Pinar Yanardag

Pinar Yanardag is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), part of the College of Engineering. Her research focuses on advancing human-AI collaboration, augmented creativity, and deep learning techniques with an emphasis on diffusion models and submodular optimization. She holds a Ph.D. in Computer Science from Purdue University (2016), an M.S. in Computer Engineering from Bogazici University, Turkey (2010), and a B.S. in Computer Engineering from Canakkale Onsekiz Mart University, Turkey (2007).

Her work explores interpretable directions in generative models, disentangled representations, and creative content generation. Notable contributions include frameworks like Crea for collaborative AI creativity, LoRAShop for training-free image editing, and FluxSpace for semantic manipulation in diffusion models. She also investigates bias mitigation in AI systems and cross-modal reasoning with vision-language models.

Recent trends in her publications emphasize scalable adaptation of diffusion models, contrastive learning for customization, and explainability in AI. Her research bridges theoretical advancements with practical applications in creative industries and interactive systems.

Dr. Yanardag's research is supported by grants focused on AI creativity, explainable AI, and generative modeling. She collaborates with industry partners on developing user-friendly tools for style exploration and content co-creation. Her lab actively engages in open-source projects to democratize access to advanced AI techniques.