Building the next generation of generative AI that people can control, align, and personalize—from long-horizon video to interactive worlds. I lead GEMLAB at Virginia Tech.
Generative models can imagine almost anything: I make them controllable.
I am an Assistant Professor of Computer Science at Virginia Tech, where I lead the Generative Modeling Lab (💎 GEMLAB). My research focuses on control, alignment, and personalization in generative AI, with an emphasis on video generation and interactive world models.
Since I joined Virginia Tech in Fall 2023, my lab has produced 25+ papers at top conferences including CVPR, NeurIPS, and ICCV/ECCV. My research is supported by an NSF CAREER award on generative cinematography and by industry partners including Google Research, Adobe Research, Snap, and Qualcomm. I was also recognized as an Outstanding Area Chair for CVPR 2026.
I received my Ph.D. in Computer Science from Purdue as a Fulbright Fellow. Before joining Virginia Tech, I was a postdoctoral researcher at the MIT Media Lab and a visiting professor at Boğaziçi University.
You can reach me at [email protected], or find me on X@PINguAR.
I study how generative models represent content, motion, and temporal context, and turn these insights into efficient methods for control. My work spans interpretable representations, training-free interventions, and redesigned memory mechanisms for controllable images, long-horizon video, and interactive worlds.
I develop reward- and preference-guided methods, including reinforcement learning, that steer generation toward desired outcomes such as diversity, physical plausibility, and visual quality. These methods guide fixed generators through their inputs or adapt the generative models themselves.
I personalize generative models to both what people want to depict and how they want it to look. My work spans concept customization and individual preference learning, enabling generation that reflects a user's subjects, visual tastes, and creative goals.
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, supporting our research and education in controllable, accessible generative AI. Award details ↗
Recognized for outstanding service on the program committee for #CVPR2026.
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.
Spanning training-free dynamic view synthesis to personalizing content, including a Spotlight selection.
Controllable and personalizable image editing and generation.
Including CLoRA, selected as a 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.