Research Areas
Pushing the boundaries of AI agent technology through innovative research in multiple domains.
Cross-Modal Understanding
Our research focuses on developing agents that can seamlessly process and understand multiple data modalities including text, images, audio, and video.
- Vision-Language Models
- Audio-Visual Speech Recognition
- Cross-Modal Retrieval Systems
Adaptive Decision Making
We develop sophisticated reinforcement learning algorithms that enable agents to learn optimal strategies through interaction with complex environments.
- Deep Q-Networks (DQN)
- Policy Gradient Methods
- Multi-Agent Reinforcement Learning
Language Understanding
Advanced NLP research enabling agents to understand, generate, and reason with human language at unprecedented levels of sophistication.
- Large Language Models
- Reasoning and Logic
- Multilingual Understanding
Advanced Training Techniques
Innovative approaches to training AI agents efficiently and effectively.
Self-Supervised Learning
Training agents to learn representations from unlabeled data through innovative pretext tasks.
Distributed Training
Scalable training systems that leverage distributed computing for large-scale agent development.
Transfer Learning
Techniques for adapting pre-trained agents to new domains and tasks with minimal additional training.