About Maksim Makarenko Maksim Makarenko Ph.D. Student, Electrical and Computer Engineering Maksim Makarenko is a Ph.D. student at the King Abdullah University of Science and Technology, where he is currently studying Electrical & Computer Engineering and doing research in the Primalight research group under the supervision of Andrea Fratalocchi. His research interest is developing algorithms for Machine Learning and Optimization implemented into optical hardware. Based on his Ph.D. study, he co-founded the Pixeltra startup company, where he is currently employed as Chief Technical Officer. Before KAUST, he obtained his MS degree in Physics at the Novosibirsk State University. Events Presented Events Nov 20 - Nov 26, 2022 Machine learning in hardware via trained metasurface encoders: theory, design and applications Maksim Makarenko, Ph.D. Student, Electrical and Computer Engineering Nov 23, 10:00 - 12:30 B2 L5 R5209 machine learning artificial intelligence photonics In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).
Machine learning in hardware via trained metasurface encoders: theory, design and applications Maksim Makarenko, Ph.D. Student, Electrical and Computer Engineering Nov 23, 10:00 - 12:30 B2 L5 R5209 machine learning artificial intelligence photonics In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).
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