Ariel Lubonja is a PhD student in Computer Science at Johns Hopkins University with eight years of software engineering experience focused on scalable machine learning and efficient graph embeddings. He combines academic rigor from his PhD and MSE in Data Science with practical full-stack experience gained at jBoxers building JavaEE and Django applications. Based in Baltimore, he investigates implementation-level efficiency for graph representation learning, bridging theory and production-ready code. Known for adaptability and agile delivery, he brings a systems-minded approach to accelerating graph ML workflows and often emphasizes performance optimizations that go beyond typical research prototypes.
9 years of coding experience
4 years of employment as a software developer
Johns Hopkins University
Bachelor's degree, Computer Science, Mathematics, Bachelor's degree, Computer Science, Mathematics at American University in Bulgaria
[CVPR 2024] Generalizable Tumor Synthesis - Realistic Synthetic Tumors in Liver, Pancreas, and Kidney
Contributions:152 pushes, 1 branch in 10 months
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