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
Blumenstein, M, Green, S, Fogelman, S, Nguyen, A & Muthukkumarasamy, V 2008, 'Performance analysis of GAME: A generic automated marking environment', Computers and Education, vol. 50, no. 4, pp. 1203-1216.View/Download from: Publisher's site
This paper describes the Generic Automated Marking Environment (GAME) and provides a detailed analysis of its performance in assessing student programming projects and exercises. GAME has been designed to automatically assess programming assignments written in a variety of languages based on the "structure" of the source code and the correctness of the program's output. Currently, the system is able to mark programs written in Java, C++ and the C language. To use the system, instructors are required to provide a simple "marking schema" for each given assessment item, which includes pertinent information such as the location of files and the model solution. In this research, GAME has been tested on a number of student programming exercises and assignments and its performance has been compared against that of a human marker. An in-depth statistical analysis of the comparison is presented, providing encouraging results and directions for employing GAME as a tool for teaching and learning. © 2006 Elsevier Ltd. All rights reserved.
Fogelman, S, Blumenstein, M & Zhao, H 2006, 'Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks', Neural Computing and Applications, vol. 15, no. 3-4, pp. 197-203.View/Download from: Publisher's site
A simple method based on the mathematical treatment of spectral absorbance profiles in conjunction with artificial neural networks (ANNs) is demonstrated for rapidly estimating chemical oxygen demand (COD) values of wastewater samples. In order to improve spectroscopic analysis and ANN training time as well as to reduce the storage space of the trained ANN algorithm, it is necessary to decrease the ANN input vector size by extracting unique characteristics from the raw input pattern. Key features from the spectral absorbance pattern were therefore selected to obtain the spectral absorbance profile, reducing the ANN input vector from 160 to 10 selected inputs. The results indicate that the COD values obtained from the selected absorbance profiles agreed well with those obtained from the entire absorbance pattern. The spectral absorbance profile technique was also compared to COD values estimated by a multiple linear regression (MLR) model to validate whether ANNs were better and more robust models for rapid COD analysis. It was found that the ANN model predicted COD values closer to standard COD values than the MLR model.
Fogelman, S, Zhao, H & Blumenstein, M 2006, 'A rapid analytical method for predicting the oxygen demand of wastewater', Analytical and Bioanalytical Chemistry, vol. 386, no. 6, pp. 1773-1779.View/Download from: Publisher's site
In this study, an investigation was undertaken to determine whether the predictive accuracy of an indirect, multiwavelength spectroscopic technique for rapidly determining oxygen demand (OD) values is affected by the use of unfiltered and turbid samples, as well as by the use of absorbance values measured below 200 nm. The rapid OD technique was developed that uses UV-Vis spectroscopy and artificial neural networks (ANNs) to indirectly determine chemical oxygen demand (COD) levels. It was found that the most accurate results were obtained when a spectral range of 190-350 nm was provided as data input to the ANN, and when using unfiltered samples below a turbidity range of 150 NTU. This is because high correlations of above 0.90 were obtained with the data using the standard COD method. This indicates that samples can be measured directly without the additional need for preprocessing by filtering. Samples with turbidity values higher than 150 NTU were found to produce poor correlations with the standard COD method, which made them unsuitable for accurate, real-time, on-line monitoring of OD levels. © Springer-Verlag 2006.
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.