<p dir="ltr">Modern machine learning research has recently made impressive progress across various research disciplines, such as computer vision, natural language processing, also in scientific fields including materials and molecule discovery, chip, and circuit design. In photonics/optics area, conventional methods in designing and optimiza- tion typically demand substantial time and extensive computing resources, where machine learning approaches hold the potential to significantly elevate and expe- dite these processes. On the other hand, machine learning algorithms can benefit from optical/photonics based neuromorphic computing systems due to their unique strengths in power consumption and parallelization. This talk will focus on imple- menting machine learning algorithms to optimize the optical/ photonics device (ML for photonics) as well as building optical based computing system for ML applica- tions (photonics for ML): First, I will discuss my work using probabilistic generative model (CVAE) for designing nanopatterned photonics power splitter with arbitrage splitting ratio. The model is incorporated with adversarial censoring and active learn- ing to increase the quality of generated devices. Next, I will report a physics-guided and physics-explainable recurrent neural network for time dynamics discovery in op- tical resonances, which can precisely forecast the time-domain response of resonance features with a very short portion of the initial input. The model is trained in a two-step multi-fidelity framework for high-accuracy forecast. In the end, I will present our progress in developing free space reconfigurable optical computing sys- tems for scientific computing, which is an optical based general matrix multiplication (GEMM) hardware accelerator by engineering a spatially reconfigurable array made from chalcogenide phase change materials. A device-system co-design methodology was implemented for GEMM system optimization. The device has been demonstrated over a various of ML applications.</p>