“There’s only going to be more data—there will never be less of it, and I see that as a big, big challenge for humanity. We have the technology to produce and store more data, so how do we handle it all—this huge mass of data that is simultaneously growing larger and becoming more complex? We have to figure that out.

“In short, I like to solve problems.”

That’s Polo Chau, associate professor in Georgia Tech’s School of Computational Science and Engineering and associate director of the Master of Science in Analytics (MSA), explaining why he’s so enthusiastic about the field of data science.

Chau directs the MSA computational analytics track * , which includes machine learning, deep learning, natural language, AI, and high-performance computing. He also teaches a required course: Data and Visual Analytics (CSE 6242).

“How do you store data? How do you analyze it? And how do you visualize it? Often, these questions are closely related,” Chau notes.

There’s such demand for the subject that Chau teaches the class every semester to over a thousand students, in both online and in-person classrooms (with the admitted help of many teaching assistants). It’s open to undergraduate and graduate students across the Institute. “I need a break,” he says, laughing.

His research group, Polo Club of Data Science, works on problems such as human-centered AI, cybersecurity, social good issues (health, education, human trafficking), and billion-scale graph visualization and data mining. Given these focus areas—and the rapid advances brought about this past year with ChatGPT, what changes does Chau foresee for the MSA program?

“I think what’s coming is exciting but may also make some people nervous, given that things are changing so quickly. There are lots of opportunities to innovate, opportunities that previously people thought were not possible,” Chau says. “Also, as I mentioned previously, there are much larger amounts of data. There’s only going to be more data and more technology, and if we approach it the right way here in the MSA program, we can research and teach amazing things.

“But of course, once you have powerful things like this, people can do bad things with them as well, and lawmakers don’t know how to regulate them very well. So I would say that—with caveats—we’re at this exciting, pivotal point in time.”

^* The computational data analytics track allows students to build on the interdisciplinary core curriculum to provide depth and specialization in data science, including ML, deep learning, natural language, AI, visualization, databases, high-performance computing, etc. Depth elective options also include topics in data acquisition and data engineering. All students in this track are required to select at least one elective in machine learning.