An MeV ultrafast electron diffraction (MUED) in-strument system is a unique characterization technique used to study ultrafast processes in a variety of mate-rials by a pump-probe method. Combining this tech-nology with rapid data science and artificial intelli-gence/machine learning (AI/ML) technologies in con-junction with high-performance computing can create a turnkey, automated instrument. AI-based system controls can also provide real-time electron beam optimization or provide virtual diagnostics of the beamline operational parameters. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations Such a data-science-enabled MUED facility will open this technique to a wider user base with a wider variation of experience, providing an automated or semi-automated state-of-the-art instrument, with a beamline scientist orchestrating the overall data collection pro-cess. Updates on research and development efforts pri-marily in the realm of initial studies of network con-nection between the ALCF and the Accelerator Test Facility (ATF) at Brookhaven National Laboratory are presented.

In our pursuit to tailor a precise electron bunch with a photoinjector, fine-tuning laser parameters, especially those influencing the photocathode pulse, is pivotal. Our ongoing research integrates machine learning, training neural networks with experimental data from ATF. The first approach involves generating a downstream photocurrent image to replicate the emission profile, serving as a fitness function for neural network training. The second approach employs an emittance scan during each iteration of the neural network-controlled laser profile, using magnetic optics and beam profile monitors, with calculated beam emittance as an additional fitness function. Our research aims to demonstrate the potential superiority of the neural network in achieving precise laser shaping for electron beam optimization. Leveraging real data, our goal is to reduce electron beam emittance through optimized laser profiles, underscoring the impactful applications of machine learning in advancing photoinjector technology.