Graduate student in Quantitative Biomedical Research Center (QBRC). My major research involved: 1) Developing machine learning, and deep learning models to predict anti-cancer drug sensitivity and drug synergistic effect using high-throughput sequencing data. 2) Developing Bayesian statistical models to analyze methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data, and understand cellular methylation regulation events.3) Developing statistical methods to infer gene regulatory network using high-throughput sequencing data.

Data scientist intern at Uber for business team. Building deep learning model to forecast organization life time value (LTV).

Summer internship at Microsoft Satori knowledge graph team.1) Developed a convolutional neural network model to extract entity relations from text (achieved 93% accuracy in BioNLP dataset). 2) Applied machine translation model to translate English entity information into Chinese to enrich Chinese knowledge graph content.

Development and synthesis of small molecules that modulate transcription factor FOXM1 for breast cancer treatment.