In its effort to promote the research and development of data science methods in support of scientific research, the Academy of Data Science Discovery Fund has granted an all-time high six awards for the 2024-25 academic year.

Previously, no more than three awards had been given in a single funding cycle.

The projects supported in this year’s awards focus on the use of data science methods in the fields of chemistry, computer science, forest health, mathematics, psychology, public health, social sciences, and statistics.

The Academy of Data Science Discovery Fund was established in 2021 to support pilot studies, data collection, or data analysis that will enable eventual application for external interdisciplinary research funding. The fund provides up to $10,000 to a single investigator and up to $20,000 for multiple investigators.

While the Academy of Data Science Discovery Fund was previously earmarked for projects generated by the College of Science, it is now open to all Virginia Tech faculty members, including collegiate and research faculty.

Three of the six projects funded this year came from outside the College of Science: two from the College of Engineering, including one in association with the Virginia Tech Carilion School of Medicine, and one from the College of Natural Resources and Environment.

Projects receiving awards are the following:

  • Carrie Fearer, assistant professor, Department of Forest Resources and Environmental Conservation, and co-principal investigators P. Corey Green, assistant professor, Department of Forest Resources and Environmental Conservation; David Carter, associate professor, Department of Forest Resources and Environmental Conservation; and W. Mark Ford, associate professor, Department of Fish and Wildlife Conservation. Project: “Rapid, In-Field Identification of Resistant Trees Using NIR Spectroscopy and Machine Learning.” The goal of this project is to develop machine learning algorithms coupled with new, portable near-infrared technology to detect Fraser fir trees resistant to balsam woolly adelgid, a nonnative, invasive forest pest in the southern Appalachian red spruce fir forests, in hopes of managing and restoring these ecosystems. These methods will then be adapted for other tree-pest systems to improve and protect the health of America’s native forests and tree species. Award: $10,000.
  • Ivan Hernandez, assistant professor, Department of Psychology, and co-principal investigator Louis Hickman, assistant professor, Department of Psychology. Project: “Enhancing Open Science Data Sharing through the Synthesis of Qualitative Datasets.” Led by graduate student investigator Amal Chekili, the goal of this project is to develop a novel tool for promoting data sharing within research that collects open-ended narratives, which may contain sensitive or identifying information. This tool would maintain key statistical properties within the text data while ensuring confidentiality. It would be particularly useful for researchers engaged in artificial intelligence (AI) /machine learning, statistical analysis, and modeling, as it allows for greater data transparency, collaboration, and archival analysis of complex datasets without risking privacy breaches. Award: $15,000.
  • Andrew Katz, assistant professor, Department of Engineering Education. Project: “Using Large Language Models and Generative AI to Scale Qualitative Data Analysis.” Qualitative data analysis is a critical component of research in the social sciences, yet is often a time-consuming and labor-intensive process that can lead to biased or incomplete results. With this proposal, Katz aims to leverage large language models and classic machine algorithms to improve researchers’ abilities to analyze large volumes of qualitative data in a robust and replicable manner. Award: $10,000.
  • Justin Krometis, research assistant professor, Virginia Tech National Security Institute, and adjunct assistant professor, Department of Mathematics, and co-principal investigator Jeff Borggaard, professor, Department of Mathematics. Project: “Bayesian Approaches to Estimating Fluid Domains.” This project follows up on previous work applying the Bayesian approach to shape estimation, which captures uncertainty or unresolved degrees of freedom in the domain of a fluid flow. It will attempt to extend the models of the fluid flow to describe more realistic flows and develop approximate adjoint methods for the forward map to allow more efficient and accurate approximation of the posterior distribution describing the unknown shape. Award: $15,000.
  • Chris Thomas, assistant professor, Department of Computer Science, and co-principal investigators Jacob Gillen, assistant professor, Department of Surgery, Virginia Tech Carilion School of Medicine, and Chris Arena, collegiate associate professor, Department of Biomedical Engineering and Mechanics. Project: “Instreyed: Advancing Fine-grained Data-limited Visual Recognition for Smart Surgical Tray Management.” In this project, the principal investigators aim to develop novel computer vision techniques to build a smart surgical instrument management system. The system recognizes very fine-grained instruments and detects anomalies on them, such as contamination or rust, with very little training data. This system helps ensure that surgeons have the necessary tools to perform live-saving surgeries when time is of the essence. Award: $15,000.
  • Hongxiao Zhu, associate professor, Department of Statistics, and co-principal investigator Feng Lin, associate professor, Department of Chemistry. Project: “Data-Enabled Knowledge Discovery in Rechargeable Batteries.” This is a collaborative proposal from the statistics and chemistry departments on data analysis for rechargeable batteries. The proposed work will develop data analysis techniques to characterize data heterogeneity and perform feature extraction. Award: $15,000.

“Data science is essential to much of today’s research being conducted at Virginia Tech throughout the university, not only in the College of Science,” said Tom Woteki, founding director of the Academy of Data Science. “With these awards, we are excited to support the investigators who are using data science to address significant issues in society across all disciplines.”

The Academy of Data Science was launched in 2020 to promote the application of data science methods to help solve scientific problems and foster the development of data science methods in support of science.

An interdisciplinary hub for data science collaboration and research for faculty, it serves as the connective fabric between the College of Science and other Virginia Tech colleges and institutes in collaborating to develop new data science methodologies and applications.

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