CCL: Deadline TODAY: Data Mining and Machine Learning in Molecular Sciences

 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) <hachmann++buffalo.edu>
 > Cc: 'Ferguson, Andrew' <alf++illinois.edu>
 > 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://www.cbe.buffalo.edu/hachmann>;
 > http://hachmannlab.cbe.buffalo.edu <http://hachmannlab.cbe.buffalo.edu/>;
 >
 -----------------------------------------------------------------------------------------
 >