Arts & Sciences Faculty Receive Combined $1 Million in Funding from Department of Energy
Through the FAIR initiative, the DOE's Office of Science is supporting mutually beneficial relationships between Minority Serving Institutions (MSIs)/Emerging Research Institutions (ERIs) and partnering institutions. The $37 million in grant funding will help facilitate basic research in applied mathematics, biology, chemistry, computer science, engineering, geoscience, isotope research, materials science and physics at the selected institutions.
Mei, professor of physics and director of the Center for Ultra-Low Background Experiments at Dakota (CUBED), will receive $564,300 over a 36-month award period for his proposed research project titled, “Demonstration of Home-Grown Crystals for Future SuperCDMS Experiment (DHGC-FSE).” Mei will work alongside a co-investigator from a DOE partner laboratory: Francisco Ponce, Ph.D., Pacific Northwest National Laboratory (PNNL).
“This opportunity is a testament to the power of collaboration in driving meaningful scientific progress and opens doors to exciting possibilities in the field of physics research,” Mei continued.
Mei’s DHGC-FSE project is dedicated to advancing cosmic frontier experiments in high-energy physics through the development of low-threshold, cryogenic germanium detectors. These detectors aim to explore the properties of dark matter and neutrinos, contributing to the understanding of the universe’s composition.
Saravanan, assistant professor of computer science, will receive $562,500 over a 36-month award period for her proposed research project titled “An Efficient Storage-Driven Machine Learning Model for Performance in the Era of Multimodal Scientific Data.” Saravanan will work alongside co-investigator Khaled Z. Ibrahim, Ph.D., from Lawrence Berkeley National Laboratory.
Saravanan’s project seeks to address issues of storage efficiency in high performance computing (HPC) by innovatively transforming data and leveraging recent advancements in artificial intelligence and machine learning techniques to accelerate real-time multimodal data processing and distributed data storage in HPC.
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