O. Nejadseyfi

My research focuses on computational design and mechanics from a stochastic perspective. Variability in manufacturing processes, material properties, and operating conditions fundamentally limits the predictive accuracy of deterministic physics-based engineering models. Ignoring these sources of uncertainty can lead to unreliable designs, non-reproducible performance, and misleading optimization outcomes.

I develop computational frameworks for stochastic modeling, uncertainty quantification, and robust design optimization, with a strong focus on design and sustainable manufacturing processes. My work spans both additive manufacturing and conventional manufacturing technologies, such as metal forming, enabling uncertainty-aware decision-making across the full materials-design-manufacturing chain. These frameworks are coupled with experimental validation, where measurements are used to identify uncertainty sources, calibrate and update models, and assess the reliability of computational predictions.

In parallel, I develop cross-disciplinary, open-source research and educational tools. One example is Robustimizer (www.robustimizer.com) for robust optimization and uncertainty quantification. Through its graphical user interface, Robustimizer supports both academic and industrial partners by lowering the barrier to uncertainty-aware design and process optimization.