Gully Burns

Gully Burns
Information Sciences Institute, University of Southern California
Marina del Rey, United States

Speaker of Workshop 2

Will talk about: Using experimental design to design neuroinformatics data structures

Bio sketch:

Gully Burns develops pragmatic biomedical knowledge engineering systems for scientists that (a) provide directly useful functionality in their everyday use and (b) is based on innovative, cutting edge computer science that subtlely transforms our ability to use knowledge. He was originally trained as a physicist at Imperial College in London before switching to do a Ph.D. in neuroscience at Oxford. He came to work at USC in 1997, developing the 'NeuroScholar' project in Larry Swanson's lab before joining the Information Sciences Institute in 2006. He is now works as project leader in ISI's Information Integration Group, as well as a Research Assistant Professor of neurobiology at USC’s College of Letters, Arts and Sciences. He maintains a personal blog called 'Ars-Veritatis, the art of truth', and is very interested in seeing how his research in developing systems for scientists could translate to helping and supporting understanding and our use of knowledge in everyday life.

Talk abstract:

The interdisciplinary nature of neuroscience research leads to an explosion of different informatics tools, data structures, platforms and terminologies. A central difficulty faced by developers is that knowledge representations for any neuroscience subdomain must serve the domain-specific needs of that specified sub-community. Related representations overlap, they contradict each other, they have competing standards. The process of standardization is itself difficult to organize within the community and even harder to enforce in practice. This involves complex issues involving ease of use, computability, data availability as well as scientific correctness and philosophical purity.

In this talk, I present a novel, relatively simple conceptual design that makes a clear distinction between interpretive and observation knowledge to build a general framework for scientific data. Our methodology (called 'Knowledge Engineering from Experimental Design' or KEfED)  uses an experiment's protocol's to define the dependencies between its independent and dependent variables. These dependencies support the construction of a data structure that can capture (a) data points, (b) mean values, (c) statistical significance relations and (d) correlations. We will describe the underlying formalism of the KEfED approach, the tools we provide to help researchers build their own models, our approach to unify and standardize the definition of variables, the application of KEfED to complex neuroscience knowledge and possible research directions for this technology in the future.