Shoshana’s is the Business Relationships Manager for Faculty of Engineering and Information Technology, University of Technology Sydney.
Shoshana’s key role at UTS:FEIT is to drive and build strategic research partnerships with industry, government and research organisations, that will enable them to access the skills, expertise and innovative capacity of UTS researchers in the areas of Engineering and IT, as well as access to our world-leading facilities such as the Data Arena, to develop cutting-edge solutions to solve real world problems.
Shoshana has a research and technical background in water technology, developing smart water monitoring systems for recycled water, potable and wastewater systems. Shoshana has experience in IT, technology and start-ups, through her own experiences as a research scientist at Griffith University and the University of Queensland and industry roles in PICT Alliance, IT Forum Gold Coast and Australian Water Association.
Shoshana has over 10 years’s experience in stakeholder engagement and building collaborative industry/research partnerships. In recognition of her developments in water technology in 2008 was awarded a prestigious Queensland Smart Women Smart State Award, in 2009 awarded the title of Queensland Young Water Professional of the Year, 2010 awarded a highly commended as Australian Young Water Professional of the Year, by the Australian Water Association
Dr Fogelman is currently member of the Australian Water Association.
She has published over 15 journals, conference and government reports.
Smart Water Resource Management
Rahman, JS, Li, J, Xie, J, Fogelman, S & Blumenstein, M 2018, 'Connectivity Based Method for Clustering Microbial Communities from Metagenomics Data of Water and Soil Samples', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Rio de Janeiro, Brazil, pp. 1-8.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. Understanding microbial community structure of metagenomics water and soil samples is a key process in discovering functions and impact of microorganisms on human and animal health. Evolution of Next Generation Sequencing (NGS) technology has encouraged researchers to sequence large quantity of microbial data from environmental sources. Clustering marker gene sequences into Operational Taxonomic Units (OTU) is the most significant task in microbial community analysis. Several methods have been developed over the years to improve OTU picking strategies. However, building strongly connected OTUs is a major issue in majority of these methods. Herein we present ConClust, a novel method for clustering OTUs that is based on quantifying connectivity among the sequences. Experimental analysis on two synthetic datasets and two real world datasets from water and soil samples demonstrate that our method can mine robust OTUs. Our method can be highly benelicial to study functions of known and unknown microbes and analyze their positive and negative effect on the environment as well as human and animal health.