updated pubs. preview

Author: ab-sayed

_bibliography/papers.bib

@@ -83,7 +83,9 @@ @inproceedings{sayed2020experimental
   year={2020},
   organization={Springer},
   url={https://link.springer.com/chapter/10.1007/978-3-030-44289-7_3},
+  pdf={https://link.springer.com/chapter/10.1007/978-3-030-44289-7_3},
   abbr={AI & Robotics},
+  preview={AICV2020_Hexa.gif},
   selected={false}
 }
 
@@ -97,7 +99,9 @@ @inproceedings{sayed2020deep
   year={2020},
   organization={Springer},
   url={https://link.springer.com/chapter/10.1007/978-3-030-44289-7_29},
+  pdf={https://link.springer.com/chapter/10.1007/978-3-030-44289-7_29},
   abbr={AI & Robotics},
+  preview={AICV2020_3RRR.gif},
   selected={false}
 }
 
@@ -112,7 +116,9 @@ @inproceedings{azar2019neuro
   year={2019},
   organization={IEEE},
   url={https://ieeexplore.ieee.org/abstract/document/8909333},
+  pdf={https://ieeexplore.ieee.org/abstract/document/8909333},
   abbr={AI & Robotics},
+  preview={Niles2019.gif},
   selected={false}
 }
 
@@ -126,6 +132,8 @@ @inproceedings{azar2019pid
   year={2019},
   organization={Springer},
   url={https://link.springer.com/chapter/10.1007/978-3-030-31129-2_22},
+  pdf={https://link.springer.com/chapter/10.1007/978-3-030-31129-2_22},
   abbr={Control & Robotics},
+  preview={AISI2019.gif},
   selected={false}
 }

_news/announcement_UGE_Internship_2024.md

@@ -0,0 +1,20 @@
+---
+layout: post
+title: Master’s thesis opportunity on "Connecting Discrete and Continuous Neural Network Models"
+date: 2024-11-11 15:53:00-0400
+inline: false
+related_posts: false
+---
+
+📢 Come work with me and Pierre-Jean Meyer! 📢
+
+---
+<a href="https://estas.univ-gustave-eiffel.fr/english/estas">ESTAS</a> Lab at Université Gustave Eiffel in Lille is offering an exciting Master’s thesis opportunity on the topic of "Connecting Discrete and Continuous Neural Network Models."
+
+🔍 What’s it all about?
+
+We’re working to establish formal relationships between the behavior of discrete neural networks and their continuous counterparts, neural ordinary differential equations (neural ODEs). These relationships could then be used to guarantee the safety of one model after verifying the other, ultimately enabling safer and more reliable AI systems.
+
+💡 Interested?
+
+Check out all the details, including the desired profile and application process, 👉 <a href="https://gdr-macs.fr/sites/default/files/2024-11/241112_Master_thesis_neural_ODE.pdf">here</a>