Projects old

Long-Chain Reasoning of LLM for Diagnostic Knowledge Tracing and Image-Text Fusion

Principal Investigators & Key Members:

This project develops an AI-powered diagnostic system for gastrointestinal diseases, integrating multimodal data and clinical knowledge to enhance early cancer detection. By combining endoscopic imaging with real-time retrieval of medical guidelines and chain-of-thought reasoning, the system generates accurate, explainable, and structured diagnostic reports. It aims to reduce diagnostic errors, improve healthcare accessibility, and support clinicians with transparent decision-making tools. The research contributes to precision medicine and equitable healthcare by delivering scalable, trustworthy AI solutions for clinical environments.

AI-Enhanced Soft Robotic Endoscopy System for Precision Colonoscopy and Real-Time 3D Navigation

Principal Investigators & Key Members:

Thai Mai Thanh, PhD

Gastrointestinal (GI) cancers remain major global health challenges, with early detection through colonoscopy crucial for survival. Conventional endoscopes are rigid and operator-dependent, causing discomfort and limiting precision. This project develops an AI-Enhanced Soft Robotic Endoscopy System that integrates AI-driven design automation and SLAM-based real-time 3D navigation to enhance safety, flexibility, and diagnostic accuracy. It advances soft continuum robotics, foundation model–based design, and visual–inertial SLAM for intelligent, adaptive navigation. The project aligns with healthcare innovation priorities, aiming to improve early GI cancer detection, reduce patient trauma, and accelerate the digital transformation of medical robotics through clinical and industrial collaboration.

3-D MRI-informed AI for Hand Bone 2-D X-ray Image Enhancement

Principal Investigators & Key Members:

Prof. Saeid Sanei

Carpal (wrist) bone fracture is very popular among ageing community, patients suffering from seizure, stroke, various degenerative brain diseases, and athletes due to impact or falling. The diagnosis procedure includes taking an X-ray and upon detecting the hand bone fracture, the treatment follows. In many cases the subject is sent home due to invisibility of fracture and in over 20% of the cases the patient returns to hospital with more pain and fracture symptoms within 2-3 weeks. At this stage, the doctor asks for taking 3-D structural MRI which provides approximately 100% diagnosis accuracy. However, x-ray is cheap and accessible while MRI is expensive and requires queueing. Therefore, in this project, the plan is to create an intelligent system which learns from pairs of X-ray-MRIs on how to enhance individual X-rays to have the same information visible in MRIs. Such a system alleviates the need for patient return, enhances the diagnosis and considerably reduces the cost for taking MRI. The application can be easily extended to other bone fractures.

Semantic EEG-to-Speech/text translation with visual feedback and large-vocabulary dictionary

Principal Investigators & Key Members:

Prof. Saeid Sanei

In this project the area of brainwave-to-speech translation, which is in its infancy, will be significantly developed to ensure error-free and semantic speech production. This is performed through visual feedback allowing the subject to decide among the most-likely responses suggested by the system and accept the correct word to be spoken. The major advantage is generating speech for those with full or partial speech disabilities (e.g. autistics, suffering stroke or brain degenerative diseases) covering more than 2.2% of Vietnam population.

Physics-Guided Scientific Machine Learning for Trustworthy Mechanical and Physical Systems

Principal Investigators & Key Members:

Nguyen Vu Linh, PhD

This project develops a new generation of Scientific Machine Learning models that combine the rigor of physics with the adaptability of artificial intelligence. By embedding physical laws into AI systems, the research aims to create models that are accurate, interpretable, and efficient for complex mechanical and physical systems such as robots, materials, and industrial machines. The outcomes will enhance predictive capability, safety, and energy efficiency while supporting Vietnam’s Industry 4.0 vision and advancing VinUni’s leadership in trustworthy, physics-guided AI research.

Advancing Humanoid Robot Learning through Perception Modeling, World Model, and Biomimetic Machine Intelligence

Principal Investigators & Key Members:

Assist. Prof. Le Duy Dung

Current humanoid robots’ capabilities are limited: they excel in short-term tasks but struggle to thrive in long-horizon ones with high levels of uncertainty. Their safety protocols are usually not human-aware, and training them requires extensive expert demonstrations, incurring high budget, time, and effort. Our proposal addresses these challenges by introducing brain-inspired predictive perception modeling, enabling robots to learn internal representation of the world and imagine future outcomes before acting. This further enhances robots’ robustness in uncertain scenarios and improves their safety measures. Therefore, our research will lay the foundation for safer, more versatile, and more trustworthy humanoid robotics.