My Ph.D. research is at the intersection of machine learning, uncertainty modeling, and numerical methods. In particular, I am developing machine learning algorithms that can be used in domains under significant uncertainty—with randomness, imprecise data, hidden information, and partial observability. Ultimately, the main goal of my research is to develop methods and algorithms for autonomous decision-making and control, where artificial agents have to act in real-world situations. The applications of my research include: physical infrastructure systems, markets & finance, cloud computing, and cyber-security.



I am a Ph.D. candidate and researcher in the School of Computational Science & Engineering at Georgia Tech. I am also the Chief Scientist at Vorstella, a tech startup using A.I. to stabilize and optimize cloud infrastructure systems.

Prior to my Ph.D. work in Machine Learning, I obtained a B.S. and an M.S. in engineering mechanics with a focus on numerical methods.

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David Betancourt - David Betancourt, Deep Learning, machine learning, uncertainty modeling, reinforcement learning, time series, anomaly detection