Merge pull request #49 from ML4SCI/patch-2

Update proposal_SYMBA.md

_gsocproposals/2024/proposal_SYMBA.md

@@ -1,36 +1,49 @@
 ---
-title: Symbolic empirical representation of squared amplitudes in high-energy physics
+title: Transformer Models for Symbolic Calculations of Squared Amplitudes in HEP
 layout: gsoc_proposal
 project: SYMBA
-year: 2023
+year: 2024
 organization:
  - Alabama
  - FSU
- - Kentucky
+ - FAU
+ - QU
 ---
 
 ## Description
 
-One of the most important physical quantities in particle physics is the cross section, or a probability that a particular process takes place in the interaction of elementary particles. Its measure provides a testable link between theory and experiment. It is obtained theoretically mainly by calculating the squared amplitude (matrix M). 
+One of the most important physical quantities in particle physics is the cross section, or a probability that a particular process takes place in the interaction of elementary particles. Its measure provides a testable link between theory and experiment. It is obtained theoretically mainly by calculating the squared amplitude. The approach we use in this project is to treat the amplitude and squared amplitude as mathematical symbolic expressions and use language-translation models to map from the amplitude to squared-amplitude.
 
 ## Duration
 
 Total project length: 175/350 hours.
 
 ## Task ideas and expected results
-  * Apply symbolic machine learning techniques to predict the squared amplitudes and cross section for high-energy physics 
+  * Develop  various transformer-based models on sequence-to-sequence tasks
+  * Benchmark different models on simulated physics datasets of various complexity and sequence lengths to find the best model
+  * Integrate with the SymbaHEP pipeline
 
 ## Requirements 
-Python, C++ and some experience in Machine Learning sequence models.
+Significant experience with Transformer machine learning models in Python (preferably using pytorch).
 
-<!-- ## Test
-Please use this [link](https://docs.google.com/document/d/1eMtRPR-nH2NyituMBIDAZdmcCkZF2TyUFQp6zg-z5pA/edit) to access the test for this project. -->
+## Difficulty Level 
+Advanced
 
+<!-- ## Test
+Please use this [link](https://docs.google.com/document/d/1eMtRPR-nH2NyituMBIDAZdmcCkZF2TyUFQp6zg-z5pA) to access the test for this project. -->
 
 ## Mentors
-  * [Abdulhakim Alnuqaydan](mailto:[email protected]) (University of Kentucky)
+  * [Eric Reinhardt](mailto:[email protected]) (University of Alabama)
+  * [Abdulhakim Alnuqaydan](mailto:[email protected]) (Qassim University)
   * [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
   * [Harrison Prosper](mailto:[email protected]) (Florida State University)
-  * [Eric Reinhardt](mailto:[email protected]) (University of Alabama)
+  * [Nobuchika Okada](mailto:[email protected]) (University of Alabama)
+  * [Marco Knipfer](mailto:[email protected]) (University of Erlangen-Nürnberg)
 
 Please **DO NOT** contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and **include your CV** and **test results**. The mentors will then get in touch with you.
+
+## Links
+  * [Paper 1](https://ml4physicalsciences.github.io/2023/files/NeurIPS_ML4PS_2023_183.pdf)
+  * [Paper 2](https://iopscience.iop.org/article/10.1088/2632-2153/acb2b2)
+  * [Poster 1](https://nips.cc/media/PosterPDFs/NeurIPS%202023/76219.png)
+  * [Blog post 1](https://medium.com/@neerajanandfirst/my-journey-to-google-summer-of-code-2023-with-ml4sci-8822ce64464a)