Adil Hussain

My research:

I’m working on understanding neutrino interactions using the MicroBooNE liquid argon time projection chamber (LArTPC). This involves combining reconstruction, truth-matching, particle identification (PID), and detailed event classification to better understand nuclear effects and model systematics.

My expertise is:

I’m proficient in LArTPC event reconstruction, ROOT-based data analysis, and statistical techniques for signal-background separation

A problem I’m grappling with:

Accurately modeling and separating the background contributions

I’ve got my eyes on:

Data-driven background estimation methods

I want to know more about:

Advanced machine learning approaches for event classification in LArTPCs, improved neutrino-nucleus interaction modeling (especially nuclear effects and final state interactions), and how to better constrain systematics related to detector response and flux uncertainties.