December 2013 - Dm-meeting - mailman.cs.vt.edu

Meeting notes (Dec 5th, 2013)
by yaozhang 05 Dec '13

05 Dec '13

Hi everyone, Today we discussed about Dr. Jieping Ye's talk and our projects. First we discussed about Dr. Ye's work. Ben showed us a KDD2012's paper "Multi-Source Learning for Joint Analysis of Incomplete Multi-Modality Neuroimaging Data" which talked about how to predict incomplete neuroimaging model scores from multi-sources. Then Yao and Liangzhe presented their course project "Group immunization" and "Predicting E-state in Twitter using Geo-information". Finally Shashi talked about his course project about classifying hashtags of twitter using parameters of SpikeM. Since Aditya will travel next week. I don't think we will have the group meeting this month. Regards, Yao * Interested in giving a talk at the DM-Meeting? Send an email to yaozhang(a)cs.vt.edu with a short description of the talk.

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Tomorrow @ 1pm
by yaozhang 04 Dec '13

04 Dec '13

Hi guys, Just reminder that we will have the regular meeting tomorrow1pm at 3160a Torg. (conference room). Regards, Yao

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Fwd: [Dac-faculty] Seminar Announcement, Dr. Jieping Ye, ASU, Tue, Dec 3, 2013, 11am, McBryde 110
by B. Aditya Prakash 02 Dec '13

02 Dec '13

Hi guys, You should attend this talk. He is a well known researcher! Cheers, - Aditya Apologies for the brevity Sent from my iPhone Begin forwarded message: > From: Naren Ramakrishnan <naren(a)cs.vt.edu> > Date: December 2, 2013 at 11:07:10 AM EST > To: Blacksburg CS Faculty <bburg-fac(a)cs.vt.edu>, dac-faculty(a)cs.vt.edu > Subject: [Dac-faculty] Seminar Announcement, Dr. Jieping Ye, ASU, Tue, Dec 3, 2013, 11am, McBryde 110 > Reply-To: naren(a)cs.vt.edu > > (If you would like to meet with the speaker, please contact me.) > > Title: Large-Scale Sparse Learning for Biomedical Data > > Abstract: Sparse methods have been applied extensively to analyze biomedical data. In this talk, we consider sparse methods for (1) variable selection where the structure over the features can be represented as an undirected graph or a collection of disjoint groups, (2) multi-source data fusion with a "blockwise" data missing pattern, and (3) network construction. We address the computational challenge by designing novel screening strategies which scale sparse methods to large-size problems. > > Bio: Jieping Ye is an Associate Professor of Computer Science and Engineering at the Arizona State University. He is a core faculty member of the Bio-design institute at ASU. He received his Ph.D. degree in Computer Science from University of Minnesota, Twin Cities in 2005. His research interests include machine learning, data mining, and biomedical informatics. He has served as Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, KDD, IJCAI, ICDM, SDM, ACML, and PAKDD. He serves as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the SCI Young Investigator of the Year Award at ASU in 2007, the SCI Researcher of the Year Award at ASU in 2009, and the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at the International Conference on Machine Learning in 2004, the KDD best research paper honorable mention in 2010, the KDD best research paper nomination in 2011 and 2012, the SDM best research paper runner up in 2013, and the KDD best research paper runner up in 2013. > > -- > Naren Ramakrishnan > Thomas L. Phillips Professor of Engineering > Department of Computer Science > http://www.cs.vt.edu/~naren > Director, Discovery Analytics Center, Virginia Tech > http://dac.cs.vt.edu > _______________________________________________ > Dac-faculty mailing list > Dac-faculty(a)cs.vt.edu

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