SF Bayarea Machine Learning Message Board › Seminar: Data Mining and Predictive Analytics for Research Applications
San Francisco, CA
San Francisco State University
Center for Computing for Life Sciences (CCLS) Presents:
Data Mining and Predictive Analytics – Research Applications
6:00 PM October 13, 2011 SFSU
About the seminar/panel discussion
This is the third seminar of the new CCLS Bay Area Seminar Series. This seminar, in the format of a panel discussion, will provide insights and explore issues regarding the role of data mining and predictive analytics in life sciences with a focus on research applications whereas the prior seminar focused on clinical applications. The panelists will describe the evolution of their experiences with data mining and predictive analytics and how they are using these research processes today. The seminar will address how data can be 1.) aggregated and mined, 2.) applied to heuristics, algorithms, and machine learning and then 3.) characterize interrelated data, interpret results and predict endpoints being studied. This seminar will be useful for statisticians, software developers, machine learning researchers, IT architects and information systems managers in life sciences.
Please register at: http://sfsudmparesear...
• Nigel Duffy – Chief Technology Officer, Numerate
• Pek Lum – VP of Life Sciences, Ayasdi
• Nicholas Tatonetti – Biomedical Informatics, Stanford
• Thomas Wu – Sr. Scientist, Bioinformatics and Computational Biology, Genentech
• Moderator: Bob Dunkle – A.B.E.S Partners
Date: Thursday, October 13, 2011
Time: Networking: 6:00-7:00 PM, Seminar: 7:00-8:45 PM
Location: SFSU Campus, Science (SCI) - Room 101, 1600 Holloway Ave (off 19th Avenue), San Francisco
SFSU directions and parking: http://www.sfsu.edu/~... - we recommend parking structure 20, then follow the map http://www.sfsu.edu/~...
Cost: No charge (light refreshments will be served)
Welcome - Dragutin Petkovic, Director, CCLS
Overview of Seminar – Bob Dunkle
Experiences and perspective in data mining and predictive analytics – Panelists
- How is disparate data aggregated to include relevant data sets for mining applications?
- Methods and processes to characterize the data and optimize predictive analytics performance
- Methods validation
- Use cases
Panel conversation and open Q&A
CCLS - SFSU Center for Computing for Life Sciences http://cs.sfsu.edu/cc...
IBM Almaden Research Center http://www.almaden.ib...
Edited by Bob Dunkle on Sep 5, 2011 11:48 AM