Prof. Arash Arami

March 20th, 2026 – 12:00-1:00pm, EC4-2101A

Understanding complex physical systems—from engineered materials to human movement—requires models that capture structure, variability, and cross-scale interaction. In this talk, I will present our group’s work on data-driven modeling across manufacturing, biomechanics, and human–robot interaction. I will first highlight the use of generative and image-based models to synthesize realistic microstructures and predict material properties, enabling exploration and optimization. I will then present approaches for modeling and predicting human gait, including detection and prediction of pathological behaviors such as freezing of gait in Parkinson’s disease. Finally, I will describe learning-based methods for real-time movement estimation and human–robot interaction in wearable robotics, illustrating how these models support adaptive and personalized control.