GSoC-2025

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Mar 25

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using ChartGPT

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Based on your background in mechanical engineering, IoT, microcontrollers, cloud technology, security (DevOps/DevSecOps), and physics/mathematics, here are the best GSoC 2025 projects from CERN that align with your skills: Web Site

Top Project Recommendations

1. CernVM-FS: Evaluate Distribution of ML model files on CVMFS
   • Why? You have experience in cloud computing and DevOps. This project focuses on distributing ML models efficiently in a cloud-like environment using CernVM-FS (CERN Virtual Machine File System).

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2. Ganga: Incorporate a Large Language Model to assist users
   • Why? Since you are interested in AI/ML and DevSecOps, this project involves integrating an LLM into Ganga, a Python-based tool for managing large-scale physics workflows.

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3. Clad: Enhancing LLM Training with Clad for efficient differentiation
   • Why? If you're interested in AI/ML and performance optimization, this project enhances training for large models using automatic differentiation in Clad, a tool that optimizes C++ computations.

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4. CppInterOp: Implement CppInterOp API exposing memory, ownership, and thread safety information
   • Why? You have C++ and Python knowledge, and this project focuses on improving C++ interoperability, which is useful in high-performance computing and DevSecOps.

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5. MCnet: MCnet/OpenData - tools and exercises for open-data exploration with MC simulations
   • Why? Since you're a physics and mathematics lover, this project involves Monte Carlo simulations for particle physics. It will allow you to work with open data and scientific computing tools.

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6. SMARTHEP: Estimating the energy cost of ML scientific software
   • Why? If you're interested in cloud cost optimization (FinOps), this project estimates the energy cost of ML workloads, which is useful in DevOps and cloud resource management.

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7. ✅ BEAD: Background Enrichment augmented Anomaly Detection (BEAD) for new physics searches at LHC
   • Why? You have experience in data analysis (Taguchi method, Minitab software). This project focuses on anomaly detection in physics data, which is closely related to your IoT data analysis projects.

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Alternative Choices (If you want to focus more on security & IoT)

• Xeus-Cpp: Implementing Debugging Support (Good for C++ debugging in scientific computing)
• ROOT: RNTuple in JSROOT (If you want to work on structured data for physics applications)
• ML4EP: TMVA SOFIE - GPU Support for Machine Learning Inference (If you're interested in AI acceleration & DevOps)

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Best Strategy

• Step 1: Pick a project that aligns with your DevOps/DevSecOps, cloud, or IoT expertise.
• Step 2: Start learning and contributing to the project's GitHub repo (if available).
• Step 3: Contact the project maintainers or mentors early to express interest.
• Step 4: Build a small demo project related to the topic to showcase your skills in the proposal.

Final Recommendation

Your best pick is CernVM-FS (Cloud & DevOps) or SMARTHEP (FinOps & ML cost estimation), followed by Ganga (AI in scientific computing) or BEAD (Physics & data analysis).

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✅ This BEAD project in GSoC 2025 aligns well with your data analysis, cloud computing, and machine learning interests, given your experience with Minitab for electroplating optimization, IoT projects, and DevOps/DevSecOps knowledge.

Why BEAD is a Good Fit for You?

1. Data Analysis Experience – You have worked with Taguchi analysis in Minitab, making you familiar with pattern recognition and optimization, which is crucial for anomaly detection in physics data.
2. Python & ML – You have some experience with Python, and this project involves deep learning, auto-encoders, transformers, and Graph Neural Networks (GNNs).
3. Linux & Cloud Knowledge – Since you are interested in cloud computing & DevOps, working with Python in a Linux environment will be a natural extension.
4. Physics Background – Your love for physics and mathematics makes this project ideal, as it involves particle physics, high-energy physics (HEP) datasets, and unsupervised learning techniques.

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How to Prepare for BEAD GSoC 2025?

1. Learn Unsupervised ML Algorithms:

   • Study autoencoders, transformers, GNNs, and probabilistic models.
   • Explore Anomaly Detection in Machine Learning (Isolation Forest, Variational Autoencoders, etc.).

2. Get Comfortable with BEAD’s Tech Stack:

   • Python (NumPy, Pandas, SciPy, Matplotlib)
   • PyTorch (for Deep Learning models)
   • Jupyter Notebooks (for visualizing data)
   • ROOT Framework (used in HEP data processing)
   • Linux (basic CLI, working with servers)

3. Start Contributing to BEAD’s GitHub Repository:

   • Check open issues and documentation.
   • Try replicating their results on small datasets.
   • Contact mentors early (Pratik Jawahar, Sukanya Sinha) to ask how you can contribute before GSoC starts.

4. Practice Physics-Based Data Analysis:

   • Work on simulated physics datasets to understand event-level & object-level data.
   • Use Matplotlib for visualization.
   • Study HEP data representation (ROOT, CERN Open Data).

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Final Steps

• Choose a Task Idea: Either develop a new autoencoder model or test models on different datasets.
• Write a Strong Proposal: Clearly explain how your past experience (Minitab, IoT, Python, Cloud) will help improve BEAD.
• Reach Out to Mentors on CERN’s GSoC Discussions (ask for guidance on starter issues).

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proposal example

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May 9 2025, 00:19