# Aditya Joshi

> AI/ML Engineering, Research & Technical Systems


## Posts

- [A Better Way to Write Project Notes](https://dev.aditya-joshi.com/a-better-way-to-write-project-notes/): A project page earns its space when a reader can follow the problem, the engineering choices, and the evidence. Here is a structure that makes those decisions legible.
- [Reproducing a Paper Starts With Its Assumptions](https://dev.aditya-joshi.com/reproducing-a-paper-starts-with-its-assumptions/): A method section is more than a recipe. Reading its assumptions closely helps distinguish a faithful reproduction from a result that merely uses similar code.
- [A Model Score Is Not a Decision](https://dev.aditya-joshi.com/a-model-score-is-not-a-decision/): A strong metric can still hide the failure that matters in practice. Start evaluation by asking what decision the model will support—and what a mistake costs.

## Pages

- [Contact](https://dev.aditya-joshi.com/contact/): For research collaborations, engineering inquiries, and speaking invitations, send a note below.
- [Resume](https://dev.aditya-joshi.com/resume/): A structured summary of engineering roles, research appointments, technical toolsets, academic training, and honors. Designed to be read on screen or printed.
- [About](https://dev.aditya-joshi.com/about/): I work across industry-grade machine learning systems and academic investigation, with a steady focus on model optimization, evaluation methodology, and robust ML systems.
- [Explore](https://dev.aditya-joshi.com/blog/): Longform writing on model mechanics, evaluation frameworks, training dynamics, and system architecture, with the assumptions made visible.
- [Lab](https://dev.aditya-joshi.com/lab/): Experimental software, algorithmic demonstrations, and proof-of-concept models you can adjust and run directly in the browser.
- [Research](https://dev.aditya-joshi.com/research/): My research sits at the intersection of applied machine learning and algorithmic efficiency.
- [Work](https://dev.aditya-joshi.com/work/): Machine learning pipelines, model optimization, and backend infrastructure, each documented with its constraints, architectural choices, and measured outcomes.
- [Home](https://dev.aditya-joshi.com/): I'm Aditya Joshi, an AI/ML engineer and researcher.


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