Brett Beaulieu-Jones, a doctoral student in genomics and computation biology in the Perelman School of Medicine, has had an interesting few years, to say the least.
Today, he’s working towards his PhD at Penn by training computers to analyze huge samples of patient data. Through machine learning — helping the computers get better at the analysis on their own — Beaulieu-Jones hopes his team can more easily find disease subtypes in patients just by looking at the data in electronic medical health records.
Synconset team, from left to right: Jeffrey Impey, Alexander LoVerde, Brett Beaulieu-Jones
But, it was just a couple of months ago that the Boston College grad was in Los Angeles accepting an Emmy Award for his work revolutionizing the way film and television production do business.
In the spring of 2012, Beaulieu-Jones was preparing to for grad school when his friend asked if he was up for a project. The friend saw a pair of sunglasses in a movie and, wanting to get himself a pair, did what everyone does and did some online digging. Surprisingly, he found nothing, but these days, when you can’t find something on the internet, generally what you do find is an opportunity.
While researching the movie and television industry, Beaulieu-Jones and his friends learned that production designers were still using binders to keep track of everything on set. They worked for the next few years to build Synconset, a company that developed software aimed at improving collaboration among production teams, an especially important feature as productions often include hundreds of people working in different locations – not great for a binder.
The Synconset team also developed an app that could receive a screenplay and perform a breakdown, turning the script into metadata. The metadata is used by crews to determine how many costumes will be needed, which characters should be wearing what costumes for which shots to maintain continuity, the consistency of design between shots for a scene, and so on. A hit out of the gates, the software was expanded to include props, makeup, hair, locations and set decorations.
Beaulieu-Jones said they grew organically from there, spread by word of mouth, and now, after a relatively short period of time, Synconset is used by half of all television and film productions. The effect they’ve had in Hollywood hasn’t gone unnoticed, either. In October, the Synconset team flew to Los Angeles to accept their Primetime Engineering Emmy Award.
“I think we were the youngest group to win an Engineering Emmy,” Beaulieu-Jones said. “We were the only winners there who all brought their parents.”
Emmys in hand, the company is still evolving, Beaulieu-Jones said. They’re working on implementing an e-commerce platform so production design crews can buy what they need right from inside of the app. That capability would create a snowball effect, allowing for the possibility of simplifying other product sourcing opportunities, such as product placements.
Meanwhile, back at Penn…
Beaulieu-Jones isn’t as involved in Synconset as he used to be. Instead, he’s now using his technical know-how to help make the most of the digital mountains of data we collect each day.
“Synconset has helped me learn how to build and manage large databases in complicated workflows,” he said. “Electronic medical records struck me as an area where there’s a huge amount of data, but due to the realities of the clinic, the data is complicated and noisy and we aren’t fully utilizing it for research yet.”
Beaulieu-Jones and team are studying how machine learning can help diagnose patients with metabolic syndrome, people who are at risk of diabetes or cardio disorders, through the health data collected while they are treated at Penn. Patients come in for a variety of reasons, and while doctors are focused on treating the priorities, Beaulieu-Jones said the system could analyze the many points of data collected and hopefully predict future issues a patient could face, ultimately allowing a physician to intervene earlier.
Digesting the amount of data we create at Penn and accurately predicting future diagnoses isn’t possible for humans to do on their own, according Beaulieu-Jones, but thanks to enormous advancements in technology and a clever team from a broad, and unexpected, range of backgrounds, it’s now a reality.