Official Biography

David Betancourt, Ph.D.

Dr. David Betancourt was born in the city of Medellín, Colombia. He emigrated to the United States at the age of thirteen. He is a machine learning scientist currently working at Apple on privacy-preserving machine learning.

His research interests lie at the intersection of machine learning, uncertainty modeling, numerical methods, and autonomous decision-making. In particular, his research seeks to use machine learning in domains under significant uncertainty—with randomness, incomplete data, hidden information, and partial observability. The main goal of his research is to develop methods and algorithms for autonomous decision-making and control, where artificial agents have to act in real-world situations.

David attended Georgia Tech, where he received his bachelor’s and master’s degree in engineering with a concentration in solid mechanics, a master’s in computer science with a concentration in machine learning, and his Ph.D. in computational science and engineering.

He previously worked as an engineer for the French company Areva, where he worked on carbon-free energy projects. In particular, he led projects in the area of computational mechanics and probabilistic risk assessments of a new generation of nuclear reactor designs. He later worked as a consultant in energy and defense projects, assessing the reliability of safety-critical systems (e.g., combat vehicles, nuclear fuel storage) under extreme conditions with computational models. 

For his doctoral studies, he worked under the guidance of Dr. Rafi L. Muhanna, in the Center for Reliable Engineering Computing lab. During his research, he pioneered the area of “interval deep learning,” which uses interval analysis for deep learning to solve problems under uncertainty. During his doctoral studies, he interned for the defense company STR, developing reinforcement learning algorithms for cyber-physical applications. In his free time in the course of his Ph.D., he developed market trading algorithms using deep reinforcement learning. Prior to joining Apple, he was the chief scientist of the AI technology start-up, Vorstella, which was acquired in 2020.

Education
  • Ph.D. Computational Science and Engineering.
  • Thesis: Interval Deep Learning for Uncertainty Quantification in Engineering Problems.
  • Advisor: Dr. Rafi L. Muhanna
  • Committee: Drs. Steffen Freitag, Vladik Kreinovich, B. Aditya Prakash, Chao Zhang, Abdul-Hamid Zureick
  • Qualifying Areas: Machine Learning and Numerical Linear Algebra
  • Minor Area: Uncertainty Modeling
  • M.S., Computer Science. Concentration: Machine Learning
  • M.S., Engineering. Concentration: Computational Mechanics
  • B.S., Engineering, High Honors. Concentration: Solid Mechanics