Dr. Juxihong Julaiti (AKA Jushkhun/Josh) is a data scientist at TE Connectivity. He received his dual-title Ph.D. in Operations Research and Industrial Engineering from the Pennsylvania State University.
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He works with applications in manufacturing systems and supply chains. His research interests are in stochastic optimization, stochastic control, and scheduling.

 

He uses various techniques, including reinforcement learning (deep Q network, deep deterministic policy gradient, cross-entropy method, value Iteration), supervised machine learning (long-short term memory neural networks, K-nearest neighbors, random forest, XGboost), meta-heuristics (genetic algorithm, simulated annealing, guided local search), L-shaped method, simplex method, Karush–Kuhn–Tucker conditions, branch and bound, and dynamic programming.

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Before joining TE, he worked as a lecturer to teach IE405 (Deterministic Models in Operations Research ) at Penn State University. In addition, he was an Advanced CyberInfrastructure Software Specialist in Institute for CyberScience at Penn State and a Data Scientist at CPNET.
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