Drew Endy's a professor at Stanford and synthetic biology pioneer. I discovered him via this talk he gave at a hacker conference about programming DNA. He unfortunately doesn't have a ton of recent public content, but I recommend watching that talk I linked and reading this interview. Two of my favorite quotes of his are (from memory so paraphrased), "biology is nanotech that works" and "what's the most advanced thing on a person's desk. It's not their iPhone; it's the plant they keep there."

I think Drew (also) satisfies most of your desiderata.

• He thinks biology is overly focused on science and should focus more on maturing as an engineering discipline by building reusable modular parts.
• (See the prior bullet, which was pretty novel to me when I first heard it.)
• Drew caused me to update towards thinking that synthetic biology was more promising as a solution to problems beyond health, e.g. for producing cheaper materials, democratizing production, and producing more sustainable energy. He also caused me to update towards the view that synthetic biology was making very rapid progress.
• Pushing forward synthetic biology seems pretty interesting to me. Drew also created the iGEM competition, which has enabled 10s of thousands of students to participate in synthetic biology projects.
• Drew created iGEM and generally advocates for learning by doing as an alternative to just studying in the abstract.

David Ha's (Twitter, blog) one of the more interesting deep learning researchers I follow. He works loosely on Model-based Reinforcement Learning and Evolutionary Algorithms, but in practice seems to explore whatever interests him. His most recent paper, Weight Agnostic Neural Networks looks at what happens when you do architecture search over neural nets initialized with random weights to try and better understand how much work structure is doing in neural nets.

I believe David satisfies all of your desiderata.

• Often disagrees with the consensus on various questions around what are promising AI research directions.
• Consistently produces original deep learning research that makes me go "wow, I never would have thought of that.".
• Has caused me to update on which aspects of neural nets are important for performance.
• Is definitely effective as a researcher (see above).
• Writes much clearer than average papers and also often uses visual aids and blog posts to explain his and others' work (this is for the last two together).