Statistical Analysis & ML for Publication Ready Research

Overview

I support researchers with statistical analysis and machine learning that is reproducible and manuscript ready. The goal is not just results, but clear diagnostics, honest limitations, and outputs you can explain to reviewers.

Ideal collaborators

Graduate students, labs, and applied teams working with soil, plant, water, or spectroscopy datasets who want solid analysis and clean figures without overstating what the data can support.

What you get

• QA and QC checks, tidy data tables, and clear variable definitions
• Exploratory analysis that surfaces confounders, outliers, and data gaps early
• Benchmarking from simple baselines to machine learning and CNN models when appropriate
• Diagnostics and error analysis that explain where models fail and why
• Publication quality figures and tables plus a short methods notes block you can adapt

Inputs to start

• Dataset and metadata with units, sampling design notes, and any lab protocols
• Primary question and what success looks like, including target metrics
• Constraints on interpretability, model complexity, or computational limits
• Any draft methods text, reviewer comments, or journal expectations you want to meet

Workflow

1. Clarify the question, the design, and the evaluation plan
2. Clean and explore the data, then lock the modeling dataset
3. Run baselines and the selected models with consistent validation
4. Package results as figures, tables, and reproducible notebooks with brief notes

Typical outcomes

• A reproducible analysis bundle with notebooks, figures, and tables
• A clear comparison of model options with diagnostics and interpretation guidance
• Draft methods and results phrasing that matches what the analysis actually supports