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 ([email protected])

> 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, [email protected])

> Johannes Hachmann (University at Buffalo, [email protected])

>

> ---

>

> 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

> http://www.cbe.buffalo.edu/hachmann <http://www.cbe.buffalo.edu/hachmann>

> http://hachmannlab.cbe.buffalo.edu <http://hachmannlab.cbe.buffalo.edu/>

> -----------------------------------------------------------------------------------------

>