Thai-Hoc Nguyen | Institute of Applied Science and Technology (IAST)

About Me

I am an AI Researcher at the Institute of Applied Science and Technology (IAST), specializing in the intersection of Computer Vision, Speech Processing, and Multimodal Learning.

With a strong foundation in Data Science from ICTU, my work focuses on bridging the gap between theoretical research and real-world deployment. I am passionate about architecting scalable AI solutions for high-impact domains, particularly in Medical Diagnostics (Healthcare) and Education Technology.

For more details, please view my Curriculum Vitae or contact me at thaihoc.it02@gmail.com.

Research Interests

My research aims to build Scalable & Reliable AI Systems, covering the full lifecycle from data engineering to production deployment:

Speech & Multimodal Intelligence: Advancing SOTA models (Wav2Vec2, HuBERT, LLMs) for Mispronunciation Detection (MDD) and natural language understanding.
Medical Computer Vision: Developing deep learning frameworks for high-precision 3D image analysis and automated medical screening.
AI Engineering (MLOps & Big Data): Optimizing end-to-end workflows using Hadoop/Spark and MLOps best practices to ensure model robustness in production.

Honors & Awards

[2024] Outstanding Research Student Award, ICTU.
[2024] Student of 5 Merits (Regional Level), Thai Nguyen University (TNU).
[2023] Student of 5 Merits (University Level), ICTU.
[2023–2025] Academic Excellence Scholarship, ICTU.

Publications

ICTA

An automatic machine learning based customer segmentation model with RFM analysis

Xuan-Thi Tran*, Thai-Hoc Nguyen* (* equal contribution)

International Conference on Advances in Information and Communication Technology (ICTA), 2024.

PDF Code Project Page
Keywords. Customer Segmentation; RFM; K-means Clustering; Hadoop; Spark

ICTA

A Study on Ensemble Learning for Cervical Cytology Classification

Van-Khanh Tran*, Thai-Hoc Nguyen*, Xuan-Lam Dinh, Chi-Cuong Nghiem (* equal contribution)

International Conference on Advances in Information and Communication Technology (ICTA), 2024.

PDF Code Project Page
Keywords. Ensemble Learning; Cervical Cancer Cytology; Deep Learning