Project & News
Ongoing Researching Projects
Custom AI Chips & Compiler Optimization for Heterogeneous Computing Circuits
This project focuses on heterogeneous computing and intelligent chip compiler optimization for applications such as intelligent control, signal processing, tensor computing, edge perception, security-oriented computing, and autonomous driving. By integrating architecture co-design, functional verification, compiler mapping, and deployment optimization, it aims to improve design efficiency, task mapping capability, and large-scale deployment support for domestic intelligent computing platforms.
The project further develops technical methods spanning task characterization, hardware abstraction, scheduling, dataflow organization, compiler mapping, and prototype implementation, supporting efficient, secure, and application-oriented deployment on heterogeneous chip platforms.
Heterogeneous chip co-design, tensor compilation, and deployable AI computing platforms
Multimodal Brain Function Screening and AI-Driven Rehabilitation
This project focuses on early screening and digital rehabilitation for brain function disorders such as Alzheimer’s disease (AD) and autism spectrum disorder (ASD). It integrates portable EEG, 3D eye tracking, EMG, electrodermal activity, and other physiological signals with AI-based multimodal analysis to support a unified pathway from convenient screening and precise intervention to closed-loop regulation.
Building on self-developed immersive 3D visual stimulation and eye-tracking devices, low-to-high-channel EEG reconstruction, cross-device adaptation, and multimodal fusion, the project further explores AIGC-driven personalized story/video training and EEG-guided music intervention. The goal is to enable robust community screening and home-based cognitive rehabilitation with personalized content generation, real-time feedback, and long-term follow-up.
Portable EEG, 3D eye tracking, AIGC training, and closed-loop brain function screening and rehabilitation
Multimodal Affective Computing and Intelligent Analysis
This project focuses on multimodal affective computing and intelligent analysis for applications such as intelligent interaction, mental health, content understanding, and service intelligence. By modeling audio, video, text, and contextual signals, it aims to improve dynamic emotion perception, deep semantic understanding, and robust multimodal fusion in real-world scenarios.
The project builds on sustained research in multimodal representation learning, feature alignment, and robust modeling, and supports practical deployment in areas such as elderly care, postoperative monitoring, smart terminals, and related intelligent service systems.
Audio, video, and text understanding for emotion analysis, mental health, and intelligent services
Computational Spectral Imaging, Multi-Source Fusion, and High-Dimensional Visual Sensing
This project focuses on computational spectral imaging, multi-source image fusion, and perception-oriented analysis for high-dimensional visual sensing in complex scenarios. It integrates imaging system design, reconstruction algorithms, and application validation to improve the acquisition, fusion, and intelligent use of spatial, spectral, and cross-modal information for remote sensing, industrial inspection, and related applications.
Building on research in compressed spectral reconstruction, hyperspectral super-resolution, and multi-scale image fusion, the lab has established integrated acquisition and processing platforms that support end-to-end research from front-end sensing to back-end analysis.
Imaging systems, reconstruction algorithms, and cross-modal perception for complex visual scenarios
AI-Driven Precision Nutrition for Chronic Diseases
This project focuses on precision nutrition assessment, dietary behavior sensing, and intelligent recommendation for health management and chronic disease intervention across the life cycle. By integrating AI, mobile internet, nutritional medicine, and multimodal health data, it aims to support personalized dietary recommendation, nutrition analysis, and digital intervention for chronic disease prevention and management.
With the Kangshi app and clinician management platform, the project combines image recognition, voice input, wearable data collection, and personal health records to enable convenient nutrition tracking, report generation, online consultation, and long-term intervention support.