through enhanced information technology. In many cases a barrier to effective implementation of new technology is a lack of knowledge of the effectiveness of that technology for a particular class of cognitive tasks that much be performed by that organization. This generates research questions for empirical studies of human cognition and collaboration in the organization and in our laboratory. The results of this work inform the selection and customization of technological systems for cognitive support, enhanced design of those systems, and methods for training users and integrating visual analytic support technology within an organization and across collaborating organizations. The project is designed to assure integration of key aspects of visual analytics: technology design, onsite and distributed collaboration with Aeroinfo (Boeing Canada) customers, and direct support for core business operations at Aeroinfo.
VA for Carbon Trading: This NSERC-funded project is conducted in partnership with Cap-Op Energy. Cap-Op has developed the premier energy efficiency platform for the oil & gas industry to automate and standardize the quantification of greenhouse gas credits (i.e. carbon offsets) from data acquisition through to verification and reporting. The company now faces two technical issues that it must overcome to enable it to grow its client offering. The first is to reduce the amount of time and effort required to clean and validate datasets. The second is to enhance the capability to detect and analyze energy efficiency patterns and trends in the data (e.g., what is the fuel consumption over the course of the year? What are the time trends?). The research challenge for CapOp Energy is to move from raw data, to knowledge to insights about energy usage and efficiency, ending in plans to optimize the overall financial situation relative to carbon use. This challenge requires the application of data analytics. Computational methods will be an important part of this, e.g., the application of statistical approaches for data cleaning, and machine learning for aggregation of energy efficiency indicators. However, computational approaches only work under certain conditions: when algorithms exist that can compute the energy efficiency patterns and trends; when data are correctly structured and well-defined; and when data are complete, correct and do not change over time. In the case of CapOp Energy, these pre-conditions are frequently not met due to challenges in quality of the data and the need for human interpretation of the accuracy and implications of given data elements. Ontologies and semantics for many aspects of data are ill defined and ambiguous, requiring that a human interpret their meaning. Data are frequently erroneous, incomplete, and may change over time, thus requiring human intervention to interpret, clean or pre-process. For this reason, an approach that combines the strengths of computation with human analysis is recommended.