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Thai Tran, PhD

GeoAI & Geospatial Data Scientist

Education

PhD in Information Systems and Computer Science (Dean's List)

Lincoln University - New Zealand
2017 - 2021

Master of Science in Computer Science and Engineering

National Chiao Tung University - Taiwan
2012 - 2014

Languages

  • Vietnamese (Native)
  • English (Professional)

Interests

  • Photographer (Iphone, Canon, GoPro)
  • Play games (Chess and Rummikub)

Community

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About me

- I am a PhD-trained GeoAI researcher (Lincoln University, NZ) focused on GIS, Remote Sensing, and climate-risk applications. My work connects Earth observation data with practical interventions in urban resilience and environmental management.

- I work across QGIS, ArcGIS, Google Earth Engine, Python, and geospatial analytics pipelines to build end-to-end workflows: from data ingestion and modelling to decision-ready outputs.

- I am currently seeking roles as a GIS Researcher / Remote Sensing Scientist / Geospatial Data Scientist, with a focus on delivering measurable, decision-ready outcomes.

Experiences

Geospatial Research Assistant

05/2022 - 10/2025
Lincoln Agritech Ltd., Lincoln, New Zealand

- Delivered end-to-end GIS and remote sensing workflows using QGIS, ArcGIS, Google Earth Engine, and Python.

- Built reproducible pipelines for satellite and UAV imagery acquisition, preprocessing, modelling, and reporting.

- Worked with multi-source EO data (Sentinel-2 and UAV platforms such as eBeeX/Pix4D) to support applied research and operational decisions.

- Produced technical reports, workshop materials, and research outputs for mixed technical/non-technical stakeholders.

Research Technician

01/2022 - 04/2022
Bio-Protection Research Centre, Lincoln University

- Supported field and lab experiments by preparing equipment, materials, and experimental setups.

- Collected, organised, and quality-checked gas/soil field samples for downstream analysis.

- Maintained laboratory and field operation readiness across concurrent research activities.

Statistical Analysis and Machine Learning Consultant

02/2022 - 04/2022
N3T, Whangārei, New Zealand

- Performed data cleansing, statistical analysis, and machine learning experiments for product analytics.

- Developed data visualisations and PDF reporting workflows for decision support.

- Contributed to backend-frontend integration and AWS-based deployment setup.

Postdoctoral Researcher (AI / Machine Learning)

08/2021 – 12/2021
Lincoln University, New Zealand

- Built and analysed neural-network models (including RNN-based approaches) for biological system dynamics.

- Implemented data processing, modelling, and visualisation in Python/MATLAB to generate research-grade outputs.

- Translated analytical findings into papers, reports, and technical documentation.

PhD Candidate (Dean’s List)

04/2017 – 08/2021
Lincoln University, New Zealand

- Researched autonomous self-repair systems using neural-network-based computational frameworks.

- Designed and evaluated multiple algorithmic approaches for tissue/organism regeneration modelling.

- Published research outputs and contributed peer-review service to journals and conferences.

Data Analyst (AI / Machine Learning)

09/2019 – 12/2019
Lincoln University, New Zealand

- Prepared and structured datasets for neural-network analysis of biological tissues.

- Implemented modelling and visualisation workflows to examine memory and topology effects in Hopfield networks.

- Synthesised findings into technical reports and publication-ready research content.

Selected GeoAI / Remote Sensing Projects

I design operational geospatial systems that translate Earth observation data into decisions cities and organisations can execute. I am currently open to roles in GIS Research, Remote Sensing Science, and Climate Risk Analytics.

Projects

Extreme Heat Resilience Decision System (On going)
Built a neighbourhood-level heat-risk workflow combining satellite EO, weather inputs, and local vulnerability layers for 24-72h action planning. Target outcomes: 2-5°C hotspot cooling, >=20% reduction in heat-exposure hours, and 10-25% cooling-energy reduction in intervention sites.
High-Accuracy Rice Identification Tool (ESA-funded case study, Dong Thap)
Developed geospatial classification workflows for rice mapping in the Mekong Delta using multi-source remote sensing data to support agricultural monitoring and planning.
UAV + Satellite Crop Monitoring Workflows (Lincoln Agritech)
Developed Python-based pipelines for imagery download, preprocessing, feature extraction, and reporting from Sentinel and UAV datasets to improve reproducible agronomic analysis.
Bucket Test from Space (PI)
Developed an AI tool using satellite imagery to detect irrigation issues and improve farm water efficiency.
Geospatial Capacity Building and Applied Research Delivery
Led workshops, technical documentation, and collaborative implementation across researchers, field teams, and stakeholders to convert GeoAI outputs into operational decisions.

Best publications

  1. Sandhya Samarasinghe, T.N Minh-Thai, Komal Sorthiya, Don Kulasiri (2025): Neurons and neural networks to model proteins and protein networks, BioSystems, Volume 258, 2025, 105613, ISSN 0303-2647, https://doi.org/10.1016/j.biosystems.2025.105613. SCIE- WoS.
  2. Sandhya Samarasinghe, T. N. Minh-Thai, A Comprehensive Conceptual and Computational Dynamics Framework for Autonomous Regeneration of Form and Function in Biological Organisms, PNAS Nexus, 2023;, pgac308, https://doi.org/10.1093/pnasnexus/pgac308. ESCI- WoS.
  3. T. N. Minh-Thai, Sandhya Samarasinghe, Michael Levin: A Comprehensive Conceptual and Computational Dynamics Framework for Autonomous Regeneration Systems. Article in Artificial Life. 2021; MIT Press. DOI: https://doi.org/10.1162/artl_a_00343. SCIE- WoS.
  4. T. N. Minh-Thai, Aryal J., Samarasinghe S., Levin M. (2018) A Computational Framework for Autonomous Self-repair Systems. In: Mitrovic T., Xue B., Li X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science, vol 11320. Springer, Cham. Scopus - Elsevier.
  5. T. N. Minh-Thai and Nguyen Thai-Nghe. (2015). An Approach for Developing Intelligent Systems in Smart Home Environment. In: Dang T., Wagner R., Küng J., Thoai N., Takizawa M., Neuhold E. (eds) Future Data and Security Engineering. FDSE 2015. Lecture Notes in Computer Science, pp 147-161, vol 9446. Springer, Cham.Scopus - Elsevier.
  6. T. N. Minh-Thai and Nguyen Thai-Nghe. (2015). Methods for Abnormal Usage Detection in Developing Intelligent Systems for Smart Homes. In Proceedings of the 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE 2015). pp. 114-119, ISBN 978-1-4673-8013-3, IEEE.Scopus - Elsevier.

Medium articles

Geospatial analysis is a rapidly growing field that combines geography, spatial data and technology to provide insights into a wide range of applications. Whether you're analyzing urban planning, disaster response, or the spread of disease, geospatial analysis provides a unique perspective on complex problems. In this blog, we will explore the latest trends, techniques and tools in geospatial analysis, and how it is changing the way we understand the world around us.

1. Spatial Programming & Remote Sensing - Spatial programming and remote sensing are essential components of modern technology and are transforming the way we understand our world.
2. Apply masks on Satellite Images - When we use Satellite Imagery, they usually provide masks for clouds, water, etc. I will show you how to apply masks to remove water pixels from satellite images.
3. Remove photo background by Python - Removing the background of an image using Python can be done using image processing and computer vision techniques. One popular method is using the OpenCV library, which provides a range of tools and functions for image processing and computer vision.
4. Watermark Photos with Python - To watermark photos using Python, you can use the Python Imaging Library (PIL) or OpenCV library. Both libraries provide tools for image processing and can be used to add a watermark to an image.
5. ChatGPT API with Python - To use the OpenAI API with Python, you will need to use the OpenAI API client library, which provides a Python interface for the API. The API client library makes it easy to interact with the API, allowing you to send requests to the API and receive responses in a convenient and easy-to-use format.