MSI PIs Receive Distinguished Teaching Awards

Each year, the University of Minnesota presents the Morse-Alumni and Graduate and Professional teaching awards to recognize faculty who are committed to ongoing improvement of teaching and learning. Morse-Alumni awards recognize outstanding contributions to undergraduate education. The Graduate and Professional teaching awards recognize faculty in graduate and/or professional education.

Four MSI PIs have received awards for 2018-19. The complete lists of awardees can be found on the Scholars Walk website:

Horace A. Morse – University of Minnesota Alumni Association Award for Outstanding Contributions to Undergraduate Education

  • Catherine E. Wolfgram French (professor, Civil, Environmental and Geo- Engineering)
    • Professor French creates computer models of reinforced and prestressed concrete structural systems. These models allow researchers to investigate a broader range of parameters than is possible with experiment alone.
  • Daniel Keefe (associate professor, Computer Science and Engineering)
    • Professor Keefe develops and studies visualization, computer graphics, and human-computer interaction techniques for analyzing scientific datasets. His group uses MSI to investigate new approaches to data analysis based on interactive, exploratory visualization.

Award for Outstanding Contributions to Graduate and Professional Education

  • Daniel Boley (professor, Computer Science and Engineering)
    • Professor Boley uses MSI for studies into machine learning. His group’s research focus is to analyze the convergence behavior of many different algorithms to solve machine learning problems, and to compare the theoretical predictions with the observed behavior from computational experiments.
  • Monica Luciana (professor, Psychology)
    • Professor Luciana is conducting a comprehensive investigation of brain development during adolescence and early adulthood and to determine how brain development is affected by the use of alcohol and/or other drugs. MSI resources are used to work with MRI scans to determine brain network connectivity.