Alumni from our undergraduate and graduate programs go on to do a lot of different things. We provide two undergraduate majors: in Psychological & Brain Sciences and in Cognitive Neuroscience. A bachelor’s degree in Arts & Sciences is a broad liberal arts degree that qualifies a person for a wide range of careers, and our majors’ paths after graduation reflect this. Some undergraduate alumni go on to further study in psychology or a related field, but many go on to careers in business, medicine, creative practice, and other diverse pursuits.

A PhD in Psychological & Brain Sciences is a specialized degree that qualifies a person to lead independent research in psychological science and neuroscience. (Our Clinical Science PhD program, together with state licensure, additionally qualifies a person to provide evidence-based psychotherapy.) This means that alumni of our PhD program tend to pursue a narrower range of careers—but not as narrow as you might think! Despite the stereotype that all PhDs pursue academic careers, our graduates have always followed a range of pathways: professor, consultant, clinical practitioner, researcher in government or industry, human factors specialist, demographer...

One trajectory runs labs at WashU to research or product development arms of technology-forward companies. The tech industry has always been on our trainees’ radar, but over the last few years interest in the technology industry has swelled amongst PhDs in the psychological science, and the attraction is mutual.

I caught up with two recent graduates of the P&BS PhD program who have begun careers in Silicon Valley: Tan Nguyen and Jessica Nicosia. I asked them about the path that brought them to where they are now and about where they see their industry going. We talked about AI, of course. This is a particularly fast-moving moment, but the relationship between psychology and technology has a long history and has had periods of dramatic change before.

Tan Nguyen (PhD 2025)

Dr. Tan Nguyen grew up in Vietnam and attended the University of Science, majoring in Information Technology. His interest in the intersection between psychology, neuroscience, and technology is longstanding. During his undergraduate years he started working for Axon Enterprise, developing AI Vision systems recognizing features in camera footage. He joined Axon full-time for a year while applying to PhD programs, before joining our program in 2020. Tan describes his major as having been very flexible, leaving him time to explore philosophy, psychology, and neuroscience—including a lot of work on his own, utilizing coursework developed by Coursera and EdX.

By the time he started the PhD program, Tan knew firmly that he wanted to do cutting-edge exploration of human intelligence, and that he wanted computational methods to be a big part of his work, but he was not sure whether he wanted to ultimately pursue a path in academia or in industry. At WashU, Tan continued to pursue his interests in neuroscience and computational modeling, building a research program exploring how the mind and brain build representations of experience from movies and stories. He completed the interdisciplinary Computational, Cognitive, and Systems Neuroscience training pathway, and published papers in eLife, PNAS Nexus, and Scientific Reports. At the same time, Tan kept up with the latest in programming and industrial research. He completed two research internships during his time as a PhD student, one at Microsoft Research and one at Google. (He still left a little time for other pursuits, including running the St. Louis Marathon!)

As graduation approached, Tan was sure that a career in Silicon Valley was right for him. After several interviews, he accepted a position at Google, working on their Gemini AI technology. He has since moved to Stripe, the largest privately owned financial technology company. (Things move fast out there!)

At Stripe, Tan integrates AI agents into the machine learning infrastructure across the company. I asked what he saw as the key differences between the models he works with now and the models we develop in computational neuroscience. He told me that the scale of the training sets and models is much larger, and that the learning mechanisms are more generic. The focus is less on the internals of how the model is doing what it does, and much more on how to make the available data digestible to the model.

Jessica Nicosia (PhD 2026)

Dr. Jessica Nicosia completed her undergraduate degree at the University of Michigan (in three years!), working with Cindy Lustig, who had previously completed a postdoctoral fellowship here in the Aging & Development training program. Jessica stayed on at Michigan for one more you to do an accelerated masters program before joining the PhD program at WashU. Here, she worked with Dave Balota studying attention, mind-wandering, learning and memory, and how they change with adult aging and in early dementia.

When she arrived at WashU, Jessica was convinced that she was headed for the professoriate. But as her interests and her priorities evolved, she started to sense that the academic path might not tick all her boxes. She remembers reading a chapter in The Compleat Academic, in which Roddy Roediger urged prospective professors that they could not be geographically picky—if the colleagues and research resources were great, you should be ready to go. She went to talks by speakers from industry and talked to recent graduates who had followed that path. She took some interviews with companies like Google, Facebook, and Robin Hood, but none of those roles felt right. So, after graduating she moved to the Knight Alzheimer Disease Research Center for a postdoctoral fellowship with Jason Hassenstab and continued to hone her goals. She was grateful that Jason was supportive of her career goals; together, they worked out a project that was important for the lab and also built out her skills in areas she knew to be important to industry.

As she wound up her postdoc, Jessica found the right fit in the software engineering group at Apple. There, she shapes products that go into the hands of millions by advising on the human experience, designing and executing behavioral studies, and interpreting and communicating data. I asked Jessica how her training here prepared her for the work; she deeply appreciates the training she received from Dave and Jason, but also noted the critical value of statistics classes from Mike Strube and Ian Dobbins’ research methods course.

A different culture

A common theme in our discussions is that the culture of the technology industry has some commonalities with that of academia, but also a lot of differences. Tan and Jessica used a variety of sources to identify potential openings; LinkedIn was particularly valuable as a source of leads, and GlassDoor was useful to learn about the environment in different companies. Informational interviews with people in the industry were very helpful. The interview process is quite different than an academic job interview: There may be a bit of discussion about one’s previous research, but a lot of the interview time was spent in standardized evaluations.

The rhythm of a workday at Stripe or Apple has a lot in common with a day in the life of an academic researcher: a lot of small meetings, a few larger meetings, and a good amount of time working solo. Of course, there are no classrooms and no grant review panels!

Unique value

Tan and Jessica are both bullish on the future of behavioral, cognitive, and neural science in the technology industry. Jessica notes that there is huge interest from folks in our field in joining the industry—she gets many LinkedIn queries every day. (Fun fact: My graduate school labmate was one of the early employees at LinkedIn. Goes to show you that the relationship between psychology and tech is not new!) Both Jessica and Tan think that our field is only going to be more critical in the next few years.

In my interviews, I was truck that Jessica and Tan cited more or less the same characteristics that make psychological and brain scientists uniquely valuable in the tech industry. One is training in how to break down a fuzzy, ill-posed problem and turn it into something tractable. Another is an appreciation for variability and the technical statistical tools to deal with it. Finally, we bring an appreciation for the role of humans in the loop. At the end of the day, isn’t that what it’s all about?