Discovering and Leveraging Latent Capabilities for Efficient Control
My research uncovers and exploits the internal representations of generative models to enable efficient, training-free control of image and video synthesis.
Building generative AI that's controllable, expressive, and accessible — from steering the latent space of diffusion models to long-horizon video generation. I direct GEMLAB at Virginia Tech.
Me & my kids on Halloween
Generative models can imagine almost anything: I make them controllable.
I am a tenure-track Assistant Professor at Virginia Tech, in the Department of Computer Science, where I lead 💎 GEMLAB. I am also a member of the Sanghani Center for AI and Data Analytics.
Prior to joining Virginia Tech, I was a postdoctoral researcher at MIT. I received my Ph.D. in Computer Science from Purdue University, where I was advised by S.V.N. Vishwanathan. I completed my M.S. under the supervision of Ethem Alpaydın at Boğaziçi.
Beyond academia, I was CEO of AI Fiction, a creative design studio specializing in AI, and I co-founded GLITCH — the world's first generative-AI clothing line. At MIT, I launched the How to Generate (Almost) Anything project and Nightmare Machine.
In a past life — long before I worked on making models behave — I was a security researcher who broke software on purpose, and a core developer of a GNU/Linux distribution.
My research uncovers and exploits the internal representations of generative models to enable efficient, training-free control of image and video synthesis.
My research develops reinforcement learning and preference-based methods to steer generation toward desired outcomes, including diversity, physical plausibility, and visual quality.
My research moves beyond one-size-fits-all generative AI with methods for personalized content generation, and democratizes generative AI for creators.
Find papers, code, datasets, demos, and more on our website.
A selection of recent peer-reviewed work.
Recent papers, awards, and recognition from the lab.
Spanning long-horizon video synthesis, physically aligned video generation, and controllable world models.
Honored to receive the NSF's CAREER award — its most prestigious recognition for early-career faculty — supporting our research and education in controllable, accessible generative AI. Award details ↗
Recognized for outstanding service on the program committee for #CVPR2026.
The lab will present four papers at CVPR 2026, including ∞-RoPE for infinite, action-controllable video and DPP-GRPO for diverse video generation.
For concept-level interpretability in diffusion models, at the CVPR 2025 Visual Concepts Workshop.
Including LoRAShop (Spotlight) and CREA, spanning efficient personalization and creative generation.
Including FluxSpace and related work on controllable, interpretable generation.
Including CLoRA, selected as a Highlight.
Including NoiseCLR (Oral) and RAVE (Highlight).
Recent keynotes and invited talks on generative AI.
Below are some of the courses I’ve recently taught.
Interested in collaboration, prospective graduate study, or industry partnership? I'd love to hear from you.