Rct225 patch 1

_fellows/2025/KyryloFilonenko.md

@@ -5,31 +5,32 @@ shortname: KyryloFilonenko
 permalink: "/fellows/KyryloFilonenko.html"
 fellow-name: "Kyrylo Filonenko"
 title: "Kyrylo Filonenko - IRIS-HEP Fellow"
-active: False
+active: True
 dates:
   start: 2025-07-01
   end: 2025-09-23
+  start: 2026-07-06
+  end: 2026-09-28
 photo: "/assets/images/team/fellows-2025/Kyrylo-Filonenko.jpg"
 institution: "Taras Shevchenko National University of Kyiv"
 e-mail: "ababagalamagat1@gmail.com"
 focus-area:
 challenge-area:
-project_title: "Tagging low momentum taus in CMS"
-project_goal: >
-    The goal of this project is to develop a dedicated tau reconstruction (tagging) algo-rithm for the CMS Run 3 dataset. This will be based on and extend the low-momentum 3-prong tau tagger developed for CMS Run 2, utilizing the ABCNet model — a graph neural network enhanced with attention mechanisms for improved performance.
-mentors:
-  - Valeriia Lukashenko
-proposal: "/assets/pdf/fellows-2025/UKR014-proposal-Kyrylo-Filonenko.pdf"
+projects:
+  - project_title: "Tagging low momentum taus in CMS"
+    project_goal: >
+        The goal of this project is to develop a dedicated tau reconstruction (tagging) algo-rithm for the CMS Run 3 dataset. This will be based on and extend the low-momentum 3-prong tau tagger developed for CMS Run 2, utilizing the ABCNet model — a graph neural network enhanced with attention mechanisms for improved performance.
+    mentors:
+      - Valeriia Lukashenko
+    proposal: "/assets/pdf/fellows-2025/UKR014-proposal-Kyrylo-Filonenko.pdf"
+  - project_title: "Snakemake workflow engine for HEP analyses"
+    project_goal: >
+      Port existing enginge-based analysis workflows to Snakemake, compare the resulting user experience between the engines, and validate distributed execution through Snakemake’s Slurm and HTCondor executor plugins.
+    mentors:
+      - Clemens Lange
+    proposal: "/assets/pdf/fellows-2026/UKR007-proposal-Kyrylo-Filonenko.pdf"
 presentations:
-  - title:
-    date:
-    url:
-    meeting:
-    meetingurl:
-    recordingurl:
-    focus-area:
-current_status: >
-  A placeholder for status updates
+current_status:
 github-username: "KyryloFilonenko"
 linkedin-profile:
 funding-source: impress-u

_fellows/2025/knottedtree123.md

@@ -5,31 +5,32 @@ shortname: knottedtree123
 permalink: /fellows/knottedtree123.html
 fellow-name: Cody Tanner
 title: Cody Tanner - IRIS-HEP Fellow
-active: False
+active: True
 dates:
   start: 2025-07-01
   end: 2025-09-26
+  start: 2026-06-15
+  end: 2026-09-07
 photo: /assets/images/team/fellows-2025/Cody-Tanner.jpg
 institution: University of Washington
 e-mail: cjt05@uw.edu
 focus-area: ia
 challenge-area:
-project_title: Differentiable Modeling of Systematic Uncertainties in ATLAS Object Corrections
-project_goal: >
-  Modern ATLAS analyses depend on object corrections that are currently implemented through non-differentiable procedures like histogram lookups and conditional logic, limiting their integration into gradient-based pipelines. This project proposes a neural network model that replicates ATLAS object corrections, including systematic uncertainties, for small-R jets in a differentiable and computationally efficient form. Starting from an existing baseline trained on the JZ2 dataset, the model will be refined through architectural tuning, loss reweighting, and incorporation of per-object uncertainties to approach sub-percent residuals in jet kinematics. A final case study will use the model to reconstruct Z→jj peaks, evaluating the physics impact of improved corrections and uncertainty modeling. This work provides a foundation for embedding fast, uncertainty-aware corrections directly into end-to-end ATLAS workflows.
-mentors:
-  - Gordon Watts (University of Washington)
-proposal: /assets/pdf/fellows-2025/USA035-proposal-Cody-Tanner.pdf
+projects:
+  - project_title: Differentiable Modeling of Systematic Uncertainties in ATLAS Object Corrections (2025)
+    project_goal: >
+      Modern ATLAS analyses depend on object corrections that are currently implemented through non-differentiable procedures like histogram lookups and conditional logic, limiting their integration into gradient-based pipelines. This project proposes a neural network model that replicates ATLAS object corrections, including systematic uncertainties, for small-R jets in a differentiable and computationally efficient form. Starting from an existing baseline trained on the JZ2 dataset, the model will be refined through architectural tuning, loss reweighting, and incorporation of per-object uncertainties to approach sub-percent residuals in jet kinematics. A final case study will use the model to reconstruct Z→jj peaks, evaluating the physics impact of improved corrections and uncertainty modeling. This work provides a foundation for embedding fast, uncertainty-aware corrections directly into end-to-end ATLAS workflows.
+    mentors:
+      - Gordon Watts (University of Washington)
+    proposal: /assets/pdf/fellows-2025/USA035-proposal-Cody-Tanner.pdf
+  - project_title: Differentiable Modeling of Systematic Uncertainties in ATLAS Object Corrections (2026)
+    project_goal: >
+      "Modern ATLAS analyses of rare processes such as Z → qq̄ depend on both precise calibration of reconstructed objects and machine-learning classifiers, but the two are typically decoupled by non-differentiable selection steps that break gradient flow. This project proposes a fully differentiable analysis pipeline connecting a systematically calibrated jet-correction network to a downstream signal-background classifier. The central technical contribution is replacing the discrete arg max jet-pair selection with a smooth alternative, such as soft attention or a Gumbel-softmax relaxation, restoring end-to-end differentiability across the full chain. With gradient flow restored, the classification stage will be trained on frozen, physics-validated calibrated inputs, and the resulting pipeline will be evaluated under variations of the leading systematic uncertainties to quantify the robustness gained relative to an uncalibrated baseline. A final study will report performance in terms of signal significance and a cross-section measurement, with results documented in a public code release and a draft publication. This work demonstrates a path toward embedding calibration-aware, systematics-robust machine learning directly into end-to-end ATLAS analysis workflows."
+    mentors:
+      - Gordon Watts (University of Washington)
+    proposal: /assets/pdf/fellows-2026/USA003-proposal-Cody-Tanner.pdf
 presentations:
-  - title: ""
-    date: ""
-    url: ""
-    meeting: ""
-    meetingurl: ""
-    recordingurl: ""
-    focus-area: ia
-current_status: >
-  A placeholder for status updates
+current_status:
 github-username: knottedtree123
 linkedin-profile: https://www.linkedin.com/in/cody-tanner-12940421b
 ---

_fellows/2026/pauliusB20.md

@@ -4,7 +4,7 @@ pagetype: fellow
 shortname: pbalciunas
 fellow-name: Paulius Balčiūnas
 title: Paulius Balčiūnas - IRIS-HEP Fellow
-active: True
+active: False
 dates:
   start: 2026-02-02
   end: 2026-04-18