Used word/sentence embedding methods (word2vec, transformers) to build representations of conversation language that were used to predict insights from the conversations.

Built models for predicting patient risk & symptoms from conversations.

Did various analyses & produced visualizations for scoping out useful insights from conversation data to show our clinicians/members.

Engineered features, defined labels & tuned hyperparameters of regression/tree-based models for predicting credit risk.

Did feature engineering & performance analysis to build rule-based fraud model from scratch for new savings feature.

Built dashboards for monitoring various types of risk.

Built an abstraction for parsing cognitive models made in PsyNeuLink and building them out identically in Pytorch, enabling them to be trained faster.

Improved model training time by ~100x without sacrificing the ease-of-use/modularity PsyNeuLink provides.

Wrote extensive tests to ensure abstraction correctness and measure training speedup for different model architectures.