Difference between revisions of "BOSC Keynote Speakers"

Revision as of 18:41, 12 March 2013

BOSC 2013 is pleased to announce the following keynote speakers:

Sean Eddy

Sean Eddy is a group leader at the Howard Hughes Medical Institute's Janelia Farm. He is interested in deciphering the evolutionary history of life by comparison of genomic DNA sequences. His expertise is in the development of computational algorithms and software tools for biological sequence analysis. He is the author of several computational tools for sequence analysis including the HMMER and Infernal software suites, as well as a coauthor of the Pfam database of protein domains. He serves as an advisor to several foundations and US science agencies, including the National Institutes of Health and the National Academy of Sciences, often on matters of large-scale computation and data analysis in biology.

Sean's talk is entitled Biological sequence analysis in the post-data era.

Biological systems are almost unfathomably complex, yet their complexity is reproducibly specified by a small digital genome. We understand many basics of development and evolution but we lack a truly satisfying quantitative understanding of how biological complexity is specified and how it evolves. One important line of attack on the problem is to reconstruct the history of molecular evolution by comparative genome sequence analysis. Biological sequence comparison has a long intellectual history, but only recently, with the advent of inexpensive large scale DNA sequencing, have we gained comprehensive access to genome sequences from essentially all species. Though welcome, this influx of genome sequence data is exposing structural flaws in computational biology research tools. Because the research community values innovative science over infrastructure in any short-term decision, academic researchers have difficulty investing sufficient effort in robust software and datasets that may enable even more innovative science over the long term. Meanwhile, professional commercialization of the software and data infrastructure also continues to prove difficult, in part because open source code and data availability is a fundamental principle of scientific publication of reproducible, reusable results. I'll discuss what I see as some of the key tensions, challenges, and opportunities in these regard, in part in the context of our work at Janelia Farm on the HMMER and Infernal codebases, and our nascent work on the genomic specification of neural circuits in Drosophila.

Cameron Neylon

Cameron Neylon is Advocacy Director for the Public Library of Science, a research biophysicist and well known agitator for opening up the process of research. He speaks regularly on issues of Open Science including Open Access publication, Open Data, and Open Source as well as the wider technical and social issues of applying the opportunities the internet brings to the practice of science. He was named as a SPARC Innovator in July 2010 for work on the Panton Principles and is a recipient of the Blue Obelisk for contributions to open data. He writes regularly at his blog, Science in the Open.

Cameron will speak about Network ready research: The role of open source and open thinking:

The highest principle of network architecture design is interoperability. If Metcalfe's Law tells us that a network's value can scale as some exponent of the number of connections then our job in building networks is to ensure that those connections are as numerous, as operational, and as easy to create as possible. Where we make it easy for anyone to wire in new connections we maximise the ability of others to contribute to the value of our shared networks.

Bioinformatics has, from time to time, been derided as "slidedecks full of hairballs", yet those hairballs, and their ubiquity are emblematic of the fact that at its heart bioinformatics is a science of networks. Networks of physical interactions, of genetic control, of degree of similarity, or of ecological interactions amongst many others. Bioinformatics is also amongst the most networked of research communities and amongst the most open in the sharing of research papers, of research data, tools, and even research in process in online conversations and writing.

Lifting our gaze from the networks we work on to the networks we occupy is a challenge. Our human networks are messy and contingent and our machine networks clogged with things we can't use, even if we could access them. What principles can we apply so as to build our research into networks that make the most of the network infrastructure we have around us. Where are the pitfalls opportunities? And where are the opportunities? What will it take to configure our work so as to enable "network ready research"?