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Below are some recent publications and presentations that have been added to CACR’s publications list. See the full list here. You can also subscribe to get notifications of new publications either via our RSS feed or the CACR Twitter feed.
- Computational Morphodynamics: Integrating Development Over Space and Time.
Nature Reviews Molecular Cell Biology (submitted)
Adrienne H. K. Roeder, Paul T. Tarr, Cory Tobin, Xiaolan Zhang, Vijay Chickarmane, Alexandre Cunha, and Elliot M. Meyerowitz.
- Cloud-based Remote Auscultation Using Mobile Phones – A Demonstration
Presentation and Demonstration at the Diabetes Technology Meeting, Bethesda MD, November 2010
Julian Bunn, Mani Chandy et al
- Discovery of the Extremely Energetic Supernova 2008fz
A.J. Drake, S.G. Djorgovski, J.L. Prieto, A. Mahabal, D. Balam, R. Williams, M.J. Graham, M. Catelan, E. Beshore, S. Larson, 2010
astro-ph, ApJL
- An efficient method for computing steady state solutions with Gillespie’s direct method
J. Chem. Phys. 133, 144108 (2010); doi:10.1063/1.3489354 (7 pages)
S. Mauch and M. Stalzer
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Anthony Goldbloom
Kaggle (http://www.kaggle.com/)
Thursday January 6, 2011
2:30 PM
100 Powell-Booth
Abstract
Machine learning and data prediction is crucial to most organizations. Banks predict which loan applicants are likely to default, treasuries forecast tax revenues and medical researchers predict the likelihood of illness from gene sequences.
Crowdsourced data mining can lead to vastly better models. My project, Kaggle, recently hosted a bioinformatics contest, which required participants to pick markers in a series of genetic sequences that predict the progression of HIV. Within a week and a half, the best submission had already outdone the best methods in the scientific literature.
This result neatly illustrates the strength of competitions. Whereas the scientific literature or in-house models tend to evolve slowly (somebody tries something, somebody else tweaks that approach and so on), a competition inspires rapid innovation by introducing the problem to a wide audience. There are an infinite number of approaches that can be applied to any machine learning problem and it is impossible to know at the outset which technique will be most effective.
Bio
Anthony is the Founder and CEO of Kaggle, a global platform for data prediction competitions. In addition to founding Kaggle, Anthony continues to consult to hosts of Kaggle competitions to help them frame prediction tasks, to get the best out of the new platform and help them integrate insights into their day-to-day operations.
Before Kaggle, Anthony was a macroeconomic modeler for the Reserve Bank of Australia and before that the Australian Treasury. In these roles, Anthony built and maintained macroeconomic models of Australia’s economy to improve forecasting and model the economic effect of changes in policy parameters, such as interest rates and fiscal policy.
Anthony graduated with first class honours in econometrics at the University of Melbourne and has published in The Economist magazine and the Australian Economic Review.
