Build more together
The Machine Learning Research Group focuses on two main research directions: traditional ML and modern DL. The former concentrates on established topics in the field of machine learning/AI, such as CV, NLP, RL, ... while the later focuses on emerging topics such as foundation models, Federated Learning, ...
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This lab focuses on chip design and manufacturing to enhance performance, optimize computational processing in microprocessors for AI tasks, and explore the application of AI in chip design and production processes.
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The research in this group encompasses AI research and applications in the fields of biotechnology, food technology, and health sciences. The research topics are divided into two major areas: the design and production of smart medical devices and the development of smart medical software solutions.
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This lab focuses on AI applications in environmental science and climate change, including energy management, pollution control, green agriculture, clean energy, and waste management.
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This research team focuses on studying automation and intelligent control systems, where AI is applied to automate production processes and make control decisions. Key research topics include: Autonomous Vehicles, Smart Cities, Intelligent Transportation Systems.
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Leveraging the existing strengths of HUST, this team will promote AI research and applications to drive digital transformation in the education sector. Some specific research topics include: Data Analysis and Decision Support, Intelligent Content Creation, Communication Support.
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Crafting Quality, Enriching Lives
Fi-Mi relies on lightweight air quality monitoring devices mounted on the buses. Fi-Mi can broaden the monitoring regions and provide fine-grained air quality information while significantly reducing costs compared to the existing approaches.
VAIPE aims to build an intelligent healthcare system to assist users in collecting, managing, and analyzing their health-related data; enabling users to collect heterogeneous data, and support users.
This project introduce an AI-powered digital twin platform for proactive carbon farming management in the Thanh Hoa Province. It provides a dynamic digital twin platform for a complete carbon farming lifecycle.
Powered by Digital Transformation and AI
Delivering Essential Data to Solve Real-World Challenges 
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This dataset consists of a real-world, large-scale pill image. All these datasets are established under real-world scenarios and then normalized to train machine learning systems.
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This dataset consists of an Open Dataset of Prescription Images collected from large Vietnamese hospitals with various templates.
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This is a comprehensive large-scale PET/CT dataset collected from hospitals. The dataset consists of 2,028,628 paired CT-PET images from studies of 3,454 patients, designed to cover a wide range of anatomical regions.
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We provide a new large-scale, multi-view sign language recognition dataset spanning 1,000 glosses and 30 signers resulting in over 84,000 multi-view videos.
Professor Sushil Varma at University of Michigan, Ann Arbor, is currently seeking highly motivated PhD students to join his research group. Professor Sushil Varma's research focuses on: Theoretical machine learning Reinforcement learning Queuing theory Application Details How
