Statistical Learning
Uncertainty-aware prediction, model diagnostics, and robust evaluation.
M.S. Statistics Student
I focus on modern statistics, machine learning systems, and practical applications in finance. My goal is to bridge rigorous inference with scalable implementation.
Currently exploring uncertainty-aware modeling, reproducible ML workflows, and quantitative decision tools.
About
I am a graduate student in Statistics with a strong interest in computer science applications, including ML engineering, data infrastructure, and probabilistic modeling.
Prior to graduate studies, I worked with finance datasets and decision workflows, shaping my interest in risk modeling, forecasting, and explainable analytics.
Research Interests
CS + Data Systems
Efficient experimentation pipelines, reproducibility, and deployment workflows.
Quantitative Finance
Risk forecasting, time-series analysis, and decision-support tooling.
Featured Projects
Project One — Bayesian Time-Series Dashboard
Built a probabilistic forecasting app to compare posterior predictive intervals across macro and market indicators.
Project Two — ML Pipeline Monitoring Toolkit
Developed automated model drift checks and alerting for production data workflows.
Project Three — Portfolio Risk Scenario Lab
Simulated stress scenarios and sensitivity metrics to support robust allocation decisions.
Experience
- Graduate Research Assistant
- Data Analyst (Finance)
- Teaching Assistant, Intro Statistics
Contact
I’m open to research collaboration, internships, and technical discussions in statistics, ML systems, and quantitative finance.
Email: your.email@domain.com · LinkedIn: linkedin.com/in/your-handle · GitHub: github.com/your-handle