Although faculty members transition from industry to academia (and vice-versa), it’s rare to go back and forth. How does each setting help a researcher grow, and what skills are critical in both environments? Sam King offers his insight.
Five years ago, I left my tenured position in computer science at the University of Illinois at Urbana-Champaign to push myself intellectually and professionally in industry. During these years, I started a company (Adrenaline Mobility), sold my company to Twitter, worked as a software engineer, managed a two-person team, managed a 25-person organization, battled overseas fraudsters and fake accounts, and led a nine-month project (an eternity in industry) that ended up being the largest growth initiative in the history of Twitter.
Now, I’m back in academia — at the Department of Computer Science at the University of California, Davis. Why?
My transition to industry began when I was on sabbatical from Illinois to work on my startup in California, where we were working on ways to make it easy for other programmers to add encryption to their apps. The startup was born out of my academic research on digital security, and, after a couple of years, Twitter bought it. Suddenly, I had a decision to make: work at Twitter in San Francisco, or stay in academia.
In the end, I left. Although I enjoyed the work and the stability, I wanted to step outside my comfort zone, and I wanted to experience the entire process of taking an idea all the way to production — instead of forgotten in a paper as so often happens in academia. I wanted to have impact, too: software developed in a research setting is used rarely outside of academia; industry provided an opportunity for other people to use the things I made.
I spent two years at Twitter working on preventing fake accounts and improving security, before Lyft (a North America-only Uber competitor) recruited me away to help with fraud, where I spent another two years.
What I learnt
One of the unique aspects of industry that I enjoyed the most was the fast pace. At Twitter and Lyft most of my teams were focused on security. Both apps face active and worthy adversaries that regularly try to hack Twitter and Lyft accounts — or create fake accounts which could be used to make money. At Twitter, they could use compromised or fake accounts to send spam, and at Lyft they could use them to get free rides. In other words, I got to fight against bad guys. The faster we moved, the more successful we were in protecting our users and systems.
In industry, you are always interacting with others, leading or building teams. There is more value in being able to manage people, having technical breadth and being able to see — and adapt to — a big-picture vision. You have access to a huge number of users, and the solutions you devise must be straightforward and simple to implement because they have to be carried out at a large scale. As a result, the impact you can have is tremendous.
For example, signing up for a new account is a deceptively complex process at Twitter. From a user’s perspective, it means filling out three fields in a form and pressing a button. Behind that, Twitter uses vast, intelligent infrastructure to make it easy for genuine users to sign up while keeping bad actors and bots out.
Building security to work within this requires respecting Twitter’s hunger to grow, while coordinating with different teams across the business and measuring impact quantitatively. Any changes to the sign up process have a direct and massive impact on the business, so security countermeasures must be well thought out. All of this is hidden behind one little button.
In industry, having straightforward solutions is critical: simplicity is king. In academia, you can build complex systems because you’re trying to prove a concept. But in industry, people must be able to use the software you’ve created, which adds a unique set of design constraints.
There is an open-endedness to work in academia that I enjoy that doesn’t translate to industry, which is driven by quarterly objectives and stakeholders. When I was in industry, I missed the academic freedom and the ability to create and implement my own vision for research. I also missed working on projects that focused on long-term outcomes (measured in years, not months) and a far-reaching, personal vision. This became a catalyst for my return to academia.
I was also motivated by events in my personal life: two years ago, my son was diagnosed with Type I diabetes and I found myself trying to carve out time to research the topic while working in industry. In academia, I knew I could approach the topic with more time and access to additional resources and collaborators at the university.
Strangely enough, I also missed failure! In industry, when you work on a successful product, your main job is execution. There are unique challenges and difficult problems, but by and large, a well-executing team in industry fails rarely. When I reflect back on my previous academic experience, the two projects that stick out the most are failed research projects, because we had no idea whether they were going to work. (They didn’t.)
Leaving industry was scary. I was at Lyft, an up-and-coming company with a bright future, I worked with talented people who I trusted and had worked with for many years, and I loved the pace. In fact, each of the four years that I was in industry, people from academia asked me about coming back, and I always turned them down.
It wasn’t until UC Davis, with a strong pedigree in security research and a deeply collaborative and collegial faculty, reached out that I even considered coming back to academia. With this, coupled with my motivation to help my son and desire to work on long-term research, I came to the realization that to pursue my own interests in research successfully, I had to come back to academia.
When I came back to academia, it wasn’t a flawless reentry. I was wired to move and think fast, but I had to retrain myself to consume information slowly and deliberately. This adjustment showed up even in banal activities like reading papers: in industry, I was used to skimming articles to get the gist. But in academia, being a specialist means you must dive deep into the literature to understand minute details of other people’s research, compare your work with others’ efforts, and explain the concepts to other people when you teach.
In contrast, I noticed that in either setting, and to succeed in any career, you need to have strong communication skills, both written and spoken, to make a case that is both well-laid out and logical. The big difference is that researchers are trained in this skill and practice it often, whereas many software engineers end up picking it up on the job.
Upon reflection, having been in academia and industry has given me the best perspectives of both worlds: I am better at managing people, and I know what students encounter as they go through their educational experiences and careers, including going through multiple environments before finding the right fit. The best piece of advice I’d share: don’t be too afraid to make a change — wherever you go, you’ll learn something.
Sam King, Ph.D., is an associate professor of computer science at the University of California, Davis. He returned to academia after spending four years in industry as both the Head of Accounts at Twitter and the Head of Fraud and Identity at Lyft.