My research is at the intersection of machine learning, uncertainty modeling, and numerical methods. In particular, I develop 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 have included physical infrastructure systems, markets & finance, personalization, cloud computing, and cyber-security.



I obtained my Ph.D. in Computational Science & Engineering from the College of Computing at Georgia Tech. My doctoral research sought to use deep learning for engineering problems under epistemic uncertainty using interval analysis. I currently work at Apple on privacy-preserving machine learning algorithms. Prior to that, I was the Chief Scientist at Vorstella, a tech startup that used AI to stabilize and optimize cloud infrastructure systems (acquired in 2020).

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