I am Xindi Wu, a 4th year Ph.D. student in Computer Science at Princeton University, advised by Prof. Olga Russakovsky. Previously I was in Master of Science in Computer Vision at Carnegie Mellon University. I was very fortunate to be advised by Prof. Deva Ramanan and work with Prof. Min Xu and Prof. Haohan Wang and Prof. Kris Kitani. I was admitted to the Honors Youth Program at Xi'an Jiaotong University.

My research focuses on data for efficient world models and multimodal machine learning, developing data-centric approaches that trace model performance back to training data composition and introduce data-driven capabilities to build more scalable and capable multimodal systems.

My work sits at the intersection of three core pillars:

• Compressibility (how efficiently models acquire, store, and leverage information across data and memory) [WorldTrace, REFINE, VL data selection, VL dataset distillation]
• Compositionality (how well models generalize to novel combinations of concepts) [COMPACT, ConceptMix]
• Controllability (how precisely we can control generation) [Motive, Corgi]

I am happy to collaborate and answer questions about my research, feel free to send me an email. I especially encourage students from underrepresented groups to reach out.