Khoury News
Khoury lecturer Steve Schmidt on the math behind Nike’s machine learning
Steve Schmidt's career began in sales and marketing, far from the realms of math and computing. Now he's not only plying his machine learning skills for corporate household names, he's also passing on those skills.
According to Khoury College lecturer Steve Schmidt, the fundamentals behind his machine learning work aren’t as complex as people might imagine. Despite working for some of the world's most recognized brands, including his recently concluded role at Nike, Schmidt says the foundational math is essentially the same as that used in everyday applications — and in all the courses he teaches.
Schmidt’s own grasp of those fundamentals involved a diverse journey that included a less-than-stellar undergraduate experience and early career roles in hotel sales and marketing. He had never taken a math course but taught himself math online and from books, ultimately earning a master’s in theoretical math.
He says it wasn’t long before he realized that “you need computers to do most of the cool math,” so Schmidt pursued a second master’s degree, in computer science. His experiences with supportive professors in both degrees compelled him to begin teaching part-time, first at the College of Charleston and then, after relocating to Boston, Khoury College.
“I had a lot of help from people who believed that I could learn this stuff, even with a poor undergraduate resume,” Schmidt says.
The combination of his willingness to do whatever it took — which included “a lot of tests” and watching YouTube videos — along with the support of people who believed in him led Schmidt to his advanced degrees, and eventually, to a career that grew to include roles with Nike, BAE Systems, Raytheon, and Wayfair.
In his role at Nike, which he began in 2022, Schmidt oversaw a team that develops and optimizes machine learning models’ data and objectives. These models customize users’ search experiences, allowing them to immediately see different and more relevant search results.
One such change was the evolution from lexical search, which relies on matching exact words or phrases, to natural language models that allow for more nuanced searches. This enables the machine learning team to provide enhanced and improved user experiences. Schmidt gave the example of a user searching for “marathon-winning shoes”; in the past, results could be more limited because the search term likely wouldn’t match the description on Nike’s website.
“More sophisticated reasoning models enable more of a natural language appeal to how a user would experience that search,” Schmidt explains. “So, you could type in ‘marathon-winning shoes’ and that model will reason over the fact that that’s how people talk about it and then present you with the styles that have been worn by marathon winners.
“From a user perspective, you've gone from hoping that the search keywords that you used matched exactly to now expecting results that are more natural language focused,” he adds.
Another experience Schmidt says his team “really bought to life” is when they alter a model that influences users’ individual product recommendations. Mixing what Nike knows about a user’s behavior — what they’ve browsed, clicked on, and put into their carts — with their long-term customer profile (e.g. athletic vs. lifestyle and leisure) “really is the essence of personalization,” he explains.
“The individual experiences, whether it’s for search, browsing, recommendations, what you're getting in your emails — all of that can be tailored through machine learning to each and every person,” Schmidt says.
And the tweaking is never done.
“It’s always day zero,” Schmidt says. “There’s always the ability to improve on personalizing those results.”
While applications vary in complexity and scope, he maintains that the behind-the-scenes math fundamentals, the same ones he teaches at Khoury College, are generalizable.
“It’s the same math that's used to train an unmanned undersea vehicle as we use to personalize your website experience at Nike,” Schmidt says. “It's different applications, but the underpinnings that you would learn in an undergraduate or graduate course are exactly the same.”
As with his students, this fall ushers in a new chapter for Schmidt; he is starting a new role in machine learning research and development at Boston Dynamics. But his commitment to teaching remains steadfast, due in part to the influence good teachers had on his career trajectory.
“That can really make the difference in getting a job and being passionate about your career,” he says.
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