Tuna Meral bio photo

Tuna Meral

PhD Student @ VT • Generative AI, Diffusion & Autoregressive Models • Video + Multimodal Generation

I worked on making sense of images,
Now I am working on images making sense.

About Me

I am Tuna ([tu-nah]), a Ph.D. student in Computer Science at Virginia Tech, advised by Dr. Pinar Yanardag Delul, and a member of the GemLab.

My research focuses on developing controllable and interpretable generative models across images, video, and language. I design alignment objectives for diffusion and autoregressive architectures to enable efficient, user-aligned generation without post-hoc tuning.

Before joining VT, I worked across startups and industry labs, building real-time image generation services and scalable ML systems in production. This mix of applied and theoretical experience enables me to create generative models that are both research-grade and deployable.

At the core of my work is a belief that generative AI should not only create high-quality content, but do so transparently and in alignment with user goals.

Current Interests

  • Controllable generation in diffusion and autoregressive models
  • Token-level interpretability in transformers (image/video/LLM)
  • Steering of foundation models
  • Zero-shot image/video editing

Recent News

Recent Updates

Feb 2025

My new preprint ConceptAttention is available at arXiv.

Nov 2024

My new preprint MotionFlow is available at arXiv.

Nov 2024

My new preprint MotionShop is available at arXiv.

Oct 2024

I have been awarded a research grant from Deloitte to work on mechanistic interpretability for large language models.

Publications