Abeer Mazher
Postdoctoral Fellow in Machine Learning, Spatial Statistics and Remote Sensing
I have experience in developing statistical algorithms and working as an applied statistician in the fields of econometrics, energy, remote sensing and earth sciences to solve real world problems. I also have approximately 5 years’ experience of teaching mathematical subjects, probability, statistical inference and stochastic processes to graduate students. My research interests are diverse, and I have worked across various research teams on projects to deal with statistical modelling, integration of spatio-temporal data, machine/deep learning for prediction and classification, clustering, correlation quantification, environmental impact assessment and visualisation.
Within Deep Earth Imaging, my primary area of research is presently within the data assimilation and the value of information research theme.
Technical skills
- Multivariate statistical modelling, data mining and data fusion
- Statistical applications in remote sensing, mineral resources and hydrology
- Exceptional knowledge of computational statistics and its implementation to real-world applications
- Solid understand of processing remote sensing data and hydrological data
- Application of machine learning in remote sensing, hydrology and mineral exploration applications
- Development of deep learning architectures for remote sensing, energy and mineral resources areas
Professional experience
September 2017 – Present
Postdoctoral Research Fellow in Machine Learning and Spatial Statistics: CSIRO Deep Earth Imaging Future Science Platform, Australia
2015 – 2017
Postdoctoral Research Fellow in GIS & Remote Sensing: Peking University, China
Data Mining and Fusion of Multi-sensor Multi-temporal Remote Sensing Data.
2011 – 2015
Lecturer (Statistics): Allama Iqbal Open University, Pakistan
Teaching, Project Supervision, Administrative work and Research
2010 – 2011
Lecturer (Statistics): National University of Computer & Emerging Sciences, Pakistan
2008 – 2010
Scientific Officer: Global Change Impact Studies Centre; Pakistan
2007 – 2008
Lecturer (Statistics): COMSATS Institute of Information Technology, Pakistan
Education
PhD (Photogrammetry and Remote Sensing) Peking University China (2015)
M.Phil (Statistics) Allam Iqbal Open University Pakistan (2011)
M.Sc. (Statistics) Quaid-i-Azam University Pakistan (2007)
Selected publications
Mazher A, (2020). Visualization Framework for High Dimensional Spatio-temporal Hydrological Gridded Datasets using Machine Learning Techniques. Submitted to Water. (Under Revision).
Mazher A, Emelyanova I, Pervukhina M, Moughal T, Dyt C, Warner, DS, Dunlop EC, Faiz M, Dewhurst D and Warner PER (2019). Deep Learning for the Exploration of Deep Coal Seams Gas. Presented at American Geophysical Union (AGU) Fall Meeting, San Francisco, USA. https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/500927
Moughal T, Mazher A, Emelyanova I and Yu F (2019). A Framework for Multi-sensor Image Segmentation using Fuzzy Collaborative Clustering. Paper presented at Australian Exploration Geoscience Conference (AEGC), Perth, Australia. https://www.tandfonline.com/doi/abs/10.1080/22020586.2019.12073030
Mazher A and Peeters L (2019). A Visualization Workflow for High Dimensional Spatio-temporal Datasets. Presented at Collaborative Conference on Computational and Data Intensive Science (C3DIS), Canberra, Australia. http://www.c3dis.com/3061
Imran M, Hamid Y, Mazher A and Ahmad SR (2019). Geo-spatially Modelling Dengue Epidemics in Urban Cities: a Case Study of Lahore, Pakistan. Geocarto International. https://www.tandfonline.com/doi/full/10.1080/10106049.2019.1614100
Hasan M, Zafar A, Yousaf M, Gulzar H, Mehmood K, Hassan SG, Saeed A, Yousaf A, Mazher A, Rongji D and Mahmood N (2019). Synthesis of Loureirin-B Loaded Nanoliposomes for Pharmacokinetics in Rat Plasma. ACS Omega, 4(4), 6914-6922. https://pubs.acs.org/doi/abs/10.1021/acsomega.9b00119
Zulfiqar H, Zafar A, Rasheed MN, Ali Z, Mehmood K, Mazher A, Hasan M and Mahmood N (2019). Synthesis of Silver Nanoparticles using Fagonia Cretica and their Antimicrobial Activities. Nanoscale Advances, 1, 1707-1713. https://pubs.rsc.org/en/content/articlehtml/2019/na/c8na00343b
Aslam H, Liu J, Mazher A, Mojo D and Imran M (2018). Willingness to Pay for Improved Water Services in Mining Regions of Developing Economies: Case Study of a Coal Mining Project in Thar Coalfield, Pakistan. Water, 10(4), 481. http://www.mdpi.com/journal/water/special_issues/Water_Stewardship_Mining
Mazher A and Li P (2017). Correlated Probabilities based Decision Fusion Method for Multi-Sensor Data. Paper presented at IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Texas, USA. http://ieeexplore.ieee.org/document/8127885/
Mazher A, Li P, Moughal TA and Xu H (2016). A Decision Fusion Method Using an Algorithm for Fusion of Correlated Probabilities. International Journal of Remote Sensing, 37(1), 14-25. http://www.tandfonline.com/doi/full/10.1080/2150704X.2015.1109158
Jabeen M, Yun L, Wang X, Rafiq M, Mazher A, Tahir MA and Jabeen M (2016). A study to analyse collaboration patterns for Asian Library and Information Science (LIS) scholars on author, institutional and country levels. Serials Reviews, 42(1), 18-30. http://www.tandfonline.com/doi/abs/10.1080/00987913.2016.1139526
Mazher A and Li P (2016). A Decision Fusion Method for Land-Cover classification using Multi-Sensor Data. Paper presented at the Fourth International Workshop on Earth Observation and Remote Sensing Applications (EORSA). http://dx.doi.org/10.1109/EORSA.2016.7552784
Moughal TA, Yu F, Mazher A, Liu S and Razzq A (2015). Enhanced detection of burned area using cross- and auto-correlation. Journal of Applied Remote Sensing, 9(1), 096018(1- 12). http://dx.doi.org/10.1117/1.JRS.9.096018
Mazher A (2013). Comparative Analysis of Mapping Burned Areas from Landsat TM Images. Paper presented at 6th Vacuum and Surface Sciences Conference of Asia and Australia (VASSCAA-6). http://dx.doi.org/10.1088/1742-6596/439/1/012038
Mazher A, Li P and Jun Z (2012). Mapping Burned Areas from Landsat TM Images: Comparative Study. Paper presented at IEEE International Conference on Computer Vision in Remote Sensing (CVRS). http://dx.doi.org/10.1109/CVRS.2012.6421276
Jun Z, Li P, Mazher A and Liu J (2012). Impervious surface extraction with very high- resolution imagery in urban areas: reducing tree obscuring effect. Paper presented at IEEE International Conference on Computer Vision in Remote Sensing (CVRS). http://dx.doi.org/10.1109/CVRS.2012.6421240