Projects old

Distributional Alignment and Diversity Control for Generative Models via Optimal Transport and Uncertainty Quantification

Principal Investigators & Key Members:

We will build methods that gently nudge a generative model’s behavior when it is generating words so its outputs match a desired domain or safety profile while staying useful and varied. The key tool is optimal transport, a mathematical way to align distributions. We pair it with calibrated confidence and safe refusal. We will test these ideas in Vietnamese healthcare and education, release open source tools and benchmarks, and collaborate with VinMec and VinSchool to increase the reliability and value of AI systems in real use.

BioDroneX: AI-Enhanced Bio-Inspired Drone for Adaptive Multi-Environment Missions

Principal Investigators & Key Members:

Prof. Nguyen Xuan Hung

BioDroneX is an AI-enhanced, bio-inspired drone with computer vision, designed to operate across air, land, and water using adaptive structures, advanced materials, and intelligent navigation. It enables safe access to hazardous or hard-to-reach areas for disaster response, environmental monitoring, smart agriculture, and industrial inspection. By combining biological principles, AI, and advanced 3D printing, the project aims to create autonomous cross-domain drones that reduce human risk and enhance societal resilience.

Adaptive Reinforcement Learning for Scalable Traffic Control under Certainty

Principal Investigators & Key Members:

Asst. Prof. Pham Duc Thinh

As urban traffic transportation faces increasing challenges related to traffic congestion, delays, and emissions. Efficient management of urban traffic networks is essential to enhancing operational efficiency, reducing travel time, and minimizing environmental impact. Despite advances in real-time and adaptive control algorithms, current solutions often fall short due to challenges of the real-world traffic network.This research seeks to develop a control policy that leverages advanced adaptive algorithms for rapidly adapting to environment changes and hierarchical multi-agent reinforcement learning for efficiently handling large-scale coordination complexity. The research supports Smart City goals to reduce congestion, emission and enhance mobility.

On-Device Small Language Models: Toward Private, Efficient, and Agentic Edge Intelligence

Principal Investigators & Key Members:

Asst. Prof. Huynh Thanh Trung

This project aims to develop a new generation of intelligent recommendation systems based on Agentic Artificial Intelligence (AI) that can understand user needs, learn from feedback, and provide transparent explanations for their suggestions. Current recommender systems often operate as “black boxes,” limiting user trust and adaptability. By integrating large language models (LLMs) with reasoning, planning, and self-improvement capabilities, the project will enable more adaptive and explainable personalization. Applications include education, healthcare, and sustainable e-commerce. The anticipated outcomes are enhanced trust, efficiency, and fairness in digital recommendation technologies, contributing to the advancement of ethical and human-centered AI systems.

Mechanistic Understanding and Control of Multi-modal LLMs

Principal Investigators & Key Members:

Asst. Prof. Khoa D. Doan

Multimodal LLMs that can solve complex visual, mathematical, programming, and discovery tasks still make simple mistakes - e.g., misjudging spatial relations, failing at genuine discovery, or lacking cultural awareness - limiting safe use in healthcare, finance, and low-resource/culturally diverse settings such as Vietnam. This project will build real-world benchmarks to reveal these weaknesses, explain why they occur using mechanistic analyses, and develop lightweight inference-time control and data-efficient fine-tuning methods to fix them without costly retraining. We will release open datasets, tools, and guidelines to enable safer, more inclusive AI, cut energy use through efficient training, and unlock trustworthy applications that support local innovation and economic growth.