(571g) Graduate Research & Training in Molecular Data Science Through a University Wide Approach to Integrative Data Science Education

All areas of science and engineering have been the beneficiaries of a data-deluge. Chemical Engineering is at the forefront of this wave as a result of the commoditization of tools such as robotic instrumentation for molecular synthesis and characterization, ever increasing computational resources for simulation and design, high throughput experimental methods including gene sequencers, a proliferation of inexpensive and networked sensors and mixed modality data sources. To be competitive in the changing landscape of industry and academia, the next generation of chemical engineers will need to know how to effectively use data science tools such as statistical and machine learning, scalable data management and visualization techniques to communicate their findings effectively to stakeholders and peers.

The University of Washington (UW) has embraced data science in a campus wide initiative and the Chemical Engineering (ChemE) department at UW has led the effort to integrate highly contextualized molecular data science instruction at the graduate level, for both MS and PhD. ChemE @ UW offers two transcriptable options to graduate students, Advanced Data Science & Data Science. Students who participate in these options get a graduate degree in Chemical Engineering with a transcripted recognition of their participation in the program. The former is designed for chemical engineers who want to design and create new data science algorithms and tools, e.g. new machine learning methods for sparse molecular descriptor spaces, while the latter is intended for data science tool users who want to be confident in their selection of methods, e.g. choosing an effective neural network architecture for a predictive model of blood brain barrier permeability.

This presentation will review the structure of ChemE @ UW’s graduate data science options from its inception as a result of two NSF training grants and in the context of the UW campus wide investment in data science education, research and training. It will highlight the NSF NRT Data Intensive Research Enabling Clean Technology (DIRECT) program which has been the primary vehicle for sustainable graduate molecular data science education in the department that includes 1) new graduate coursework – a sequence of flipped-classroom style courses that teach Data Science skills in a manner contextualized to molecules and 2) A capstone project that uses project based learning to practice and apply data science skills to challenging real world problems (in conjunction with external partners) in a team-based setting. It will close with outcomes from early cohorts that participated in the transcriptable options.