From owner-chemistry@ccl.net Wed Apr 18 15:52:01 2018 From: "Johannes Hachmann hachmann]*[buffalo.edu" To: CCL Subject: CCL: Deadline TODAY: Data Mining and Machine Learning in Molecular Sciences Message-Id: <-53239-180418151624-28069-6KdPPZwe+wZ6T8RE6BRkQQ*server.ccl.net> X-Original-From: "Johannes Hachmann" Date: Wed, 18 Apr 2018 15:16:21 -0400 Sent to CCL by: "Johannes Hachmann" [hachmann,,buffalo.edu] Dear Colleague, A quick reminder that the abstract submission for our CoMSEF session on "Data Mining and Machine Learning in Molecular Sciences" at the 2018 AIChE Annual Meeting in Pittsburgh (Oct 28 - Nov 2) closes TODAY at 11:59pm. Best regards, Johannes Hachmann > -----Original Message----- > From: Hachmann, Johannes > Sent: 2 April, 2018 10:00 > To: Johannes Hachmann (hachmann++buffalo.edu) > Cc: 'Ferguson, Andrew' > Subject: CoMSEF session on "Data Mining and Machine Learning in Molecular > Sciences" at the 2018 AIChE Annual Meeting in Pittsburgh (Oct 28 - Nov 2) > > Dear Colleague, > > We are writing today to let you know that we will again be running the > CoMSEF technical session "Data Mining and Machine Learning in Molecular > Sciences" at the 2018 AIChE Annual Meeting in Pittsburgh (Oct 28 - Nov 2). > > We are currently soliciting abstracts for contributed talks, and if you or your > students are interested in presenting in this session we would be excited to > receive your submission through the online application portal. The scope of the > session is intentionally broad, concerning the generic applications of data > mining and machine learning for property prediction, molecular understanding, > and rational design. Details of the session scope and instructions for abstract > submission are provided below. The submission deadline is Wed April 18. > > We look forward to seeing you in Steel City! > > Kind Regards, > > Andrew Ferguson (University of Illinois, alf++illinois.edu) > Johannes Hachmann (University at Buffalo, hachmann++buffalo.edu) > > --- > > Data Mining and Machine Learning in Molecular Sciences > > https://aiche.confex.com/aiche/2018/webprogrampreliminary/Session38660.ht > ml > > Computational approaches to correlate, analyze, and understand large and > complex data sets are playing increasingly important roles in the physical, > chemical, and life sciences. This session solicits submissions pertaining to > methodological advances and applications of data mining and machine learning > methods, with particular emphasis on data-driven modeling and property > prediction, statistical inference, big data, and informatics. Topics of interest > include: algorithm development, inverse engineering, chemical property > prediction, genomics/proteomics/metabolomics, (virtual) high-throughput > screening, rational design, accelerated simulation, biomolecular folding, > reaction networks, and quantum chemistry. > > > ----------------------------------------------------------------------------------------- > Dr. Johannes Hachmann > Assistant Professor > University at Buffalo, The State University of New York > Department of Chemical and Biological Engineering (CBE) > New York State Center of Excellence in Materials Informatics (CMI) > Computational and Data-Enabled Science and Engineering Program (CDSE) > 612 Furnas Hall > Buffalo, NY 14260 > www.cbe.buffalo.edu/hachmann > http://hachmannlab.cbe.buffalo.edu > ----------------------------------------------------------------------------------------- >