Applications of Data Science to High Throughput Experimentation

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.