Alumnus Heads CDC’s Inaugural Platforms Division
Alumnus Jorge Calzada, MS in information systems, was appointed by the Centers for Disease Control and Prevention to head machine learning and artificial intelligence and direct the CDC’s inaugural platforms division, which is responsible for creating new platforms and standards for health data. The goal is to improve processes for tracking health trends and threats, as well as to assist disease forecasting centers.
This article originally appeared on Northeastern Global News. It was published by Cynthia McCormick Hibbert. Main Photo: Jorge Calzada, Northeastern alumnus, is the newly appointed head of Machine Learning/Artificial Intelligence at the CDC’s inaugural platforms division. Photo by Alyssa Stone/Northeastern University.
The CDC is modernizing its approach to data science, and this Northeastern graduate is leading the way
As the head of the new platforms division at the Centers for Disease Control and Prevention, Northeastern graduate Jorge Calzada is charged with bringing health data from the era of fax machines into the realm of rapid delivery and analysis.
“When a pandemic happens, you go from zero cases a day, or one case a week, to hundreds of thousands,” Calzada says. “You can’t throw enough people at this problem to handle all this data.”
That’s where data and computer science come into play, helping to gather information faster and assist with trend analysis, Calzada says.
A technology executive for two decades, Calzada was hired this spring to head machine learning and artificial intelligence and direct the CDC’s inaugural platforms division.
The division is one of five in the newly created CDC Office of Public Health Data, Surveillance and Technology and will create new platforms and standards for data gathering and sharing.
The goal of modernizing health data science, Calzada says, is to do a better job of tracking health trends and threats, as well as to assist disease forecasting centers, such as the one recently funded by the CDC at Northeastern.
“Some of the problem with forecasting in the disease space is you don’t actually know what’s happening today,” he says. “There’s a lot of uncertainty. It’s like not knowing today’s weather but you’re trying to predict tomorrow’s weather in Boston.”
“That’s the equivalent of what we’re facing in public health,” Calzada says. “A lot of the things that I’m doing are hopefully going to fix those fundamental issues so that people can start building more sophisticated models and do a better job of forecasting for the future.”
Read full story at Northeastern Global News