Post by ncwebcenter on Aug 17, 2009 21:04:30 GMT -5
Case Studies in Bayesian Statistics and Machine Learning Workshop 1 - 2009
October 15-17, 2009
Carnegie Mellon University
Pittsburgh, PA
Due to a lack of funding (NIH liked our proposal but ran out of funds for the fiscal year), it is necessary to CANCEL the first Case Studies in Bayesian Statistics and Machine Learning Workshop. The DeGroot Lecture by Michael Jordan will still be given on October 16 at Carnegie Mellon, as it is separately funded.
The First Workshop on Case Studies in Bayesian Statistics and Machine Learning will take place on October 15 -- 17th, 2009 at Carnegie Mellon University, Pittsburgh, PA. The Workshop will focus on applications of Bayesian statistics and Machine Learning to problems in science and technology. It will feature three different tracks: In-depth contributed presentations and discussions of substantial research, shorter presentations by young researchers and poster presentations. The workshop builds upon the Case Studies in Bayesian Statistics Workshop which was held at CMU for the last two decades. In conjunction with the workshop, the Department of Statistics' Eleventh Morris H DeGroot memorial lecture will be delivered by Professor Michael Jordan, University of California at Berkeley.
We are calling for abstracts for all three tracks. The first is for major case studies. Each presentation is expected to be delivered by both, the statistician / ML researcher and their collaborator(s) from the applied area. These presentations will be allocated a 3 hour slot and are expected to be detailed and represent long standing, successful collaborations. A detailed abstract (2-3 pages) from those interested in presenting one of these collaborations is due Monday, February 1, 2009. Abstracts should emphasize the scientific and technological background, and should clarify the extent to which the inferential work will address key components of the problems articulated.
The second track is for 15-minute presentations by young researchers (students or those who completed PhD within the last five years). Abstracts for this track should be 1-2 pages and are due July 1. Abstracts should emphasize the scientific problems and how the statistical work solves the problems.
Abstracts not selected for presentation would be considered for a poster session. In addition, we invite additional submissions for posters (1 page) which are due September 1, 2009.
bayesml1.stat.cmu.edu/
October 15-17, 2009
Carnegie Mellon University
Pittsburgh, PA
Due to a lack of funding (NIH liked our proposal but ran out of funds for the fiscal year), it is necessary to CANCEL the first Case Studies in Bayesian Statistics and Machine Learning Workshop. The DeGroot Lecture by Michael Jordan will still be given on October 16 at Carnegie Mellon, as it is separately funded.
The First Workshop on Case Studies in Bayesian Statistics and Machine Learning will take place on October 15 -- 17th, 2009 at Carnegie Mellon University, Pittsburgh, PA. The Workshop will focus on applications of Bayesian statistics and Machine Learning to problems in science and technology. It will feature three different tracks: In-depth contributed presentations and discussions of substantial research, shorter presentations by young researchers and poster presentations. The workshop builds upon the Case Studies in Bayesian Statistics Workshop which was held at CMU for the last two decades. In conjunction with the workshop, the Department of Statistics' Eleventh Morris H DeGroot memorial lecture will be delivered by Professor Michael Jordan, University of California at Berkeley.
We are calling for abstracts for all three tracks. The first is for major case studies. Each presentation is expected to be delivered by both, the statistician / ML researcher and their collaborator(s) from the applied area. These presentations will be allocated a 3 hour slot and are expected to be detailed and represent long standing, successful collaborations. A detailed abstract (2-3 pages) from those interested in presenting one of these collaborations is due Monday, February 1, 2009. Abstracts should emphasize the scientific and technological background, and should clarify the extent to which the inferential work will address key components of the problems articulated.
The second track is for 15-minute presentations by young researchers (students or those who completed PhD within the last five years). Abstracts for this track should be 1-2 pages and are due July 1. Abstracts should emphasize the scientific problems and how the statistical work solves the problems.
Abstracts not selected for presentation would be considered for a poster session. In addition, we invite additional submissions for posters (1 page) which are due September 1, 2009.
bayesml1.stat.cmu.edu/