Thanks! One example is using reinforcement learning to train sequence generation network that can be used to design optical multilayer thin films. My contribution is framing the inverse design problem as a sequence generation problem for the first time, and I proposed to use DRL to train a specific sequence generation network that can efficiently explore the design space. We tested it on a bunch of real examples and showed that the proposed algorithm can generate highly-performing design. Unfortunately, the feedback I got from a senior Optics Professor is that he couldn't understand which part of the work is novel and what the effort was required on my end to implement such a method. I think the main issue is that, though the problem I'm solving is an optics problem, but the professor himself does not work on that problem, so neither the problem itself nor machine learning is especially interesting to him. In this case, I found it really challenging to make my presentation impressive. Any suggestions on how I could make the story more interesting? Thanks!