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Author: Ariam-Acevedo

_codas-hep-students/2026/Ariam-Acevedo.md

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+---
+layout: codas-hep-participant
+e-mail: acevedolopez.1@osu.edu
+institution: The Ohio State University
+name: Ariam J. Acevedo Lopez
+photo: "/assets/images/codas-hep/2026/Ariam-Acevedo.jpg"
+github-username: Ariam-Acevedo
+orcid: https://orcid.org/0000-0002-3843-3595
+title: PhD Student
+logos:
+    - /assets/images/codas-hep/logos/dune-logo.jpeg
+    - /assets/images/codas-hep/logos/2x2.png
+---
+## My research:
+
+My research is focused on the 2x2 prototype of the near detector for the Deep Underground Neutrino Experiment (DUNE).
+
+## My expertise is:
+
+Neutrino physics. Accelerator neutrinos.
+
+## A problem I’m grappling with:
+
+I am currently working with detector systematics. We study these by introducing nuisance parameters to describe the uncertainty in the detector response parameters and marginalizing over them in the analysis. The problem with the way they are currently implemented is that we regenerate simulations for each nuisance parameter, which is computationally expensive. Right now, I am trying to learn how to run simulations that are useful for understanding detector systematics and to use simulation-based inference to model them.
+
+## I’ve got my eyes on:
+
+Neural likelihood estimators. This technique can be useful for my systematics problem. Also, I’ve got my eyes on ghost hit identification and mitigation. A problem we face with LArTPCs is noise introduced by the electronics. As we require  strong performance in the near detector of DUNE, we must reduce this noise on the first implementation of a pixelated LArTPC.
+
+## I want to know more about:
+
+Parallel computing: I would like to know how to write programs that can be divided into multiple CPUs and GPUs. Machine Learning: New techniques and good management of large datasets that can be used for training. I also would like to know about other high-energy physics experiments and physics beyond the Standard Model.