Update information about Petro Zarytskyi

.github/actions/spelling/expect.txt

@@ -1,7 +1,9 @@
-Reoptimization
-Resugaring
-optimizing
+copyable
+Galin
 genomics
+optimizing
+Reoptimization
 reoptimization
+Resugaring
 sustainability
 transitioning

.github/actions/spelling/names.txt

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+Bistrev
+ICHEP

.github/actions/spelling/terms.txt

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+backpropagation

_data/contributors.yml

@@ -121,14 +121,35 @@
         [Final Report](/assets/docs/Aaron_Jomy_GSoC23_Report.pdf)
 
 - name: Petro Zarytskyi
-  info: "IRIS-HEP Fellow"
+  info: "Google Summer of Code 2025 Contributor"
   photo: Petro.jpg
   email: [email protected]
   education: Applied Mathematics, Taras Shevchenko National University of Kyiv, Ukraine, 2021-present
   active: 1
   projects:
-    - title: "Optimizing reverse-mode automatic differentiation with advanced activity-analysis"
+    - title: "Improve automatic differentiation of object-oriented paradigms using Clad"
       status: Ongoing
+      description: |
+        Clad is a Clang plugin enabling automatic differentiation (AD) for C++ mathematical
+        functions by modifying the abstract syntax tree using LLVM's compiler capabilities.
+        It integrates into existing codebases without modifications and supports forward and
+        reverse mode differentiation. Reverse mode is efficient for Machine Learning and 
+        inverse problems involving backpropagation.
+        Reverse mode AD requires two passes: forward pass stores intermediate values, reverse
+        pass computes derivatives. Currently, Clad only supports storing trivially copyable 
+        types for function call arguments, limiting support for C-style arrays and non-copyable
+        types like unique pointers, constraining Object-Oriented Programming usage.
+        The project aims to enhance Clad's capability to store intermediate values for non-copyable
+        types. One of the challenges lies in determining which expressions are modified in nested 
+        functions, potentially requiring run-time memory location tracking, which can be inefficient.
+        The solution involves enhancing To-Be-Recorded (TBR) analysis, currently limited with
+        poor nested function call support and no pointer reassignment handling. Improved TBR 
+        analysis will enable predictable memory handling, generating optimal code, and 
+        supporting both non-copyable types and efficient storage of copyable structures.
+      proposal: /assets/docs/Petro_Zarytskyi_Proposal_2025.pdf
+      mentors: Vassil Vassilev, David Lange
+    - title: "Optimizing reverse-mode automatic differentiation with advanced activity-analysis"
+      status: Completed project
       description: |
         Clad is an automatic differentiation clang plugin for C++. It automatically
         generates code that computes derivatives of functions given by the user.
@@ -323,7 +344,7 @@
       proposal: /assets/docs/petro_mozil_promosal_GSoC_2025.pdf
       mentors: Martin Vassilev, David Lange
 
 - name: Galin Bistrev
   photo: Bistrev.jpg
   info: "CERN Summer student 2025"
   email: [email protected]
@@ -341,7 +362,7 @@
         started using automatic differentiation (AD) technology to aid
         minimization algorithms.  Computationally cheap gradients produced by AD
         can improve performance of the larger workflows in Combine by more than
         5x as demonstrated at ICHEP.  The way how RooFit implements AD also
         benefits the shareability of likelihoods.  To get the likelihood
         gradient, RooFit transforms the likelihood from its internal
         representation to simple standalone C++ code, which is then used to