Description:
This session invites submissions on research that aims to leverage tools of data science for high throughput experimental work. Data-driven approaches include, but are not limited to, robotics approaches for automatic experimental work, high-throughput screening, machine learning, data mining, meta-analysis. All research areas of interest to chemical engineers are welcome. Submissions should articulate the impact of data science on the problem of interest and clearly articulate the experimental component of the work.
Sponsor:
Applications of Data Science to Molecules and Materials
Co-Sponsor(s):
Catalysis and Reaction Engineering Division (20), Computational Molecular Science and Engineering Forum (21), Information Management and Intelligent Systems (10E), Pharmaceutical Discovery, Development and Manufacturing Forum (26)
Chair:
Elizabeth Nance
Chemical Engineering, University of Washington, Seattle, WA
Co-Chair:
Johannes Hachmann
Chemical and Biological Engineering, University at Buffalo, SUNY, Buffalo, NY
Abstract Submissions Closed
See more of this Group/Topical: Topical Conference: Applications of Data Science to Molecules and Materials