Research

Our research bridges the gap between policy and mathematics by using optimization, game theory, and probabilistic techniques to model large-scale systems.

Such systems arise when modeling problems in energy and environmental markets, transportation, and public health. We also model engineering design and develop novel algorithms along with supporting mathematical theory.

The three main areas of MODL’s research efforts are:

Energy
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Health
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Computation
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In the energy domain, we model various fuel and power markets with a focus on policy regulation and environmental impacts. In the health domain we are developing predictive models and other tools to improve patient flow estimates, solve staffing problems, analyze hospital operations and diagnostic processes for better outcomes.

We are also implementing a new framework for the clinical trials system. While computation can be broadly defined, MODL specializes in algorithm design and development for [mixed] integer programming, stochastic programming, equilibrium problems with equilibrium constraints, multiobjective optimization, among other related classes of problems. Data mining and machine learning are also areas of interest, and we have applied these tools to healthcare and bicycle transportation.

Biofuel, Natural Gas
and Environmental Markets
Hospital Staffing
and Patient Flow
Equilibrium Problems
with Equilibrium Constraints (EPECs)
Multi-objective Optimization
Tracking Global
Bicycle Patterns
Optimization under uncertainty
when applied to systems
Mixed-Integer Robust
Optimization
Discretely-Constrained Mixed
Linear Complementarity Problem