AutoML system that intakes arbitrary rectangular data and builds and compares thousands of deep neural networks via genetic algorithms (DeepNEAT) to build the optimal model for your data. This system is highly performant on both flat and time series data, having the capacity to encode many variations of RNNs and CNNs in addition to standard dense networks.
Leveraged Genetic Algorithms to schedule optimal task orderings across all construction staff and projects. This system solved a more complicated version of the Job Shop Scheduling Problem, an NP-Hard problem that is “clearly harder than the Traveling Salesman problem.” The system optimized over individual staff skillsets, drive time, site visit ordering (traveling salesman), task ordering, resource availability, wage spend, and more.