Our research focuses on improving the reliability, efficiency, and interpretability of AI systems. We prioritize work that can transition from experimentation to production.
Our research agenda is driven by practical challenges in the AI field. We focus on model evaluation and error analysis to build more robust systems, data-centric AI workflows that prioritize information quality, and the development of multimodal learning systems. Additionally, we explore scalable training techniques under resource constraints and the implementation of responsible, explainable AI practices.
We believe in sharing insights through technical writing, experiments, and open discussions. Transparency in research leads to better engineering outcomes. Selected research findings, white papers, and technical articles will be published here over time to foster collaboration and community growth.