Jose Azucena

Also published under:José Azucena

Affiliation

Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA

Topic

Critical Infrastructure,Deep Reinforcement Learning,Geographic Information System,Inland Waterways,Mississippi River,Simulation Model,Simulation Tool,Spatiotemporal Model,Training Set,Alternative Models,Average Speed,Bayesian Model,Commodity Flows,Convolutional Neural Network,Convolutional Neural Network Model,Deep Learning,Deep Neural Network,Deep Neural Network Model,Deep Reinforcement Learning Agent,Design Problem,Drought,Environmental Assessment,Essential Task,Evidence Lower Bound,Exact Algorithm,Extreme Events,Feasibility Constraints,Gaussian Process,Generalized Additive Models,Geographic Information System Data,Highway,Land Transport,Laplacian Matrix,Leaky ReLU,Level Of Quality,Link Reliability,Lowest Cost,Maintenance Activities,Maintenance Optimisation,Markov Chain Monte Carlo,Markov Decision Process,Model Configuration,Multi-agent,Multimodal Transport,Navigation System,NetLogo,Network Design,Network Design Problem,Network Reliability,Oil Industry,

Biography

JOSE AZUCENA is a Ph.D. candidate in the Department of Industrial Engineering at the University of Arkansas. In 2014, he received a B.S. degree in Business Engineering from ESEN, Santa Tecla, El Salvador. His research interests include network reliability, deep learning, and deep reinforcement learning. His email address is [email protected].