Four more faculty receive NSF CAREER Awards

May 16, 2022

NSF providing $1.88 million to strengthen research opportunities

Four Spartan Engineering faculty members at Michigan State University have received National Science Foundation Early Career Development (CAREER) Awards and will advance their research efforts with a combined $1.88 million over the next five years.

NSF has provided Spartan Engineering researchers with 21 CAREER Awards in the past five years.
NSF has provided Spartan Engineering researchers with 21 CAREER Awards in the past five years.

NSF CAREER Awards support junior faculty members who exemplify the role of teacher-scholars through outstanding research and education. It is among NSF’s most prestigious national honors.

Engineering Dean Leo Kempel said four more NSF CAREER Awards bring the college’s total to 21 in the past five years.

“This is a very prestigious award for early career faculty members,” Kempel said. “I know from personal experience that it can be transformational for a faculty member and allows them to investigate a new area of scholarship with secure funding for at least five years. I am very proud of all of my colleagues in the College of Engineering and look forward to how they transform their work into future innovations with the help of this support.”

Helping energy-burdened homes

Environmental research by Kristen Cetin will advance what is known about energy-burdened homes. An assistant professor in the Department of Civil and Environmental Engineering (CEE), she will use a five-year, $508,500 grant to develop an integrated framework to assess energy efficiency and building decarbonization in diverse residential households. Grant work on "Improving the Participation of Diverse Residential Buildings in Demand Side Management (DSM)," begins summer of 2022.

Kristen Cetin
Kristen Cetin

Her work will focus on the application of building performance data, field- and laboratory-validated energy modeling methods, and smart technologies to improve building energy performance, reduce peak loads, and improve building load flexibility. The research also opens up new opportunities for the training of undergraduate and graduate students in Structural Engineering, Materials and Mechanics.

Cetin said the energy use models currently used for residential buildings don’t necessarily represent variables that include varied occupancy rates, consumer behaviors, energy preferences or even today’s priorities in U.S. households. They also don’t consider interdependencies or demographics.

“We hope to develop an integrated framework to assess the DSM potential of diverse residential households, specifically targeting underserved, lower-income urban, rural, and remote populations throughout different regions of the U.S,” she said.

Cetin received a Ph.D. in civil engineering in 2015 at the University of Texas at Austin. She joined MSU in August 2019 and is a member of CEE’s new energy and sustainability group and the structural engineering, mechanics, and materials (SEMM) group. She specializes in the application of building performance data, field- and laboratory-validated energy modeling methods, and smart technologies to improve building energy performance, reduce peak loads, and improve building load flexibility.

In August 2021, she was named director of MSU’s new Industrial Assessment Center (IAC), where faculty, staff and students are working with Michigan companies to save money, improve energy efficiency and shrink carbon footprints. A $2.25 million U.S. Department of Energy grant is supporting work with small and medium-sized manufacturers and commercial building owners.

Unlocking next-generation phylogenetics and new scientific discoveries

Kevin J. Liu will use a five-year $458,000 grant to unlock next generation phylogenetics in search of new biological discoveries. The grant, Future phylogenies: Novel computational frameworks for biomolecular sequence analysis involving complex evolutionary origins, will develop new models and algorithms for complex analyses.

Kevin Liu
Kevin Liu

Liu, an assistant professor in the Department of Computer Science and Engineering, explained that phylogenetics uses computational analysis of DNA and other big data approaches to reconstruct and analyze the evolutionary history of a set of organisms.

“Phylogenies and the evolutionary insights that they provide are essential to biology and other disciplines,” he explained. “Important examples include reconstructing and studying the Tree of Life - the evolutionary history of all life on Earth, understanding human origins, infectious disease epidemiology and discovery of new solutions to future pandemics, crop improvement and agriculture, and forensic science.”

Liu said the project will address gaps in STEM education through new curriculum development and a collaboration with the Impression 5 Science Center, a children’s science museum in Lansing, Michigan. Project impacts will be broadened through open-source software distributions and open data resources, new scientific discoveries enabled by the developed software and data infrastructure, scientific outreach activities, and student training and mentoring with a strong emphasis on diversity, equity, and inclusion.

Liu received his Ph.D. in computer science in 2011 from the University of Texas at Austin. Prior to joining MSU in 2014, he was a National Institutes of Health postdoctoral fellow at Rice University. His research interests are comparative genomics, computational biology, and bioinformatics.

This portion of the story, and faculty photos, are courtesy of the MSU College of Natural Science.

Huan Lei, assistant professor in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Statistics and Probability, studies the connection between scientific machine learning and computational mathematics. More specifically, Lei is interested in developing high-fidelity computational models for multiscale multiphysics systems.

Huan Lei
Huan Lei

Lei’s $413,516 CAREER Award will support the development of a machine learning–based model of complex fluids with molecular fidelity—a well-known and challenging problem in the fields of applied mathematics and fluid physics. To address this issue, Lei and his team will create a new learning framework based on the theories of model reduction and stochastic differential equations. In addition to its broad engineering applications, the developed method will enable scientists to probe some fundamental scientific questions in the nanoscale fluid physics field.

In addition to his research goal, Lei will develop an education plan that seeks to enhance training for the next generation of STEM workers by integrating data science and computational mathematics education. Despite the fast growth and progress of machine learning in computational mathematics and natural sciences, current education and training initiatives in these areas are largely based on conventional perspectives. To fill the gap, Lei will develop a new Capstone course on scientific machine learning geared toward STEM students to extend their understanding beyond conventional offerings. Ultimately, he hopes to develop a sustainable pipeline for the broad engagement of mathematics and natural science students and retain a diverse student population in this exciting research direction.

“This NSF CAREER award means a lot to me,” Lei said. “As an applied mathematician, my long-term research goal is to develop computational tools to advance our scientific understanding of complex multiscale physical systems. I envision this project as a foundation for my effort toward the predictive understanding and control of these systems.”

Lei received his Ph.D. in applied mathematics from Brown University. Prior to joining the MSU faculty in 2019, he worked as a research scientist and postdoctoral research associate at the Pacific Northwest National Laboratory.

Elizabeth Munch, assistant professor in Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics, is interested in the intersection of pure mathematics and data analysis. By taking topological graph-based representations of data, she can develop the theory needed to apply new tools to real data. Her goal is to create an interface between machine learning methods and graphical signatures, representations of the structure of the data using a graph.

Elizabeth Munch
Elizabeth Munch

“I am so proud of the diverse, interdisciplinary research group that I have built here at MSU,” Munch said. “It is truly an honor to receive the NSF Career award. In grad school, as a non-traditional student, I never thought that being a professor was something I could do. To not only achieve that dream, but to also receive such an honor, is beyond belief.”

Munch will use her $507,462 grant toward a new graduate-level graphical signatures course, an expanded seminar program and workshops on Topological Data Analysis (TDA). These programs will facilitate networking in the field, bringing new speakers to MSU, and will encourage interdisciplinary collaborations between junior researchers and domain scientists.

Munch received her Ph.D. from Duke University in 2013. Prior to joining the faculty of MSU in 2017, she was an assistant professor in the Department of Mathematics and Statistics at the University at Albany - SUNY, and a postdoctoral fellow at the Institute for Mathematics and its Applications at the University of Minnesota.