My main research interest revolves around the intersection between machine (deep) learning, computer vision, and cyber-physical systems, applied to a wide range of problems including navigation, positioning, mapping, object detection, or semantic segmentation, among others. Nevertheless, I am also keen to work in diverse interdisciplinary areas including, but not limited to, disaster prevention and management, environmental monitoring, health science, assistive technology, smart cities, intersecting with my main research interest.
Current Research Project
Global Mining Watch
The aim of the project is to perform remote sensing, geo-spatial and socio-environmental analysis of global mining footprints, supporting social impact and risk assessment of mining sites around the world. The project will utilise cutting-edge computer vision and deep-learning approaches, GIS analysis of global data and new approaches to web-based mapping and visualisation of socio-ecological environmental challenges. The project under the ‘Global Mining Watch’ umbrella will be funded from multiple funding bodies, including Google Research Scholar Award, Ford Foundation, and joint initiative between Monash University Indonesia and the University of Queensland.
Funding: Multiple grants from Google Research Scholar Award (PI), Ford Foundation (Co-PI), and UQ-MI Research Collaboration (Co-PI)
Intelligent Remote Sensing for Sustainable Flood Risk Management and Policy
This research seeks to develop an intelligent remote sensing system that can segment and map the temporal dynamics of flooding on a daily basis using AI-based remote sensing approaches to help assess flood impacts and design more sustainable flood management policies. Our study is situated in Indonesia’s upper Citarum river basin where land use change has led to the occurrence of annual flooding events.
Funding: Monash Indonesia Seed Innovation Grant (PI) and MDFI
Fintech For Social Impact
The project aims to help a fintech institution improve its risk assessment of individuals it might not usually lend to. This project combines expertise in Data Science/Machine Learning, Psychology, Human-Computer Interaction and Economics/Finance.
Funding: Monash Indonesia Seed Innovation Grant, Monash University Australia - Action Lab, and a fintech startup company (Co-PI)