Applied AI Research
Our research focuses on improving the reliability, efficiency, and interpretability of AI systems. We prioritize work that can transition from experimentation to production.
Areas of Interest
- Model evaluation and error analysis
- Data-centric AI workflows
- Multimodal learning systems
- Scalable training under resource constraints
- Responsible and explainable AI
Knowledge Sharing
We believe in sharing insights through technical writing, experiments, and open discussions. Selected research findings and articles will be published here over time.