Rongmon Barman is an undergraduate Computer Science researcher at the University at Buffalo focused on blending AI, computer vision, and robotics to build practical autonomous systems. His hands-on trajectory started with a COVID-19 health beacon project and has progressed to autonomous racing work under Dr. Karthik Dantu, where he develops ROS-based vehicles and sensor-integration algorithms. With a decade of experience in technical roles and a background that also embraces astrophysics and statistical coding on GitHub, he brings a data-driven, multi-disciplinary approach to problem solving. Based in Buffalo, he is eager to collaborate on innovative projects that translate research into real-world robotic solutions.
10 years of coding experience
Computer Science, Computer Science at University at Buffalo
A Bayesian Voigt profile fitter. Optimized for absorption line analysis.
Contributions:3 releases, 1 review, 52 commits in 3 years 2 months
bayesianastrophysicsmcmcpython-codesvoigt
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