Learn publicly. Sounds intimidating, but is likely to be one of the most efficient ways to start getting things done. It is easier and more sometimes more efficient to get feedback than to ask open-ended questions.
I haven't made a lot of progress on many of my research ideas over the past five years. Many of the concepts that excited me back then are still relatively unexplored. Every year that I wait it becomes easier though, as more of the underlying concepts become more polished, and the challenge becomes more of application and execution rather than novel algorithmic research and implementation.
Between today and the end of May 2020, I plan to study the following two papers:
There will be many threads to pull from here on, but I plan to focus on a pragmatic experimentation approach for applying these concepts to the chemical process monitoring and control.
I am also excited about the possibilities of generative design and intuitive AI, and plan to start experimenting how one could interface some of the basic algorithms with a mature simulation environment such as Elmer.