My research involves developing methods for utilizing various types of time-dependent data for generating predictive models. These models are essential for uncertainty quantification and decision making under uncertainty.
My most recent PhD work is entitled "Selection of representative realizations for decision making and optimization under uncertainty".
I am also interested in optimization (various approaches such as gradient-based, global stochastic optimization, mixed-integer optimization), inverse problems, and stochastic control.