I am a Postdoctoral researcher in Deep Learning and Spatial Ecology, working with Prof Ben Sheldon at the University of Oxford. My research focuses on developing computational Deep Learning methods to infer species identity and phenology at the level of individual trees from time series of drone images. The aim of my work is to leverage Deep Learning, Computer Vision, and Remote Sensing technologies to enhance our understanding of ecosystems and biodiversity.

Prior to that I was an EPSRC funded PhD candidate, working on Deep Learning for Remote Sensing systems at the University of Sussex, advised by Prof. Andy Philippides and Prof. Novi Quadrianto. My research focused on extracting salient feature representations for domain adaptation and semantic segmentation tasks.

Being part of the Predictive Analytics Lab (PAL) – an interdisciplinary research team, I was involved in a British Academy funded project focused on Satellite/Aerial Image Scene Segmentation, where I designed novel Deep Learning models that classify land use from satellite/aerial images. For more details please see: - PAL. The aim of the project was to apply deep learning techniques, to map peri-urban agriculture in Ghaziabad India, and research ways of integrating multiple types of data through a web-based mapping and visualisation tool. This project contributed to the SDG 11: Sustainable Cities and Communities. Please see the demo below. demo Currently, I am developing a novel architecture that extracts salient features whilst capturing more context and spatial image information by employing a wider receptive field, thus enhancing semantic segmentation performance in aerial images. The proposed approach is efficient in detecting water bodies (lakes, rivers) from Unmanned Aerial Vehicles (UAVs). Compared to the current state of the art, the proposed model is resilient to shadows and effectively retrieves scene information that is hidden beneath canopy. This project contributes to the SDG 6: Clean Water and Sanitation.

Furthermore, I investigated the Deep Learning robustness to domain shifts due to seasonal variations and proposed a novel architecture that extracts invariant feature representations between domains by combining salient texture feature extraction and a wider receptive field. This makes the model robust in the presence of domain shifts due to seasonal variations. This project contributes to the SDG 13: Climate Action.

During my research internship at Satellite Applications Catapult (under the supervision of Dr Cristian Rossi), I applied physics aware AI and Remote Sensing data to detect and classify cement plants in China by exploiting physical properties such as plant surrounding temperature and soil moisture. This project contributes to the SDG 9: Industry, Innovation and Infrastructure.

Prior my PhD, I worked as a Control and Automation Design Engineer for the oil and gas industry, where I was exposed to the full software and hardware life cycle, systems sensing and control, communications, functional safety, project planning and management.

I am always up for new collaborations, drop me an email if you want to chat!

Recent News

  • November 2023: Postdoctoral Researcher - Sheldon Lab University of Oxford!
  • August 2023: Passed my PhD Viva!
  • July 2023: Chaired session Image Analysis for the Remote Sensing of Water Bodies on IGARSS’23!
  • July 2023: Presented my paper Physics Aware Semantic Segmentation… on IGARSS’23!
  • June 2023: Presented my paper Seasonal Domain Shift… on EarthVision CVPR’23!
  • May 2023: Submitted my Thesis!
  • April 2023: Paper Seasonal Domain Shift in the Global South… got into EarthVision CVPR’23!
  • April 2023: Paper Physics Aware Semantic Segmentation… got into IGARSS’23 Oral!
  • July 2022: Presented my paper Deep Learning Robustness to Domain Shifts… on IGARSS’22!
  • May 2022: Presented my internship research Remote Sensing & Deep Learning Polluting Plant Detection… on DISCnet consortium!
  • April 2022: Paper Deep Learning Robustness to Domain Shifts… got into IGARSS’22!
  • April 2022: Paper Detection and Characterisation of Pollutant Assets… got into IGARSS’22 Oral!
  • January 2022: Research Internship Satellite Applications Catapult!
  • January 2021: Awarded DISCnet Scholarship!
  • August 2020: Machine Learning Summer School Indonesia (Awards Received: Most Active Participant, Best Research Proposal)!
  • January 2020: Joined British Academy funded project, Aerial Image Scene Segmentation!
  • November 2019: Presented a demo for Detecting Water Bodies from UAVs to National Rail!