Month: May 2020

Computing Awards COVID-19 Research Seed Grants

The College of Computing is pleased to announce that it has awarded five faculty seed grants, which will provide immediate funding in support of research projects addressing critical needs during the current global pandemic. Tim Havens, College of Computing associate dean for research, said that the faculty seed grants will enable progress in new research . . .

Meet Bonnie Henderson, Data Science Master’s Student and CCLC Coach

By Karen S. Johnson, Communications Director, College of Computing Data Science graduate student Bonnie Henderson began her master’s degree at Michigan Tech in fall 2019. From Jarrell, Texas, Henderson earned a B.A. in mathematics and French at Southwestern University, Georgetown, Texas. Henderson is a recipient of Michigan Tech’s David House Family Fellowship, which she describes . . .

Tim Havens Quote in Enterprisers Project Article

Tim Havens, associate dean for research, College of Computing, and director of the Institute of Computing and Cybersystems, was quoted in the article, “Artificial intelligence (AI) vs. machine learning (ML): 8 common misunderstandings,” published May 19, 2020, in the online publication, The Enterprisers Project. In there article, Havens likens the way AI works to learning . . .

MTU’s Adrienne Minerick Elected to Lead Engineering Educators

by Allison Mills, University Marketing and Communications Adrienne Minerick, dean of the College of Computing, is president-elect of the American Society for Engineering Education (ASEE). She will serve as president-elect from June 2020 to June 2021, a year that will surely be shaped by COVID-19 response efforts and their impacts on education, engineering industries and . . .

Havens, Yazdanparast Publish Article in IEEE Transactions on Big Data

An article by Audrey Yazdanparast (2019, PhD, Electrical Engineering) and Dr. Timothy Havens, “Linear Time Community Detection by a Novel Modularity Gain Acceleration in Label Propagation,” has been accepted for publication in the journal, IEEE Transactions on Big Data. The paper presents an efficient approach for detecting self-similar communities in weighted graphs, with applications in . . .