Skip To Main Content

RoozbehRoozbeh Dehghannasiri, a graduate student in the Department of Electrical and Computer Engineering at Texas A&M University, received the Best Paper Award from the Midsouth Computational Biology and Bioinformatics Society (MCBIOS) 2015 conference.

Dehghannasiri won the award for his paper titled "Efficient experimental design for uncertainty reduction in gene regulatory networks.” His co-authors are Dr. Edward Dougherty and Dr. Byung-Jun Yoon.

In his paper, Dehghannasiri proposes a new experimental design framework for uncertainty reduction in gene regulatory networks. The method utilizes the concept of mean objective cost of uncertainty and incorporates a network reduction scheme to reduce the computational complexity.

Dehghannasiri received his bachelor's degree from the University of Tehran in 2010 and his master's degree from McMaster University in 2012, both in electrical engineering. Since 2012, he has been a Ph.D. student in the Genomic Signal Processing Laboratory under the supervision of Dougherty and Yoon. He was awarded the McMaster Outstanding Thesis Research Award in 2012 for his master’s thesis, the Dwight Look College of Engineering Travel Award in 2013 and 2015 and the NSF Travel Award in 2014.

Dehghannasiri’s current research interests include experimental design in gene regulatory networks, objective-based uncertainty quantification, computational biology, robust filtering and statistical signal processing.

MCBIOS advances the understanding of bioinformatics and computational biology; brings together scientists of various backgrounds and disciplines; facilitates the collaboration of researchers with similar or complementary backgrounds to solve biological, health and/or medical problems; promotes education in bioinformatics and computational biology; informs the general public on the results and implications of current research in bioinformatics and computational biology; and promotes other activities that will contribute to the development of bioinformatics and computational biology.