Studies Combine Evolutionary Theory with Big Data

The University of Vermont has attracted a $3 million grant from the National Science Foundation’s Research Traineeship (NRT) Program to develop a new, potentially transformative model for graduate scholars that takes on major environmental and global health problems.

The grant supports the QUantitative & Evolutionary STEM Training (QUEST) at UVM, which will formulate predictive models for emerging infectious diseases; research the rapid evolution in response to antibiotics, pesticides and global change conditions; and explore pathogen interactions that affect food security and ecosystem health. UVM was one of just 17 NRT grant recipients among 220 applicants.

QUEST will eventually include 36 Ph.D. students from a range of disciplines including biology, mathematics and statistics, engineering, agricultural sciences, environmental studies and health sciences. The program’s unique research approach integrates conceptual evolutionary principles with the rapidly growing amount of climate, genomic, and public health data.

“There is an unprecedented amount of ‘big data’ available in biological research that can be instrumental in finding solutions to major environmental and global health problems we face today,” said Melissa Pespeni, principal investigator of the grant and assistant professor of biology at UVM. “At the same time, it’s important to use the data in intelligent and creative ways. QUEST is one of the few programs that couples evolutionary theory with big data analyses.”

Another goal of QUEST is to increase cultural diversity in STEM training at UVM, and to provide meaningful employment for their students after graduation.

“We are uniquely positioned to offer this training program due to the large number of evolutionary biologists skilled at working with large, complex data sets,” said Cynthia Forehand, dean of UVM’s Graduate College. “QUEST also builds on the existing strengths across seven academic units on campus, and aligns with our mission to prepare students to be accountable leaders who bring to their work dedication to the global community and active collaboration across disciplines.”

QUEST courses will include case-based learning models that use real-world challenges and require foundational knowledge, critical thinking, and teamwork skills. Lab courses will use flexible learning tools (online and interactive) in innovative classroom spaces. In addition, a shared physical space for trainees will promote informal, team-based learning and collaboration.

The NSF-wide NRT program addresses key issues in the scientific community, including educating and expanding the science and engineering workforce, broadening participation in STEM (science, technology, engineering and mathematics education) fields to include traditionally underserved populations, and creating new resources at institutions that train STEM graduate students.




Kevin Coburn