Dependable Data Driven Discovery

Data-driven decisions are becoming increasingly critical for the well-being of individuals and society; and the nation and the world’s reliance on such decisions is likely to increase tremendously in the next decade. However, data science lifecycles are assembled and operated by a wide variety of individuals and institutions with varying levels of expertise.

While to err is human, the consequences of errors in critical data science lifecycles can be catastrophic. Unreliable decisions can potentially have enormous negative impacts such as loss of life, widespread contagions, and economic depression. To respond to this critical challenge, this NSF Research Traineeship project is establishing a graduate program of study, "Dependable Data Driven Discovery (D4)" that engages faculty across multiple disciplines to train students with diverse scientific backgrounds to recognize risks to dependable data driven discovery and to develop corresponding mitigation strategies.

One hundred students are expected to participate from disciplines such as computer science, mathematics, statistics, bioengineering, and computational biology, including 32 funded graduate (MS & PhD) trainees and 16 undergraduate students from minority groups who are underrepresented in their participation in these fields.

Trainees will gain a holistic perspective of the entire data science lifecycle through coursework as well as collaborative, transdisciplinary research. Three focal areas comprise the D4 research and training agenda. First is a focus on formal foundations, methodology, and tools for a dependable data driven discovery framework.

Second is an examination of risk mitigation methods to handle noise in data, limited training data, and uncertainty prediction and interpretability of machine learning models. The third focal area addresses quality assurance issues for protein function prediction and cellular engineering processes to direct undifferentiated cells into mature, functional cells with dependable data science lifecycles.

The project will develop a new graduate certificate in dependable data science to train students in dependability issues within data science lifecycles. Through coursework, trainees will experience the entire data science lifecycle several times, each with an increasingly deeper understanding of the risks, measures, and risks mitigation mechanisms. Trainees will also engage with industry partners to examine their data science lifecycles and discuss risk mitigation methods.

The capstone project course will reinforce trainees’ technical skills to address the above research problems in data science, biological science, and engineering as well as written and oral communication skills. Trainees will interact with outside collaborators through the D4 seminars, gain experiential learning with industry partners, conduct original research through capstone projects, and work in internships, resulting in awareness of dependable data science lifecycles and risk mitigation mechanisms across Iowa State University, local industry, NGOs, and government.

The project will engage in outreach activities through two existing Iowa State University infrastructures: ISU Science Bound and ISU Extension and Outreach with Iowa 4-H.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

This project is jointly funded by NRT and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.