About me
As an experienced Data Management and Remote Sensing, I bring a deep understanding of geospatial data handling, remote sensing techniques, and machine learning applications tailored to environmental science. My expertise encompasses UAV and LiDAR data acquisition, data curation, and analysis, underpinned by a commitment to the principles of inclusivity, collaboration, and sustainability. With a track record of supporting academic research and education through advanced technical services and laboratory management, I am adept at fostering an environment conducive to learning and innovation within the School of Earth and Environment.
Experiences
- Proficiency in GIS, remote sensing, and geospatial data analysis.
- Skillful utilization of diverse software and tools such as QGIS, ArcGIS, GEE, among others.
- Hands-on experience with a range of sensors and equipment like UAV eBeeX, Pix4D, LAI-2200C, Dualex Force-A, GPS devices, etc.
- Developed workflows and Python scripts for imagery downloading, processing, and modeling. Additionally, created processes for UAV data collection, processing, and modeling.
- Proficient in organizing and managing workshops, as well as adept at report and research paper writing.
- Possesses extensive knowledge in GIS and remote sensing, with familiarity in using satellite and drone imagery. Skilled in sourcing Sentinel imagery from multiple platforms including SciHub, Google Cloud, and GEE. Experience includes geospatial data analysis encompassing imagery acquisition, processing, analytics, and modeling.
Responsibilities:
- Prepared equipment, materials, products, and specimens required for experiments.
- Gathered and organized field samples, including gas and soil, to support ongoing research endeavors.
- Conducted cleaning and filtration procedures on collected samples, ensuring their readiness for analysis.
- Implemented and executed field experiments, overseeing the setup and monitoring of procedures.
- Maintained and managed laboratory facilities, ensuring their functionality for both research and educational purposes.
Responsibilities:
- Generating PDF reports.
- Enhancing performance, improving user experience (UX), and developing the user interface (UI).
- Designing interactive data visualizations.
- Implementing AWS integration and setup.
- Conducting data cleansing processes.
- Performing statistical analysis and applying machine learning techniques.
- Establishing connections between the backend and React frontend.
- Gathering data from primary and secondary sources while maintaining data systems.
- Processing data by filtering, cleaning, and visualizing raw data to generate actionable results.
- Identifying, analyzing, and interpreting patterns or trends within intricate datasets.
- Utilizing statistical techniques to analyze data and produce comprehensive reports.
- Employing Neural Networks and Recurrent Neural Networks (RNN) to model cell cycles in biology, adapting and fine-tuning network parameters for training and testing data.
- Conducting software coding (Python, MATLAB, and Excel) for data analysis and visualization purposes.
- Software coding (Python, Matlab and Excel) for Data Analysis and Data Visualization.
- Analyzing outcomes and composing research papers and reports based on the findings.
Project: Autonomous Self-repair Systems
- Optimized Neural Networks to simulate biological tissue and organism self-repair processes.
- Enhanced Hopfield Neural Networks to facilitate the restoration of organism functions and structures.
- Proposed three distinct self-repair algorithms tailored for tissues, simple organisms, and more complex organisms.
- Visualized outputs, programmed, and executed simulations using Python.
- Documented and summarized results in reports, research papers, and a thesis.
- Contributed to the review process of research papers for various journals and conferences.
Project: Neural Network Analysis of Biological Tissues
- Managed data entry, migration, and preparation for analysis purposes.
- Employed Hopfield Networks to retain memory in various connection topologies related to biological tissues.
- Investigated different memory types and their impact on potential tissue function, considering the influence of connection topology and the durability of memory in tissues.
- Explored discrete and continuous states within the Hopfield Networks.
- Conducted software coding for both data analysis and data visualization.
- Formulated critical questions, conceptualized ideas, and gained a comprehensive understanding of neural network modeling in relevance to the project.
- Summarized findings in reports and research papers.
Professional Services
Reviewer:
- The International Conference on Intelligent Systems and Data Science (ISDS) 2023.
- 16th Asian Conference on Intelligent Information and Database Systems (ACIIDS) 2024.
Best publications
- 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.
- 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.
- 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.
- 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.
- 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.