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AMMAR ******* AI/ML Researcher

$1440 / day


I have over 10 years of experience as a Senior Data Scientist based in Perth, WA, Australia, specializing in machine learning, computer vision, and deep learning. Throughout my career, I have applied advanced AI techniques to tackle complex challenges in remote sensing, particularly within the mining and agriculture sectors. Currently at NGIS, I lead projects leveraging Google Cloud Platform and TensorFlow to develop machine learning models. These projects include tasks such as image retrieval from satellite imagery and object detection for ground disturbance monitoring. Previously, I made significant contributions at Astron Environmental Services and Spookfish (now Eagleview), focusing on species detection in remote sensing imagery and 3D modeling of aerial imagery. My academic background includes a PhD in Computer Vision and Machine Learning from The University of Western Australia, with research published in respected journals on topics like underwater scene classification and coral reef mapping. I am proficient in Python, TensorFlow, PyTorch, and GIS technologies, supported by practical experience across various engineering disciplines and recognized achievements in both academia and industry.


3D Point Cloud AnalysisComputer VisionData AnalysisData MiningData VisualizationDeep LearningGIS (Geographic Information Systems)Image RetrievalImage SegmentationMachine LearningObject DetectionPythonPyTorchRemote SensingTensorFlow
AI ConsultantComputer Vision EngineerDeep Learning EngineerMachine Learning Engineer


Oct 2014 - Nov 2018 PhD in Computer Vision and Machine Learning at The University of Western Australia
Sept 2012 - Aug 2014 Masters in Robotics and Control Theory at Gyeongsang National University
Sept 2008 - Aug 2012 B.E., (Hons.) Electrical Engineering at National University of Sciences & Technology


Oct 2019 - Present Senior Data Scientist at NGIS

– Machine learning for remote sensing applications in mining and agriculture using Google Cloud Platform and TensorFlow.
– Image retrieval from satellite imagery using shapes as queries using PyTorch.
– Object detection and image segmentation using satellite imagery for various projects, including ground disturbance detection and vegetation monitoring.
– Camera pose estimation, object detection, and 3D point cloud analysis for Fortescue.
– CKAN implementation for metadata cataloguing for Fortescue.

Apr 2019 - Sept 2019 Senior Remote Sensing Analyst at Astron Environmental Services

– Species detection in remote sensing imagery using deep learning-based object detectors for habitat rehabilitation.
– Semantic segmentation of urban areas to classify vegetation and hard surfaces for change detection.
– Gully detection to automatically assess erosion in mining waste rock landforms.
– Developed algorithms for land cover classification in satellite imagery to support environmental monitoring initiatives.
– Implemented deep learning models for crop type classification and yield prediction using multispectral satellite data.
– Conducted image fusion techniques to integrate optical and radar satellite imagery for enhanced feature extraction.
– Applied machine learning techniques for anomaly detection in satellite time series data for early warning systems.
– Contributed to the development of geospatial data analytics pipelines using Python, TensorFlow, and GIS technologies.

Nov 2018 - Feb 2019 Machine Learning Engineer at Spookfish (now Eagleview)

– Conducted object detection in aerial imagery to identify vegetation and classify urban scenes.
– Utilized dense reconstruction techniques on aerial imagery to generate accurate 3D models.
– Developed algorithms for land cover classification using satellite imagery.
– Implemented deep learning models for monitoring deforestation and land use changes.
– Applied image analysis techniques to assess crop health and productivity from drone imagery.

May 2018 - Oct 2018 Data Analytics Internee at Woodside

– Conducted exposure correction on images to enhance the accuracy of deep learning models used for corrosion detection.
– Generated synthetic datasets to train corrosion detection models, enabling anomaly reporting and improving model robustness.
– Implemented transfer learning techniques to adapt pre-trained models for specific environmental conditions.
– Utilized cloud-based platforms such as AWS and Google Cloud for scalable data processing and model training.

Nov 2017 - July 2018 Machine Learning Engineer at MAPIZY

– Implemented deep learning-based object detectors to detect snails in wheat grains and fields, enhancing pest management strategies in agriculture.
– Developed custom data augmentation techniques to improve model generalization across diverse environmental conditions.
– Integrated computer vision algorithms with IoT devices for real-time monitoring of snail populations, enabling proactive pest control measures.
– Collaborated with agricultural experts to validate model outputs and refine detection algorithms based on field observations.
– Deployed scalable solutions on cloud platforms such as Azure and AWS to handle large-scale image datasets and facilitate efficient model training.

Aug 2016 - Jun 2018 Teaching Assistant at The University of Western Australia

– Developed curriculum materials and assessments to enhance student understanding of data analysis techniques and visualization principles.
– Provided one-on-one mentoring to students, offering guidance on complex topics and assisting with practical assignments.
– Evaluated student performance through rigorous marking and constructive feedback, fostering a collaborative learning environment.
– Incorporated innovative teaching methodologies, including interactive simulations and case studies, to deepen students’ grasp of computational analysis and visualization tools.

Sept 2012 - Aug 2014 Graduate Research Assistant at Gyeongsang National University

– Led research on the control and navigation of magnetic nanoparticles for targeted drug delivery, focusing on enhancing precision and efficiency in medical treatments.
– Developed a hybrid magnetic particle imaging (MPI) system aimed at advancing the visualization and manipulation capabilities of nanoparticles for therapeutic applications.
– Conducted experiments to validate the efficacy of the MPI system in guiding nanoparticles to specific targets within biological environments.
– Collaborated with multidisciplinary teams of scientists and engineers to integrate biomedical imaging techniques with nanoparticle control algorithms.

Jan 2012 - July 2012 Research Student at College of Electrical & Mechanical Engineering

– Led the development of an autonomous flight and navigation system for a quad-rotor UAV as a Senior Year Design Project.
– Utilized mathematical modeling and simulation techniques in MATLAB to analyze and optimize the 6 Degrees of Freedom (DOF) flight dynamics.
– Implemented control algorithms to enhance stability and maneuverability, ensuring reliable autonomous operations.
– Conducted extensive testing and validation of the UAV system, including real-world flight trials to assess performance under varying conditions.