diff --git a/Gemfile.lock b/Gemfile.lock
index 9de18f0..5cd61fb 100644
--- a/Gemfile.lock
+++ b/Gemfile.lock
@@ -77,6 +77,7 @@ GEM
       rb-fsevent (~> 0.10, >= 0.10.3)
       rb-inotify (~> 0.9, >= 0.9.10)
     mercenary (0.4.0)
+    mini_portile2 (2.8.9)
     minima (2.5.1)
       jekyll (>= 3.5, < 5.0)
       jekyll-feed (~> 0.9)
diff --git a/pages/postdocs/postdoc-matteomarchegiani.md b/pages/postdocs/postdoc-matteomarchegiani.md
index 714ab1b..ff62c55 100644
--- a/pages/postdocs/postdoc-matteomarchegiani.md
+++ b/pages/postdocs/postdoc-matteomarchegiani.md
@@ -20,13 +20,60 @@ project_goal: >
 proposal: /assets/pdfs/Matteo-Marchegiani_proposal_2025.pdf
 
 presentations:
+  - title: Maskformers for offline reconstruction
+    date: February 10, 2026
+    url: https://indico.cern.ch/event/1649883/contributions/6935005/attachments/3217627/5732543/26.02.10_ML4reco_maskformers_offline.pdf
+    meeting: ML4RECO
+    meetingurl: https://indico.cern.ch/event/1649883/
+  - title: Studies on time resolution in GNN-based reco
+    date: December 3, 2025
+    url: https://indico.cern.ch/event/1617166/contributions/6819529/attachments/3185911/5668415/hgcal-dpg_gnn-time-resolution.pdf
+    meeting: HGCAL DPG
+    meetingurl: https://indico.cern.ch/event/1617166/
+  - title: Studies on time resolution with the latest HGCAL GNN model
+    date: December 2, 2025
+    url: https://indico.cern.ch/event/1615761/contributions/6818543/attachments/3184951/5666518/ml4reco_gnn-time-resolution-update.pdf
+    meeting: ML4RECO
+    meetingurl: https://indico.cern.ch/event/1615761/
+  - title: "Update on performance of the latest HGCAL GNN model"
+    date: "November 18, 2025"
+    url: https://indico.cern.ch/event/1612036/contributions/6792820/attachments/3176426/5649043/ml4reco_gnn-based-particle-reconstruction_update.pdf
+    meeting: ML4RECO
+    meetingurl: https://indico.cern.ch/event/1612036/
   - title: "ML4RECO: GNN and Transformer Based HGCAL Reconstruction"
     date: "October 13, 2025"
     url: https://indico.cern.ch/event/1593611/contributions/6737584/attachments/3153545/5600652/25.10.13_MLG_TownHall_HGCAL_reco.pdf
     meeting: CMS Machine Learning Town Hall
     meetingurl: https://indico.cern.ch/event/1593611/contributions/6737584/
+  - title: Single-particle energy resolution with latest GNN model
+    date: "September 17, 2025"
+    url: https://indico.cern.ch/event/1587360/contributions/6689475/attachments/3137672/5567817/ml4reco-gnn-new-training-physics-metrics-update.pdf
+    meeting: ML4RECO
+    meetingurl: https://indico.cern.ch/event/1587360/
+  - title: Updated GNN training after bugfixes in CMSSW 15_0_X
+    date: "August 27, 2025"
+    url: https://indico.cern.ch/event/1579980/contributions/6658969/attachments/3124101/5540514/ml4reco-gnn-finecalo-simtrack-error-fix-training-update.pdf
+    meeting: ML4RECO
+    meetingurl: https://indico.cern.ch/event/1579980/
+  - title: FineCalo SimTrack Reconstruction Error
+    date: "July 16, 2025"
+    url: https://indico.cern.ch/event/1569687/contributions/6612371/attachments/3105373/5503441/ml4reco-gnn-finecalo-simtrack-error.pdf
+    meeting: ML4RECO
+    meetingurl: https://indico.cern.ch/event/1569687/
 
 current_status: >
+    <br>
+    <b>2025 Q4 </b>
+    <br>
+
+    *   Progress
+        *   Study energy, position and time resolution of the GNN clustering using simulated photons, pions and tau leptons
+        *   Study issue in time resolution in CMSSW_15_X: change in the timing simulation with respect to CMSSW_11_X
+        *   Finalize publication on performance metrics of GNN clustering with 0 pileup simulation
+        *   Study the impact of increased pileup on the GNN training
+        *   Memory profiling of the training with a dataset including ~250k reconstructed hits
+        *   Working on pileup simulation: generate hard process alongside minimum bias events
+
     <br>
     <b>2025 Q3 </b>
     <br>
@@ -35,5 +82,7 @@ current_status: >
         *   Learned how to train the GNN employing GravConv layers in combination with the object condensation loss for the reconstruction of energy clusters in the HGCAL
         *   Studied the performance and energy resolution of the GNN-based reconstruction in zero-pileup environments, considering simulated photons, pions and tau leptons
         *   Ported the training datasets production to CMSSW_15_1_X, making use of the FineCalo simulation
+        *   Ported the GNN training to Pytorch 2.6.0 and CUDA 12.4
+        *   Study memory profiling of the model on GPU
 
 ---