From Academia to Silicon Valley - and back
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.
How to make undergraduate research worthwhile
Practices might differ from country to country, but undergraduate students can be better served in research, says Shaun Khoo. One of the things that excited me about taking up a Canadian postdoctoral position was that, for the first time, I would get a chance to work with and mentor enthusiastic undergraduate researchers. I looked forward to the chance to gain mentorship skills while helping out future scientists, and maybe, eventually, freeing up some of my own time. As an Australian, I had never been pressured to volunteer in a lab — most Australian students don’t do any undergraduate research unless they enroll in an extra honours year, because the law prohibits unpaid student placements that are not a course requirement. This hasn’t held back overall research productivity in Australia, but it is a stark contrast to the North American environment, where many undergraduates feel pressure to get research experience as soon as they begin university. Most graduate medical students, for example, have previous research experience, and North American graduate schools have come to expect this from applicants. In Canada, nearly 90% of graduate medical students have past research experience1. Numerous articles extol2,3,4 the virtues of undergraduate research experience, but, unfortunately, evidence supporting the benefits of undergraduate research is limited. Most studies on the topic rely exclusively on self-reports that are corroborated less than 10% of the time by studies using more-direct measurements. For example, surveys find that undergraduate student researchers say that they have developed data-analysis skills — something that would normally involve lots of practical work — yet, when interviewed, most of them admit to never having done any data analysis. Like many postdoctoral researchers and graduate students, I spend most of my time with undergraduate students working on technical skills that they might need to work in the lab, but that don’t necessarily improve their conceptual understanding. For example, if I teach a student how to use a cryostat, they might become proficient in slicing brains, but they won’t necessarily learn how synaptic transmission works. Even if we manage to instil excitement for the intricacies of research in our undergraduate students, it’s hard to avoid the conclusion that for the vast majority that continue in academic research, there will be no permanent jobs — we might just be saddling our undergraduates with unrealistic expectations. So how do we avoid wasting our time as mentors and our students’ time as learners and researchers? Here are my suggestions. Consider long-term goals. Undergraduate students should reflect on how their research experiences will prepare them for professional success. Should they be aiming for research experiences that are based on their courses, because it will better improve their understanding of scientific concepts? Will a given opportunity help them to reach their career goals by getting into a professional graduate programme? Can they commit to staying with a research programme long enough to become effective and potentially be a co-author? Acknowledge and offset opportunity cost. Undergraduate research requires significant time investments from both students and research supervisors. Undertaking such research might mean forgoing paid employment or other experiences, such as student societies, sport, performing arts or campus journalism and politics. Mentors can help undergraduate students by facilitating summer-scholarship applications or finding ways for students to get course credit for their work. Train for diverse careers. Most undergraduate students will pursue non-research careers or join professional graduate programmes. Those who try to continue in academia will eventually face a bleak post-PhD academic job market. Just as PhD students need preparation for a wide range of careers, so do undergraduate students need to build a transferable skill set. Mentors can encourage undergraduate students to build communication skills by, for example, encouraging them to present in lab meetings, or facilitating teamwork by having groups of undergraduate students complete a project together. Improve undergraduate research experiences. There’s limited non-anecdotal evidence that undergraduate research improves a given lab’s research productivity, or even student learning, but such research isn’t necessarily a waste of time. Before undergraduate students pad their CVs with research experience, they should reflect on what they will achieve by conducting research, and they should seek out meaningful projects to work on and develop relevant skills for their future career. For mentors, we have an obligation to consider the career development of undergraduate students and, for the sake of our publication records, we should aim to work with students who can commit at least a year to our projects. And, as much as possible, we should try to take the pressure off undergraduate students to do research, so that it can be an enjoyable learning experience rather than a box they need to check. doi: 10.1038/d41586-018-07427-5 This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged. You can get in touch with the editor at email@example.com. References 1. Klowak, J., Elsharawi, R., Whyte, R., Costa, A. & Riva, J. Can. Med. Educ. J. 9, e4–e13 (2018). PubMed Google Scholar 2. Smaglik, P. Nature 518, 127–128 (2015). PubMed Article Google Scholar 3. Ankrum, J. Nature https://doi.org/10.1038/d41586-018-05823-5 (2018). Article Google Scholar 4. Trant, J. Nature 560, 307 (2018). Article Google Scholar Download references
How a stint in Silicon Valley unleashed one researcher’s business skills
Tomasz Głowacki’s career now straddles academia and industry, thanks to his participation in a leadership programme organized by the Polish government. In 2007, when I started work as a research and teaching assistant at Poznań University of Technology in Poland (a job that straddled bioinformatics research and teaching discrete mathematics, algorithms and data structures), I thought academia would be a lifelong career. I enjoyed the intellectual freedom, chance to work on challenging problems and travel opportunities. Shortly after defending my computer-science PhD thesis in 2013, I secured a place on the Polish government’s Top 500 Innovators initiative, a nine-week programme in research commercialization and management at universities with high positions in the Academic Ranking of World Universities. It was set up because the Polish government thought a lack of cooperation between researchers and business was one of the main reasons for the country’s low position in European Innovation Scoreboard rankings. The focus at my interview was how to commercialize my research results. I was asked about factors such as potential customers, business models and pricing. Two months later, I was one of 500 scientists sent either to the University of California, Berkeley; Stanford University, California; or the University of Cambridge, UK. The goal was to learn from the very best researchers and business practitioners. While at the Walter A. Haas School of Business at Berkeley, I spent time with researchers, practitioners and entrepreneurs from Silicon Valley. What surprised me the most was the marriage between business and academic institutions in California. Lecturers shared their experiences of research commercialization, business and start-up firms. This was very different from Poland, where a scientific career does not recognize commercial activities in terms of cooperation between business and academia. In my experience, many Polish scientists see commercialization activities as a roadblock to their academic careers. During the Berkeley training, I heard how PhD students can successfully transition into business. These lectures were delivered by Peter Fiske, who is now director of the Water Energy Resilience Research Institute at Berkeley Lab, and whose career straddles both industry and academia. Fiske focused on transferable skills between academia and business, covering data analysis, resourcefulness, technological awareness, resilience, project management, problem solving, English proficiency and good written communication. Fiske is a strong advocate of the need to market yourself as a scientist. Mark Rittenberg, a business and leadership communications specialist at Haas School of Business, taught us about the power of communication and storytelling. As scientists, we focus mostly on research results. We tend to think that the content we present is enough to sell ourselves. But in business, how you present yourself, self-confidence, an interesting story and non-verbal communication are of at least the same importance. The innovators programme included one-day visits to technology companies in Silicon Valley, and the opportunity to undertake internships at some of them. I visited Google, the software companies Splunk and Autodesk, as well as NASA and biotechnology firm Genentech. These visits helped me to understand that ambitious work and challenging problems are not just the domain of universities. I did a three-week internship at PAX Water Technologies in Richmond, California, where I was one of five Polish scientists who set up an interdisciplinary team to work on reducing household water consumption. This was a long way from our research topics, and a new area for all of us. Willingness to learn new things, self-curiosity, creativeness and being open to unexplored areas helped us to drill down into the problem and to propose a solution. All of these are standard skills for a scientist. The programme helped me to understand that scientists can be effective and successful outside academia, and that the business world is full of challenging problems to work on. But the most important conclusion for me is that the applied aspect of what I do matters the most. The best fit for me seemed to be a transition into business. Between June and September 2013, after completing the innovators programme, I applied for several research and development positions in business. I prepared a long CV that covered my research achievements. No one got back to me. It was an important lesson. As scientists, we have to understand how our skills fit current job-market demands. So I connected with some old university friends who were working in business to discuss their interview experience. I decided to revamp my CV by making the description of my education shorter and focusing on my transferable skills; I included organizational skills, experience of data-analysis techniques, language skills and my structured approach to problem solving. As scientists we focus more on problems and solutions when we describe our work. But a potential business employer is more interested in how you get there. You should focus on the tools and methods you have used, knowledge of foreign languages, and how you organize and report your work. In 2013, I found a job as an analyst at BAE Systems Applied Intelligence at its new offices in Poznań, working with IT systems and insurance data to detect customer fraud. A year later, I discussed my transition with Fiske, who told me: “Now that you are on the other side, don’t lose touch with your friends in academia — seek ways to help them be more relevant to the outside world.” I wanted to give something back and to find my own way to contribute to the academic world. I am now head of product development at Analyx, an international marketing data-analytics company, and also work part-time at Poznań School of Banking as a business practitioner, teaching project management as well as systems analysis and design. I discuss the real business cases I face with my students. I also organize lectures and meetings for students with business experts, chief executives and consultants. Some of these have started long-term academic collaborations, and they provide a great opportunity for students to learn from practitioners and to land internships. I have managed to organize a master’s programme between academia and business. Students have the chance to get involved in hot industry topics supervised by business experts, and to present results and defend their theses at their universities. Teaching based on my personal experience is more satisfying for me. Leaving academia was not a failure. It helped me to explore new opportunities, to better understand my professional expectations and to find the career path that fits me best. This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged. You can get in touch with the editor at firstname.lastname@example.org.
Is Liveness a critical factor in learning Computer Science? Context, motivation, and feedback for learning programming
My CACM Blog post for November is on the topic of Direct Instruction, why it’s better than Discovery Learning, and how we should teach programming “directly.” I wonder about the limitations of Direct Instruction. I don’t think everything can be learned with direct instruction, even with deliberate practice. At SIGCSE 2016, John Sweller made a […]