Hello, I’m Yeonbi Han PhD Student in Statistics MD, Korean Medicine

PhD Student in Statistics
with expertise in Medicine

I am a PhD student in Applied Mathematics and Statistics with a clinical background in Korean Medicine. I work at the intersection of biostatistics, statistical programming, and healthcare data science, with interests in clinical research, real-world data, and evidence-based decision-making.

20+

Completed
Projects

3

Years
of Experience

2

Healthcare professional Licenses

SERVICES

What Can I Offer ?

Data Analysis

Analyzed large-scale clinical trial data using Python (Scikit-learn, TensorFlow, PyTorch) and R, extracting patterns through machine learning algorithms to improve the efficiency of healthcare systems. Additionally, utilized SQL for database management, performing complex queries to retrieve and manipulate structured data for more effective analysis and decision-making.

Projects

Machine Learning & Artificial Intelligence

Developed predictive models for healthcare data using linear regression, logistic regression, support vector machines (SVM), k-nearest neighbors (kNN), decision trees, and ensemble methods. Additionally, worked on Natural Language Processing (NLP) tasks utilizing RNN and CNN models, contributing to improved patient outcome predictions and research advancements.
Utilized key Python libraries such as TensorFlow, PyTorch, and Scikit-learn for building machine learning models, and employed NumPy and Pandas for data processing.

Projects

Data Visualization

Utilized Python's Matplotlib, Seaborn, along with Tableau, Canva, and Flourish to visualize complex medical data, delivering effective visual insights to clinical scientists and clinical research physicians for better understanding.

Projects

Mathematical skills

Utilized a strong mathematical foundation to analyze large-scale clinical data, applying advanced techniques from probability and statistics, geometry and vectors, linear algebra, calculus III, and discrete mathematics. These mathematical methods were crucial in extracting key insights and improving the interpretation of complex medical data, contributing to more accurate predictions and enhanced decision-making in healthcare research and systems.