Gang Liu
Adjunct Assistant Professor @Wake Forest University School Of Medicine
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WORK HISTORY
Adjunct Assistant Professor @Wake Forest University School Of Medicine
NC, US
Teaching: Designing and delivering courses in biostatistics and data science, ensuring the curriculum is current and aligns with industry standards. Providing engaging and effective instruction to students at various academic levels. Research Contribution: Actively participating in research activities within the department, collaborating with colleagues on projects, and contributing to advancements in biostatistics and data science. Publishing research findings in reputable journals and presenting at conferences. Curriculum Development: Contributing to the development and enhancement of curriculum materials, staying informed about emerging trends and technologies in biostatistics and data science, and integrating relevant advancements into course content.
EDUCATION
Shanghai Jiao Tong University
Bachelor of Business Administration
University of South Florida
Doctor of Philosophy (Ph.D.), Industrial Engineering
Shanghai Jiao Tong University
Bachelor of Science (B.S.), Mechanical Engineering
ABOUT GANG LIU
Results-driven Data Scientist/Research Scientist with over a decade of experience developing and designing statistical methods and machine/deep learning models to drive actionable insights in financial and clinical domains, proficient in leveraging data to minimize risk, forecast trends, and solve business challenges.• Developed expertise in Regression and Generalized Linear Models, Model Validation, Time Series Forecasting, Clustering, Causal Inference, Bayesian Statistics, Generative AI, LLM, Optimization and Simulation.• Excellent hands-on skills in machine learning and deep learning (e.g. Python and R), statistical analysis (e.g, SAS, R, and Excel), data visualization (e.g, Tableau and Power BI), database (e.g, MySQL and MongoDB), and optimization (e.g, Simpy, CPLEX). • Modules/Packages: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, TensorFlow, PyTorch, Langchain, AutoGen, LangGraph, LangSmith, MLflow, Airflow, LlamaIndex, PyMC, Stan, SpaCy, Keras, SciPy, SimPy, osmnx, geopandas, folium, networkx, pykrige, Transformers, R-Shiny, R- ggplot2, R- caret, R-Bioconductor, Docker, Git/Github• Data Management: EPIC, Caboodle Data Warehouse, Clarity, SlicerDicer, Reporting Workbench, REDCap, Business Objects, i2b2, and GCP Cloud Computing.Areas of my expertise:• Signal processing and time series analysis• Machine learning, deep learning, Gen AI, and LLMs• Biostatistics• Health economics• Operation Research• Data reporting and visualization
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