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Computational Optics. We develop optical platforms as hardware for executing machine learning tasks such as classification and regression of data. The data is imprinted on optical signals and is processed using both linear optical processes such as diffraction and interference as well as non-linear optical processes.
Machine learning for photonics. We employ machine learning tools to solve different problems in photonics, such as control of light fields and optical data processing geared towards applications in imaging and health care .
Optical interaction with biological neural networks. We research the use of applying optical fields to control the functionality of biological neural networks with applications in development of opto-biological computers and brain-machine interfaces.
Beam Optics. We develop theoretical and experimental methods to construct exotic optical beams with tailored parameters. Such beams are then used in our lab for optical tweezing, microscopy and optical computing.
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