GPU Nodes
144 x NVIDIA GPU
Research
From foundational algorithms to real-world deployment, MMAI Lab builds vision and learning systems that connect core research, multimodal intelligence, robotics, and biomedical impact.
Infrastructure
Research Servers
Shared Servers
Robotics Platform
UR5e
Research Area
Abstract
Computer Vision and Learning Algorithms
We study robust vision and learning algorithms for recognition, representation, and adaptation at scale. This area investigates scalable visual representations and adaptation strategies that remain reliable across data shifts, label scarcity, and long-tail conditions. Foundational vision and learning methods for robust generalization
Visual RecognitionLarge-scale ModelMeta Learning
Core Architecture
Designing efficient SOTA architectures with manageable computational cost
Representation Learning
Reinforcing feature representation through token pooling, hashing, and continual learning
Vision-Language Model
Adapting large VLM to new tasks and domains through parameter-efficient transfer learning
Abstract
Efficient Learning for LLMs
We develop practical methods for making large language models lighter, faster, and more efficient to deploy. This area focuses on making large language and multimodal models lighter, faster, and easier to deploy while preserving reasoning quality. Efficient and practical multimodal intelligence for deployment
Model CompressionEfficient LearningSystem-level Optimization
Pruning
Removing redundant parameters and structures from large models while preserving performance
Quantization
Reducing numerical precision of weights and activations to lower memory and compute cost
Efficient Transfer Learning
Adapting pretrained models to new tasks and domains with minimal additional training cost
Abstract
Robot Learning
We build robot learning pipelines that connect visual understanding to efficient and reliable robotic behavior. This area connects visual-language understanding to robot policies for practical perception-to-action pipelines in real environments. Embodied AI from perception to reliable robot behavior
VLAEfficient Robotics3D Recognition
Multimodal World Model
Learning world dynamics to understand causality and dynamics in physical environments
Robust & Generalizable VLA
Improving robustness and generalization across diverse tasks, environments, and embodiments
Speech & Sound VLA
Grounding speech instructions and sound for reactive robot control instead of relying on text
Abstract
Industrial and Medical AI
We apply AI methods to industrial reliability and medical analysis, with a focus on high-impact real-world use cases. This area applies machine learning to industry and healthcare with emphasis on reliability, interpretability, and robust validation in real use cases. Trustworthy AI for industrial reliability and medical analysis
Battery AIFault DetectionMedical AI
Medical Image Diagnosis
Building diagnostic models robust to long-tail distributions and domain shift problem
Real-World Visual Tracking
Applying vision systems to tracking, recognition, and defect detection in real-world environments
Battery Prognostics
Predicting battery degradation and remaining useful life by fusing heterogeneous signals