Welcome to the Quantitative Life Sciences Initiative
A faculty-led initiative to develop research and educational opportunities in data science for the life sciences.

        With unprecedented volumes of data being generated around the globe, it is clear that data science holds the promise of solving the most pressing issues of our times and the need for data scientists across all disciplines is at a critical juncture.

        Now more than ever, in this interconnected age of technology, data science is the key to unlocking opportunities in a vast number of fields.

        The QLSI is a university-wide, faculty-driven program to develop research and educational resources, expertise, and opportunities in data sciences for the life sciences. We collaborate with researchers in multiple STEM disciplines — mathematics, statistics, computer science and engineering, bioinformatics, biological systems engineering, electrical engineering – to establish cross-campus linkages via data science. These linkages enable transdisciplinary research, training and consulting with partners in the life sciences from academia, industry, and government.

          The main objective of the initiative is to conduct and coordinate data science research and training in the life sciences, towards the advancement of knowledge of genes, complex cell processes, and ecosystems.

       We also support the application of this knowledge to sustainable agriculture and improved health and well-being. Progress towards this objective can take many forms, from methods for data processing, transfer, and storage to novel analyses of large, complex datasets.

       We work with the Holland Computing Center and faculty across campus to facilitate hardware/software acquisition and access and providing advice on modeling and predictive analytical approaches to data. In addition the QLSI facilitates the hiring of new faculty members with advanced quantitative and computational expertise.

      With our objective and our new doctoral program in Complex Biosystems, the initiative is positioned to have a direct impact on the life and quantitative sciences at UNL. This is an exciting time for students and faculty with interests in the future of the life.



QLSI Members

 Jennifer Clarke, Ph.D., DIRECTOR

Jennifer Clarke, Ph.D., is a Professor of Food Science and Technology, and Statistics, and the Director of the Quantitative Life Sciences Initiative at the University of Nebraska-Lincoln. Dr. Clarke received her undergraduate degrees in Mathematics and Psychology from Skidmore University, a M.S. in Statistics from Carnegie Mellon University and a Ph.D. in Statistics from the Pennsylvania State University under the mentorship of C.R. Rao. She conducted postdoctoral research at the National Institute of Statistical Sciences in Research Triangle Park and the Department of Statistical Sciences at Duke University before joining the faculty at Duke. Prior to coming to UNL in 2013, she was a faculty member at the University of Miami in the Division of Biostatistics and the Center for Computational Sciences. She serves on the steering committee of the Midwest Big Data Hub and is co-PI on an award from the NSF focused on data challenges in Digital Agriculture. Her current interests include statistical methodology for metagenomics and prediction, and training the next generation of data scientists.



UNL Oversite


Archie Clutter, Dean/Director, Agricultural Research Division; Animal Science

Bertrand Clarke, Department Chair of Statistics


 Stephen Scott, Professor and Vice Chair, Computer Science and Engineering

David Swanson, Director, Holland Computing Center; Research Associate Professor, Computer Science and Engineering

Stephen Kachman, Professor of Statistics

George Avalos, Professor of Mathematics

Etsuko Moriyama, Associate Professor, School of Biological Sciences; Center for Plant Science Innovation

Heriberto Cerutti, Professor, Biotechnology; Professor, School of Biological Sciences

Andrew Benson, W.W. Marshall Professor, Food Science & Technology

Khalid Sayood, Professor, Electrical Engineering