The Michigan Tech Graduate School has announced that Computer Science Ph.D. candidate Ali Jalooli is among the graduate students who have received a Doctoral Finishing Fellowship Award. Jalooli’s research studies the optimization of message routing in heterogeneous wireless networks. His dissertation is titled, “Enabling Technologies for Internet of Things: Optimized Networking for Connected and Autonomous . . .
Part II | A WISE AND SUPPORTIVE NETWORK A Profile of Dr. Jason Hiebel: The College of Computing’s First Graduate By Karen S. Johnson, Communications Director, College of Computing and ICC Read Part I: Jason Hiebel, The College of Computing’s First Graduate Jason Hiebel completed his Ph.D. studies in December 2019, successfully defending his dissertation . . .
College of Computing Assistant Professor Bo Chen, Computer Science, and his graduate students presented two posters at the 41st IEEE Symposium on Security and Privacy, which took place online May 18 to 21, 2020. Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and . . .
By Karen S. Johnson, Communications Director, College of Computing and ICC Read Part II, A Supportive and Wise Network Part I | The Fast Track A Profile of Dr. Jason Hiebel: The College of Computing’s First Graduate In fall 2007, Jason Hiebel enrolled in his first semester at Michigan Tech. He’s been studying, teaching, and . . .
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 . . .
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 . . .