Fast diffusion GSoC project (#1497)

* Create project_Geant4.md

* Update mentors.md

* Create proposal_Geant4-fast_diffusion.md

* Update mentors.md

_gsocprojects/2024/project_Geant4.md

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+---
+project: Geant4
+layout: default
+logo: Geant4-logo.png
+description: |
+  [Geant4](https://geant4.web.cern.ch/) is a toolkit for the simulation of the
+  passage of particles through matter. Its areas of application include high
+  energy, nuclear and accelerator physics, as well as studies in medical and space
+  science. The three main reference papers for Geant4 are published in Nuclear
+  Instruments and Methods in Physics Research A 506 (2003) 250-303,  IEEE
+  Transactions on Nuclear Science 53 No. 1 (2006) 270-278 and Nuclear Instruments
+  and Methods in Physics Research A 835 (2016) 186-225.
+summary: |
+  Geant4 is a toolkit for the simulation of the passage of particles through matter.
+---
+
+{% include gsoc_project.ext %}

_gsocproposals/2024/proposal_Geant4-fast_diffusion.md

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+---
+title: Geant4-FastSim - Fast inference of Diffusion models
+layout: gsoc_proposal
+project: Geant4
+year: 2024
+difficulty: medium
+duration: 350
+mentor_avail: June-October
+organization:
+  - CERN
+---
+
+## Description
+
+In high-energy physics experiments such as the [Large Hadron Collider](https://home.cern/science/accelerators/large-hadron-collider) (LHC), some particles interact electromagnetically and/or hadronically with the material of the calorimeter, creating cascades of secondary particles, or showers. Describing the showering process relies on simulation methods that precisely define all particle interactions with matter. A detailed and accurate simulation is based on the Geant4 toolkit. The simulation of showers, with large amounts of particles created and tracked, is inherently slow. Alternatively, machine learning techniques such as generative models are used to speed up the generation of showers in a calorimeter, i.e., simulating the calorimeter response to certain particles.
+
+Considering this, we are investigating a few different kinds of generative models such as VAE, VQ-VAE, and Diffusion based on transformer architecture. Diffusion models have proven to be significantly more accurate than others, which is what we need. However, these diffusion models come at the cost of slow inference. Therefore, this project aims to make the inference of diffusion models faster.
+
+Furthermore, a byproduct of this project is that the student will get to work with diffusion transformer models which are currently at the forefront of AI research and learn to use them in the context of high-granularity shower data (a part of [CaloChallenge](https://calochallenge.github.io/homepage/)).
+
+## First Steps
+
+1. Understand how transformers and diffusion models work.
+2. Understand the [shower data](https://zenodo.org/record/6366324#.Y-DJ9ezMKdY).
+3. Propose ideas (with justification) for faster inference of diffusion models.
+
+## Project Milestones
+* Survey promising methods to make diffusion faster.
+* Implementing and experimenting with those methods.
+* Perform ablation studies around time vs accuracy
+
+
+## Expected Results
+
+A fast and accurate diffusion model for fast simulation of calorimetry showers.
+
+## Requirements
+
+* Solid knowledge of ML, DL, and Transformers
+* Strong Python, TensorFlow/PyTorch skills
+
+## Evaluation Tasks
+
+Python and ML exercises.
+
+## Mentors
+* [Piyush Raikwar](mailto:[email protected]) (CERN)
+* Peter McKeown
+
+## Links
+* [Repo](https://gitlab.cern.ch/fastsim/diffusion4sim/-/tree/DiT_renato?ref_type=heads)
+* [G4FastSim](https://g4fastsim.web.cern.ch/)

gsoc/2024/mentors.md

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 * Benedikt Hegner [[email protected]](mailto:[email protected]) CERN
 * Wim Lavrijsen [[email protected]](mailto:[email protected]) CompRes
 * Alexander Penev [[email protected]](mailto:[email protected]) CompRes
+* Piyush Raikwar [[email protected]](mailto:[email protected]) CERN
 * Vaibhav Thakkar [[email protected]](mailto:[email protected]) CompRes
 * Vassil Vassilev [[email protected]](mailto:[email protected]) CompRes
 * Valentin Volkl [[email protected]](mailto:[email protected]) CERN