Roxana Tanase is a Machine Learning Developer with 11 years of experience and a PhD in Computational and Applied Mathematics, based in Bucharest. She blends deep expertise in numerical simulation, stochastic estimation (Ensemble/KL/SCKF variants), and PDE-constrained optimization with practical engineering, having shipped computer vision solutions at Bosch and ML systems at SOFTWIN. Her research at the University of Pittsburgh produced efficient parameter-estimation frameworks and theoretical results in stochastic optimal control, reflecting a rare mix of theoretical rigor and production-oriented code. Known for resourcefulness and strong interpersonal skills, she thrives on translating biophysical modeling and complex math into business-impacting solutions for science-driven ventures. An ongoing learner, she leverages her background in mathematics and computer science to tackle high-dimensional, uncertainty-aware problems that typical ML engineers may not approach.
11 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy (PhD), Computational and Applied Mathematics, Doctor of Philosophy (PhD), Computational and Applied Mathematics at University of Pittsburgh
Master of Science - MS, Mathematics, Master of Science - MS, Mathematics at University of Bucharest
Contributions:3 pushes, 1 branch in 3 years 4 months
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Roxana Tanase - Machine Learning Developer at SOFTWIN