Aria Haghighi, co-founder of the app Prismatic, discusses his decision to leave academia in this blog post. Aria holds a Ph.D. in Computer Science from UC Berkeley and a BS in Mathematics, and his area of focus was Natural Language Programming. It’s an interesting thought process:

At some point while at MIT, I decided to leave and do a startup because I felt my work as an academic wasn’t going to have the impact I wanted it to have. I went into academic CS in order to design NLP models which would become the basis of mainstream consumer products. I left because that path from research to product rarely works, and when it does it’s because a company is built with research at its core (think Google). This wasn’t a sudden realization, but one I had stewed on after observing academia and industry for years.

During grad school, I did a lot of consulting for ‘data startups’ (before ‘big data’ was a thing) and consistently ran into the same story: smart founders, usually not technical, have some idea that involves NLP or ML and they come to me to just ‘hammer out a model’ for them as a contractor. I would spend a few hours trying to get concrete about the problem they want to solve and then explain why the NLP they want is incredibly hard and charitably years away from being feasible; even then they’d need a team of good NLP people to make it happen, not me explaining ML to their engineers on the board a few hours a week. Useable fine-grained sentiment analysis is not going to be solved as a side project.

And his thoughts on making this tough decision:

Nearly two years later, after a lot of learning about industry and making real products, I can confidently say that I’m happy I left academia. Prismatic is a pretty tight realization of how I would’ve wanted NLP and ML to work in a startup and manifest in product. The relationship is symbiotic: the machine learning and technology is informing possibilities for the product, and conversely product needs are yielding interesting research. Various pieces of the machine learning (like the topics in a topic model) are first-class product elements. Many of the more ambitious NLP ideas I thought about during grad school will become first-class aspects of the product over the next few years.