- Linux/Unix, OSX
- R, Python, Matlab, C, Git
- Machine Learning
- Scikit - learn
- Signal Processing
- Amazon AWS
- Google Cloud, Microsoft Azure
- Apache Spark
- Rapid prototype
- Solidworks, 3D Printing
- English / Chinese
Brain Imaging Data Scientist
- Created patient enrollment R Shiny dashboard to quantify patient enrollment and team performance metrics for NIH funded clinic.
- Created automated pipelines in Shell, Python, Ansible to streamline MRI image acquisition, database storage, and analysis.
- Applied random forest classifiers on graph theory metrics to predict non - invasive brain stimulation tDCS performance.
Postdoc Fellow, Department of Psychiatry
- Developed fMRI brain imaging protocols and tasks for veterans suffering from PTSD.
- Collected and analyzed MRI/fMRI data (over 1000+ subjects, ~ 5 TB), genetic data (over 1000+ subjects, 2000+ SNPs) and eye-tracking data.
- Published 15+ peer-reviewed scientific journal articles (bit.ly/seanpapers).
- Presented at international, national and local meetings through 20+ invited talks/mini-symposiums/poster sessions.
Member, Faculty Advisor
- Assisted the city of Flint in collaboration with Google to provide lead level prediction at the 50,000+ individual parcel level.
- Applied machine learning techniques, including XGBoost, Random Forest to predict parcels with water lead level above the Federal standard 15 ppb.
- Applied machine learning techniques, including random forest classification, radiant boosting (xgboost) and deep learning networks (Mxnet & Theano), to create prediction models.
- Disseminated prediction results and data visualization through Google’s mobile app for Flint residents.
Postdoctoral Researcher in Psychology
- Worked on individual differences in drug addiction, social bonding in animal model and its interaction with neurotransmitter, Dopamine.
Graduate Research Assistant
- Working on animal models for Parkinson's Disease (PD) with focus on voice deficits that PD patients suffer.