Mohammad Kamani is a Senior AI/ML Researcher with a Ph.D. from Penn State and eight years of experience designing efficient algorithms for federated learning, distributed optimization, model compression, and Edge AI. He has transitioned research breakthroughs into industry impact across roles at Wyze, AMD, and now NVIDIA, focusing on resource-constrained inference and training for real-world systems. His academic work introduced novel multi-objective optimization methods for bias mitigation—Targeted Data-driven Regularization and Pareto Descent Optimization—that outperform prior approaches and generalize beyond fairness tasks. Mohammad combines deep theoretical expertise with applied engineering, routinely tackling domain adaptation, imbalanced and robust learning, and knowledge distillation in production settings. Based in Kirkland, WA, he is known for bridging collaborations between research and product teams to deliver high-quality, deployable ML solutions. An uncommon strength is his track record of turning provable optimization ideas into compact models suited for edge devices.
8 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Information Sciences and Technology, Doctor of Philosophy - PhD, Information Sciences and Technology at Penn State University
Master's degree, Electrical, Electronics and Communications Engineering, Master's degree, Electrical, Electronics and Communications Engineering at Sharif University of Technology
Contributions:12 commits, 2 pushes, 1 branch in 3 years 2 months
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Mohammad Kamani - Senior AI ML Researcher at NVIDIA