Applications of Data Science to High Throughput Experimentation

Chair(s):
Nance, E., University of Washington
Co-chair(s):
Hachmann, J., University at Buffalo, SUNY

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.

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Individuals

AIChE Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $225.00