Research

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.

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