Update contributors.yml with project information.

_data/contributors.yml

@@ -14,7 +14,7 @@
     David serves as the principal investigator for the NSF-funded components of
     the project, ensuring both administrative and technical coherence across its
     various work streams. He also contributes as a mentor in programs such as
     Google Summer of Code (GSoC), IRIS-HEP, and others.
 
 - name: Vassil Vassilev
   info: Research Staff
@@ -59,7 +59,7 @@
   associated: 1
   org: University of Plovdiv "Paisii Hilendarski"
   responsibilities: |
     Martin has research interest in the area of the compiler construction, analysis of complex software systems and bioinformatics.
     He has obtained his PhD in 2020 in the University of Plovdiv "Paisii Hilendarski" where he continued to develop his academic career.
 
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@@ -73,39 +73,39 @@
   education: "B. Tech in Computer Science, Manipal Institute of Technology, Manipal, India"
   active: 1
   projects:
     - title: "Improve robustness and performance of Cppyy by bringing it closer to LLVM"
       status: Ongoing
       description: |
         Developing CppInterOp, an interoperability mechanism between Python and C++ based on the interactive C++ technologies available in Clang and LLVM.
         This involves modernizing Cppyy, to use upstream LLVM’s Clang-REPL component as a runtime compiler.
         We also explore the added performance gain due to fewer dependencies, faster lookups as well as the expanded feature set that can be made available to Cppyy users on schedule with upstream Clang-REPL releases.
         The project aims to stabilize CppInterop with Clang-REPL and Cling, as well as develop its capabilities with template instantiation and lookups.
         Another aspect is exploring the optimizations of the current Python-C++ interoperability framework utilizing Numba.
         Numba allows Python users to Just-In-Time compile subsets of mixed Python-C++ code into machine code which can accelerate codebases that use cppyy by 20 times.
         Current research aims to expand capabilities to enable users to leverage CUDA C++ code in Python and subsequently utilize Numba for its JIT compilation.
       mentors: Vassil Vassilev, Wim Lavrijsen(LBNL)
     - title: "Extend the Cppyy support in Numba"
       status: Completed
       description: |
         Numba is a JIT compiler that translates a subset of Python and NumPy code into fast machine
         code. Cppyy is an automatic, run-time, Python-C++ bindings generator, for calling C++ from
         Python and Python from C++.
         Cppyy has to pay a time penalty each time it needs to switch between languages which can
         multiply into large slowdowns when using loops with cppyy objects. This is where Numba can
         help. Since Numba compiles the code in loops into machine code it only has to cross the
         language barrier once and the loops thus run faster.
         Initial support for Cppyy objects in Numba enabled the use of builtin types and classes, but
         some essential C++ features, such as references and STL classes, are not yet supported.
         The project aims to add support for C++ reference types in Numba through Cppyy and improve
         the existing numba extension implementation to provide general support for C++ templates.
         This added support will allow cppyy users to define a wider array of standard and templated
         functions that can leverage reference types to the C++ code defined in python.
       mentors: Baidyanath Kundu, Vassil Vassilev, Wim Lavrijsen(LBNL)
       proposal: /assets/docs/Aaron_Jomy_Proposal_2023.pdf
       report: |
         [Final Report](/assets/docs/Aaron_Jomy_GSoC23_Report.pdf)
 
 - name: Petro Zarytskyi
   info: "IRIS-HEP Fellow"
   photo: Petro.jpg
   email: [email protected]
@@ -126,7 +126,7 @@
         code that does a lot of unnecessary calculations. With advanced activity analysis,
         the variables which are not used to compute the result can be found and removed,
         increasing the time- and memory- efficiency of the output code.
       proposal: /assets/docs/Petro_Zarytskyi_Proposal.pdf
       mentors: Vassil Vassilev, David Lange
 
 - name: "Maksym Andriichuk"
@@ -142,7 +142,7 @@
       description: |
         Clad has undergone significant optimization in the recent years. One such optimization is the To-Be-Recorded optimization which reduces the memory pressure to the clad tape data structure. The next step is to implement the activity analysis which discards all statements which are irrelevant for the generated code. That is, if the statements do not depend on the input/output variables of a routine in a differentiable way, they are ignored. Combining both analyses could result in a much greater performance, which is our ultimate goal.        
       proposal: /assets/docs/Maksym_Andriichuk_Proposal_2024.pdf
       mentors: Vassil Vassilev, David Lange, Petro Zarytskyi
 
 - name: Matthew Barton
   info: "Open Source Contributor"
@@ -154,17 +154,23 @@
 - name: Sahil Patidar
   info: "Google Summer of Code 2024 Contributor"
   email: [email protected]
   education: "Computer Science and Engineering, Vindhya Institute of Technology, India"
   github: "https://github.com/SahilPatidar"
   active: 1
   projects:
-    - title: "Out-Of-Process execution for Clang-Repl"
+    - title: "Advanced symbol resolution and reoptimization for clang-repl"
       status: Ongoing
+      description: |
+        This project aims to enhance Clang-Repl, an interactive C++ interpreter built on top of LLVM’s ORC JIT infrastructure. Currently, Clang-Repl lacks a mechanism to automatically load dynamic libraries when encountering unresolved symbols. As a result, users must manually load the appropriate libraries if a symbol used in their code resides in a specific dynamic library. To address this limitation, we propose a solution that enables automatic library loading for unresolved symbols. Additionally, the second goal of this project is to introduce support for re-optimization within Clang-Repl, allowing code to benefit from improved performance through dynamic optimization techniques.
+      mentors: Vassil Vassilev
+      proposal: /assets/docs/SahilPatidar_GSoC2025_Proposal.pdf
+    - title: "Out-Of-Process execution for Clang-Repl"
+      status: Completed
       description: |
         This project focuses on enhancing Clang-Repl, an interactive C++ interpreter
         that leverages LLVM's JIT infrastructure. The current in-process execution model
         poses challenges, especially for resource-constrained devices and stability.
         By transitioning Clang-Repl to an out-of-process execution model, this project
         aims to address these limitations. It will improve resource efficiency, making
         Clang-Repl usable on devices with limited resources, and enhance crash resilience,
         preventing user code failures from crashing the entire interpreter. This shift will
@@ -195,7 +201,7 @@
         using Clad. These produced kernels will compute the derivative of an argument specified by the user as the output based on an input parameter 
         of their choosing. In addition, the user must be able to call these kernels with a custom grid configuration. 
       proposal: /assets/docs/Christina_Koutsou_GSoC_2024.pdf
       mentors: Vassil Vassilev, Parth Arora, Alexander Penev
 
 - name: "Vipul Cariappa"
   photo: "Vipul-Cariappa.jpg"