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About

Emily Kamienski is a rising interdisciplinary researcher whose work bridges dynamical systems, machine learning, and assistive robotics. Her key research areas include Koopman operator theory for nonlinear dynamics, adversarial robustness in machine learning, and robotic design for elderly care. In her most impactful work, Kamienski introduced the Bootstrapped Koopman Direct Encoding (B-KDE) method, a novel Monte Carlo approach that combines Koopman operator approximation with deep neural networks to achieve high-accuracy modeling of complex systems—a contribution that has already garnered 6 citations since its 2024 publication. She also developed a model-free technique for screening adversarial data points using local Lipschitz quotient analysis, addressing critical challenges in data feature selection and adversarial robustness. Earlier in her career, Kamienski optimized gear ratios for a dual-motor actuated walker robot, creating a reconfigurable assistive device that supports both sit-to-stand transitions and walking for elderly users. Her work demonstrates a rare ability to apply rigorous mathematical methods to both theoretical problems and real-world engineering challenges, making her a promising voice at the intersection of data-driven science and human-centered robotics.