-Using ML to pinpoint and provide actionable insights for chronic diseases -Explainable AI (XAI) to enhance patient engagement and user experience-Leads product management and delivery

Health Informatics and Applied ML (collaborated with Samsung Digital Health and Kaiser Permanente)- Proposed offline and incremental learning model of daily blood pressure (BP) and data collected from wearables- Enable personalized healthcare by providing both numerical prediction of BP and importance ranking among features. The method is based on Random Forest, a tree-based ensemble model. We propose a weighted re-sampling technique and a stable feature selection technique to enhance the original model.Solar generation forecasting- Integrated signal processing and ML model to extract key information form solar and contextual (temperature, humidity, etc.) time series to make better solar generation forecasting

-Implemented machine learning models to predict wafer fabrication yield reduced the prediction error by 56% -Developed internal use cases and strategies applying machine learning on sensor data and proposed a white paper

Conducted research on user mobility in Heterogeneous Networks (HetNets) and energy saving in LTE-advanced and implemented simulators. My teammate and I proposed a new solution to enhance performance, and submitted a technical paper and filed two provisional patents of the relative solutions.