Current direction
I am still at the stage of learning broadly and discovering which questions I want to pursue in depth. My current priorities are probability, mathematical statistics, analysis, optimization, and numerical computation, together with paper reading, clear writing, and reproducible project work.
Areas I am exploring
These are current interests rather than a fixed research agenda. They reflect questions that have come up in my courses and projects.
I want to understand how assumptions, uncertainty, and validation choices affect the conclusions we draw from data.
What assumptions are needed before a statistical conclusion is credible?
How can validation reveal where a model works, where it fails, and how uncertain the comparison is?
I am interested in learning how mathematical models can clarify decisions with constraints, uncertainty, and several interacting stages.
How can a complicated real-world problem be reduced to a model that is useful without hiding important assumptions?
How do robustness, interpretability, and computational cost change an optimization strategy?
Project experience & training
My experience currently consists of one completed computational research project, modeling and programming projects, seminar participation, guided reading, and presentation practice.
Foundations
My aim is to build enough mathematical and statistical depth to ask better questions, understand assumptions, and evaluate computational results more carefully.
I am learning to read a paper by asking what problem it addresses, which assumptions it makes, what evidence it uses, and how the result is checked.
These courses are helping me build the language needed to reason more carefully about uncertainty, estimation, and model evaluation.
Proof-oriented courses and numerical practice are helping me define problems more precisely and connect mathematical ideas with computation.