SEMINAR: Machine-Learning Enabled Metamaterials
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Engineering the spectral and spatial properties of infrared light is essential for applications in sensing, imaging, and spectroscopy. However, achieving targeted optical responses often requires navigating extremely large parameter spaces spanning both structural geometry and material properties. Machine learning provides a powerful framework to efficiently explore these high-dimensional design spaces.
In this talk, I will discuss machine-learning enabled design of narrowband thermal light sources, as well as how anisotropic materials enable unconventional light propagation. By integrating algorithms with material-enabled photonics, these studies demonstrate new pathways toward compact infrared systems with functionalities that are difficult to realize using conventional optical design approaches.
Dr. Mingze He is a postdoctoral researcher in the group of Prof. Andrea Alù at the City University of New York. Before that, he received his Ph.D. in Mechanical Engineering from Vanderbilt University. His research leverages machine learning to combine emerging optical materials with engineered photonic structures for infrared light control. His work explores thermal emission, metasurface-based wavefront control, and polaritonic light–matter interactions. He develops compact infrared devices for sensing, spectroscopy, and imaging applications.
This seminar will be delivered online via Zoom. Further information will be provided after you complete a registration form.