Thanh Nguyen is a Ph.D. candidate in the Computer Science Department at New Mexico State University. Thanh has been working on the LandPKS project for several years now and has taken a critical role in the development of the LandPKS app.  During his time with LandPKS, he has worked on the following:

1. Developed Data Analytics System and Prediction Model that applied Machine Learning Algorithms to get as much knowledge as possible from the soil profile, weather, and water data to build models for analyzing soil potential.
2. Developed the LandPKS Database and LandPKS API System that allowed developers and users to interact with and access LandPKS data (LandInfo, LandCover, etc).
3. Developed Web Data Portal to allow users to have the ability to access and download LandPKS data in Web Browser. In addition, the Data Portal provides tools that analyze and displays the user data.
4. Developed LandPKS mobile application that allows users to collect and interact with LandPKS data in Android and iOS.
5. Developed Big-Data processing module using Map-Reduce (Hadoop) to create accessible climate data and soil profiles for all locations in the world

Thanh Nguyen

Thanh graduated with his Masters of Computer Science at James Cook University in Australia. His thesis, entitled Data Mining in Internet Banking, is currently being used in a number of Asian banks.  His research interests include Data Mining and Knowledge Discovery (classification, clustering, association rules, and prediction models), Artificial Intelligence (knowledge representation and reasoning, planning, logic programming, answer set programming and Web semantics – Services Composition), Machine Learning, and Collective Intelligence (recommendation system, discovering groups, searching and ranking, collaborative filtering, document filtering, generative modeling, advanced classification, etc.) and Big-Data processing.  His primary research now focuses on Automation Web Services Composition in Web Semantics. He is developing a completed end-to-end AI system to collect requirements from users in Natural Language and explore workflows that can satisfy the user’s requirements automatically. After the workflow is achieved, our system is able to execute each Web Service component in a workflow sequence in order to achieve the goal.