Mark Vakulenko

My research:

As part of the Calorimeter Data Quality Monitoring (Calo DQM) team within the Mu2e experiment at Fermilab, I’ve developed and now maintain a C++ module that processes and visualizes CaloDigi data. Depending on the .fcl configuration, it either organizes ROOT histograms by channel, board, and disk into a structured .root file, or streams them to the otsdaq system for real-time monitoring and diagnostics.

My expertise is:

I’m comfortable managing unfamiliar technical challenges. I’ve written C++ modules for detector data, worked with complex nested data structures, and learned tools like ROOT and otsdaq. I focus on deeply understanding the task, learning quickly, and building robust, maintainable solutions.

A problem I’m grappling with:

I’m exploring how to grow from solving technical problems to identifying and shaping opportunities for original, high-impact contributions.

I’ve got my eyes on:

I’m aiming to integrate C++, Python, ROOT, and machine learning into the Mu2e experiment to create a novel diagnostic or analytical tool. This direction brings together my interests in systems-level programming, data science, and high-energy physics, and will form the foundation of my master’s thesis.

I want to know more about:

Practical strategies for integrating GPU computing, parallelization, and machine learning techniques into large-scale physics data pipelines.