Data scientist with a Ph.D. in Applied Mathematics (May 2025), specializing in Bayesian modeling, Uncertainty Quantification, and Machine Learning. I design algorithms and implement them on models that extract insights from data, quantify risk, and optimize decision-making in complex systems. My work integrates mathematical rigor with scalable computation, delivering predictive tools that drive impactful, real-world solutions
- Developed an efficient parameter subset selection algorithm to identify key drivers in predictive models
- Enhanced Bayesian parameter estimation pipelines to improve predictive accuracy and model interpretability
- Built and validated virtual populations for synthetic data generation, supporting model-informed decision-making
- 🧪 Projects: Published Results
- Bayesian statistics & uncertainty quantification
- End-to-end ML pipelines
- Turning complex math into real-world impact
- Fav Quote: Good science starts with uncertainty. Bayesian thinking lets us turn doubt into direction!!!
I’m a swimmer, and my favorite style is butterfly. It’s how I approach modeling problems: rhythm, focus, and flow
