Research interests (in alphabetical order)

Applied probability, Computational finance, MCMC, Queueing theory, Rare-event analysis, Simulation methodology, and Risk theory.


You can see publications here


Some Research Projects

The next are very brief and informal descriptions which intended for students who are interested in stochastic systems. If you wish to know more about these projects send me an email to set up a time to meeting in person.


  1. Robust Wasserstein Profile Inference & Machine Learning: This is a class of tools which takes advantage of the theory of optimal transport and statistics to address problems in machine learning and operations research via Distributionally Robust Optimization.


  1. Stochastic optimization: We are interested in designing fast algorithms especially in connection to problems related to item 1.


  1. Robust performance analysis: Our goal is to quantify the impact of model misspecification in performance analysis and control of stochastic system. This is a very active research area and we put special emphasis on rare event analysis.


  1. Insurance: This represents a comprehensive analysis of insurance, in particular health insurance systems. Wed like to understand the impact of technology in these specific settings.


  1. Risk analysis: The aim of this project is to develop theory applicable to current risk models, for example, risk theory with investments and also develop new models in a systemic / multidimensional setting. We try to combine optimization theory and rare event simulation and we include applications in areas such as insurance, distribution networks, and power systems.


  1. Perfect Sampling: Traditionally, Perfect Sampling or Exact Simulation algorithms related to the class of procedures that allow to obtain samples in finite time from steady-state distributions. These days, Exact Simulation also encompasses simulation from fully continuous processes (such as SDEs). Our goal is to develop efficient perfect sampling algorithms for a large class of stochastic processes.


  1. Limit theorems and asymptotic analysis: In performance analysis of stochastic systems researchers use Brownian motion and/or other types of stochastic processes as tractable approximations to quantities of interest. We develop large deviations results and corrections to these approximations (such as Edgeworth-type expansions).