People's visual and brand preferences encode a rich signal of identity that goes beyond clicks or text. In this talk, I present a pipeline for modeling a user’s “aesthetic identity” using Instagram likes, liked visuals, and followed brands. I show how to convert images and brand interactions into embedding spaces, condition a language model (via adapter / LoRA fine-tuning) to emulate that user’s responses, and evaluate the fidelity of that “digital twin.” You’ll leave with a reproducible architecture for persona modeling from multimodal data, along with insights into pitfalls of overfitting, privacy, and drift.