Morteza

🧠 About Me

I’m a Principal Scientist at NVIDIA Fundamental Generative AI Research (GenAIR) and a Visiting Researcher at Stanford University.

My work focuses on the algorithmic foundations of generative AI, including diffusion/flow models and physics-aware generation. I aim to bridge theory and practice to advance generative modeling for applications in content creation, graphics, digital twins, and science.

At NVIDIA, I lead research on diffusion/flow-based foundation models for scientific AI, such as digital twins for climate and weather simulations. Our weather foundation models have been integrated into NVIDIA products and adopted by major customers like The Weather Channel.

πŸ“ Bio

I earned my PhD in Electrical Engineering with a minor in Mathematics from the University of Minnesota, advised by Georgios Giannakis, focusing on statistical learning and optimization.

I was a Visiting Scholar at UC Berkeley’s RISE Lab with Michael Mahoney, and later a Postdoc and Research Associate at Stanford, working with David Donoho, John Pauly, and Shreyas Vasanawala on inverse problems and generative models.

I serve as an IEEE SPS Distinguished Industry Speaker, a member of the IEEE Computational Imaging Technical Committee, and am a recipient of the IEEE SPS Young Author Best Paper Award.

πŸ“° News

πŸ’Ό Opportunities

I am actively looking for PhD interns and collaborators interested in advancing generative modeling. If you’re excited about building principled and impactful generative models, get in touch:
πŸ“§ mmardani@nvidia.com

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