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.
This system was built by adapting multiple bleeding edge research papers into a full production AI system. The system accepts streams of realtime flight data, and thus has extremely low latency requirements. It then performs unsupervised clustering of timeseries data in order to flag potentially dangerous flight paths.
This system intakes 3 view mammogram scans of breast tissue and outputs the risk factors associated with the images. It improves on the current clinical accuracy of risk assessment by more than 10% in the majority demographic (caucasian women) and up to 60% improvement in the case of minority demographics. We adapted just released research into a production grade AI system and helped oversee and advise the process for technology acceptance by the FDA.
Trained a hyperspecific Named Entity Recognition (NER) and Entity Linking model on hundreds of thousands of industry specific documents which resulted in near full automation of multiple job types in the food regulatory industry, where inaccurate predictions have severe consequences. Coupled this with a powerful graphical interface for verifying predictions downstream to produce a production application on Azure Cloud.
Trained a model to reliably extract event details such as title, date, time, participants, and location from blocks of unstructured text and automatically create standardized event entries. This enabled event extraction from both the internet and from conversations, as well as instant event creation via voice.