Experience

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.