Confidence that matches the evidence.

I am Ilhame Ait Lbachir, a computer science professor and AI researcher. I work on AI you can actually trust: uncertainty quantification, explainability, and selective prediction for breast cancer detection in mammography. I publish in IEEE and Springer venues, and I have reviewed more than fifty papers for other researchers.

Calibrated Researcher is where I share how that work really happens. Not the polished version. The real one. How a paper gets written, where proposals fall apart, what reviewers actually look for, and how to use AI to make your work stronger instead of louder. The name says the standard I hold myself to: confidence that matches the evidence, never more.

If you are a researcher who wants to do rigorous work and get it past the reviewers, you are in the right place. Follow along, and the courses will come to the people who found the writing useful first.

Teaching is a large part of how I think, and much of what I share here started in a classroom. At ENSA Berrechid I teach artificial intelligence, business intelligence, operating systems and Unix, web development with React and TypeScript, and research methodology to engineering students. In earlier posts I taught programming from the ground up: Java, PHP, the C language, and object oriented programming in C# and Java. Explaining hard ideas until they click is the same skill that makes research clear on the page.