Samiul is a graduating doctoral student in the Department of Electrical and Computer Engineering at NC State University. He has successfully defended his doctoral dissertation in February 2021. He received his B.S. in Electrical Engineering from the Bangladesh University of Engineering and Technology (BUET) in 2012. Before joining NC State, he worked as a Sr. Software Engineer at Samsung R&D Institute Bangladesh (SRBD). Samiul’s research is focused on high-throughput crop phenotyping, identification of causal relationship among crop phenotype and extrinsic factors, uncertainty quantification, model selection, and inference of parameters for dynamic models. He is interested in developing and applying machine learning algorithms to improve our understanding of biological systems. His current research is concentrated on developing computer vision algorithms and machine learning frameworks to facilitate the breeding of better quality crops. Samiul has recently accepted an offer to join as a systems engineer at the SAS Institute.[/et_pb_text][/et_pb_column][et_pb_column _builder_version=”4.2″ type=”1_3″][et_pb_image src=”https://research.ece.ncsu.edu/wp-content/uploads/sites/14/2020/01/Samiul_HeadShot-scaled.jpg” _builder_version=”4.2″ hover_enabled=”0″ align=”center” positioning=”relative” vertical_offset=”82px”][/et_pb_image][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.2″][et_pb_row _builder_version=”4.2″][et_pb_column _builder_version=”4.2″ type=”4_4″][et_pb_text _builder_version=”4.2″ hover_enabled=”0″]
Computational methods to facilitate the production of better sweetpotato: In this project, we developed novel methods to quantify shape, an important quality characteristic of sweetpotato. We developed methods for we developed novel methods for sweetpotato shape feature extraction and shape classification. In addition, we introduced a data model that can be used to identify causal relationships among environmental factors (i.e., soil type, rainfall), agricultural practices, genotype (i.e., cultivars), and sweetpotato shape. These novel methods will contribute to the development of management practices and breeding strategies that improve crop value for growers and producers and reduce food waste.
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Haque, S., Lobaton, E., Nelson, N., Yencho, G.C., Pecota, K.V., Mierop, R., Kudenov, M.W., Boyette, M. and Williams, C.M., 2021. Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery. Computer and Electronics in Agriculture.
Haque, S., Ahmad, J.S., Clark, N.M., Williams, C.M. and Sozzani, R., 2019. Computational prediction of gene regulatory networks in plant growth and development. Current opinion in plant biology, 47, pp.96-105.
Koryachko, A., Matthiadis, A., Haque, S., Muhammad, D., Ducoste, J.J., Tuck, J.M., Long, T.A. and Williams, C.M., 2019. Dynamic modelling of the iron deficiency modulated transcriptome response in Arabidopsis thaliana roots. in silico Plants, 1(1), p.diz005.[/et_pb_text][et_pb_text _builder_version=”4.2″]
Room 3031, Engineering Building II
Dept. of Electrical and Computer Engineering,
North Carolina State University,
Raleigh NC USA 27695.