Learning enabled optimization for data and decision sciences 

The next generation of power-system operations: modeling and optimization innovations to mitigate renewable uncertainty 

On practical network optimization: convergence, finite buffers, and load balancing 

Modeling and analysis of parallel and spatially-evolving wall-bounded shear flows 

Differentially private and fair optimization for machine learning: tight error bounds and efficient algorithms 

Noise benefits in nonlinear signal processing 

Efficient inverse analysis with dynamic and stochastic reductions for large-scale models of multi-component systems 

Inference for stochastic models of molecular data 

Stochastic multidrug adaptive chemotherapy control of competitive release in tumors 

Real-time controls in revenue management and service operations 

Elements of dynamic programming: theory and application 

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Random forests and diffusion flow 

On stochastic integro-differential equations 

On non-zero-sum stochastic game problems with stopping times 

Time-homogeneous parabolic Anderson model 

Optimizing statistical decisions by adding noise 

Characterization of the STARS (STochastic gene Activation with Regulated Sparseness) mouse line 

The effects of noise on bifurcations in circle maps with applications to integrate-and-fire models in neural biology 

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Active state learning from surprises in stochastic and partially-observable environments 

Essays on dynamic control, queueing and pricing 

Parameter estimation in second-order stochastic differential equations 

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