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The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech’s colleges and the Georgia Tech Research Institute (GTRI).

These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we’d like you to meet Jana Boerger, a Ph.D. student who is passionate about supply chain management and building stronger connections across ML@GT.

Hometown: Osnabrück, Germany

Home School: H. Milton Stewart School of Industrial and Systems Engineering (ISyE)

Advisor: Benoit Montreuil

Current Georgia Tech degree program: Machine Learning Ph.D.

Other degrees earned: B.S. in Logistics from Technische Universität Dortmund, and M.S. in Industrial Engineering from Georgia Tech

Previous experience: Before going to graduate school I worked in the industry for a few years at Audi AG, Beiersdorf AG, and Deloitte where I focused on supply chain management, logistics, and material management.

Tell us about your research: I’m interested in the application of Machine Learning in Supply Chain and Logistics Systems. Currently, I focus on Reinforcement Learning in Inventory Management.

Favorite place to hang out on campus or in Atlanta and why: The Beltline is a great place to spend a sunny Sunday.

What are your summer plans? This summer, I’ll be interning as a JSPS research fellow at RIKEN Advanced Intelligence Project (AIP) in Tokyo, Japan.

Favorite Georgia Tech experience: This actually has not happened yet, but I’m excited about a project that my friend and fellow Ph.D. student Nathan Hatch and I are working on for this upcoming academic year.

Starting this fall, Nathan and I will be organizing a bi-weekly seminar for machine learning (ML) Ph.D. students. The talks will be given by an ML student about their current research or a tool that they like to use with time for questions and mingling.

We created this seminar series to help ML students get to know each other on a personal basis and build a stronger community. Because machine learning is spread out across campus, it’s often hard to know what other students are working on. We’re hoping these seminars will help continue to build connections while also giving students an opportunity to share their work and practice giving talks for conferences and other presentations.