Ben Burns is a second-year PhD candidate in Computational Science and Engineering at Georgia Tech with a decade of technical experience applying generative modeling and uncertainty quantification to inverse problems in scientific machine learning. His research, supported by an NSF Graduate Research Fellowship and supervised by Professor Peng Chen, builds on prior work studying score-based diffusion models, mean-field games, and probabilistic filtering for SLAM. Ben combines deep mathematical grounding from dual degrees in Mathematics and Computer Science with extensive hands-on research and engineering roles—ranging from photogrammetric 3D fire modeling to distributed collision-avoidance algorithms for unmanned aircraft. He has a strong teaching and mentorship track record, having led large undergraduate course assistant teams and authored course materials, and has been repeatedly recognized for outstanding instructional support. Known to "vibe" on GitHub, he brings a collaborative, research-to-application mindset that surfaces nonobvious links between theory and practical AI4Science systems.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Science and Engineering, Doctor of Philosophy - PhD, Computational Science and Engineering at Georgia Institute of Technology
Mathematics (BS), Computer Science (BA), Mathematics (BS), Computer Science (BA) at University of Massachusetts Amherst
Contributions:26 commits, 25 pushes, 1 branch in 5 years 2 months
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