GSoC 2025 with CERN-HSF | Aditya Pandey

I’m excited to share that I’ve been selected for Google Summer of Code 2025 with CERN-HSF, working on “Using ROOT in the field of genome sequencing.”

What is this blog about?

This blog will document my Google Summer of Code experience with CERN-HSF, focusing on applying the ROOT framework to genomic data analysis.

Why CERN-HSF?

I’ve been interested in CERN since learning about the Higgs boson discovery in 2012. The technology behind particle physics research always fascinated me, so when GSoC 2025 applications opened, CERN-HSF was my top choice.

The HSF (HEP Software Foundation) operates as an umbrella organization for high-energy physics software projects. What attracts me to this field is how particle physicists tackle fundamental questions about the universe using cutting-edge computational tools.

Project discovery

I spent time exploring the HSF project ideas published on February 11 and found two that matched my interests:

Project 1: TMVA SOFIE - HLS4ML Integration

This focused on machine learning inference optimization within ROOT’s TMVA toolkit. The goal was integrating hls4ml with SOFIE to enable efficient ML model inference, converting models from Keras, PyTorch, and ONNX formats into optimized C++ code.

Project 2: Using ROOT in Genome Sequencing

This involved applying ROOT’s data processing capabilities to genomic data storage and analysis, using particle physics tools for biological research.

Following the selection process

CERN-HSF has a structured two-phase selection process due to the high number of applicants.

Phase 1 (Feb 27 to March 24): Pre-selection and evaluation tests

Following the guidelines, I waited until February 27 to contact mentors. I sent emails for both projects, attaching my CV and explaining my motivation for each choice.

Both project mentors responded with evaluation tests.

For TMVA SOFIE / HLS4ML, the test evaluated:

C++ and Python programming skills
Understanding of machine learning frameworks (PyTorch, ONNX)
Knowledge of hls4ml architecture and high-level synthesis concepts
Familiarity with ROOT’s TMVA system
Understanding of model optimization and inference techniques

For Using ROOT in Genome Sequencing, the test covered:

C++ and Python programming proficiency
Understanding of ROOT framework and its data structures
Knowledge of genomic data formats (SAM/BAM files)
Familiarity with bioinformatics tools and workflows
Understanding of data compression and storage optimization

Both tests were challenging and required practical coding skills alongside theoretical knowledge. The mentors emphasized that the tests were private, solutions should be personal, and response time was part of the evaluation.

Phase 2 (March 24 to April 8): Proposal development

By April 1, I received emails from both mentors that I had passed the evaluation tests. This began the second phase, where I discussed project ideas, timelines, and objectives with mentors.

The mentors helped me develop detailed proposals for both projects. The application deadline was April 8, and I submitted 3 proposals.

The selection results

On May 8, Google announced the accepted student projects. Earlier I received an email from one of the organization admins saying I was selected for 2 projects and needed to make a choice.

It was good to know that I had been selected for both projects. That meant both proposals and evaluation performances were strong enough for acceptance through the competitive two-phase process.

Making the decision

Choosing between the two projects was difficult since both offered unique learning opportunities. I ultimately chose “Using ROOT in the field of genome sequencing” because:

Interdisciplinary nature: it combines computational physics with bioinformatics
Novel application: ROOT isn’t commonly used for genomic data analysis
Practical relevance: genomic datasets are growing rapidly and need efficient storage solutions
Technical innovation: working with ROOT’s new RNTuple format alongside traditional bioinformatics tools

Also the most important factor was that my previous GSoC was in bioinformatics, so it made sense to streamline my experience. Seeing Google DeepMind’s work in the field made it more interesting too. I also became part of Princeton University’s Compiler Research Team, working with some of the finest developers.

Building on previous experience

This isn’t my first GSoC participation. My previous experience taught me valuable lessons about:

Managing project scope and setting realistic milestones
Maintaining regular communication with mentors and the community
Documenting progress systematically (mandatory for CERN-HSF students)
Writing clean, maintainable code for open source projects

That experience helped me navigate the structured selection process and better evaluate which project would be the best fit.

Project overview

The selected project focuses on extending GeneROOT capabilities through:

Reproducing previous results and validating comparisons against ROOT master
Compression analysis comparing ROOT’s strategies with Samtools for BAM/RAM conversions
RNTuple implementation for efficient genomic data storage
File splitting techniques and different ROOT file splitting approaches
Performance benchmarking and comparison reports

ROOT typically achieves 10-50% smaller file sizes and multiple times faster read throughput compared to traditional formats. Applied to genomic data, this could improve storage efficiency and analysis speed for research institutions.

Working with my mentors

I’ll be working with:

Martin Vasilev from University of Plovdiv
Vassil Vassilev from Princeton University
Fons Rademakers from CERN

These developers have extensive experience with ROOT and particle physics data analysis.

What’s coming next

I’m currently working on the project and have done most of the part involving the RNTuple converter.

In upcoming posts, I’ll document technical implementation details, performance comparisons, challenges in interdisciplinary software development, and lessons learned.

Thanks for reading! I’m looking forward to contributing at this intersection of particle physics and genomics research.