As the 1st machine learning engineer, I’ve helped build Textio’s core predictive engine and learning loop for the augmented writing platform which already used by thousands of companies worldwide.

• Spearheaded the development of the Textio core models with cutting-edge technologies in statistical natural language processing and machine learning.

• Design, develop, ship, and improve production features, such as prediction engines for equal opportunity employment, job type, and document type.

• Created scoring models that helped increase predictive power significantly while preserving explainability and interpretability.

Working on the next generation of wearable devices at Microsoft, e.g. HoloLens:

• BCI with deep learning models, e.g. CNN, LSTM, GRU, with a patent pending on eye tracking technology.

• Implement speaker verification systems on DSP which includes enrollment with MAP adaptation, verification with novel scoring methods, and back-end training pipeline for GMMs.

• Reduce memory footprint and speed up runtime for i-Vector speaker recognition system with matrix factorization. Implement average stochastic gradient descent with L2 regularization to train sub-matrices.

Research on deep neural network for brain computer interface, i-Vector, probabilistic linear discriminant analysis, matrix factorization, and DNN for multiple-speaker identification.

R&D in commercial machine translation systems.

• Model adaptation: worked on techniques to automatically adapt background translation system to a specific domain/genre via information retrieval approach and machine learning methods.

• Confidence estimation: explored methods for machine translation quality-prediction including SVM and M5P decision tree. Member of the SDL Language Weaver team that won the 2012 MT quality prediction competition.

• Reordering models: implemented lexicalized reordering models with distributed Hadoop/Pig training pipeline and real-time decoding.