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@@ -106,7 +106,7 @@ <h5><a href="https://homes.cs.washington.edu/~vsiyer/">Vikram Iyer</a> <br>(Univ
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             <h3>Invited Talk </h3>
-            <h4>AI-Driven RAN Intelligence: From Theory to Practice</h4>
+            <h4>Towards Data Driven and AI Powered Open RAN </h4>
             <figure style="float: left; margin: 10px; text-align: center;" >  
                     <img src="assets/images/mahesh.jpg" class="img-responsive" align="center" width="150px">
                     <figcaption style="text-align: center"><b><a href="https://homepages.inf.ed.ac.uk/mmarina/">Mahesh Marina </a><br> <font size="2">The University of Edinburgh</font></b></figcaption>
@@ -114,9 +114,9 @@ <h4>AI-Driven RAN Intelligence: From Theory to Practice</h4>
             
 <!--                             <img src="assets/images/vikram.jpg" class="img-responsive" align="left" width="150px">
             <h5><a href="https://homes.cs.washington.edu/~vsiyer/">Vikram Iyer</a> <br>(University of Washington - Seattle)<br></h5> -->
-            <font size="2"><b>Abstract:</b> TBD</font><br>
-            <font size="2"><b>Bio:</b> Mahesh Marina is a Professor in the School of Informatics at the University of Edinburgh and a Turing Fellow at the Alan Turing Institute in London. Before joining Edinburgh, he had a two-year postdoctoral stint at the UCLA Computer Science Department. He earned his PhD in Computer Science in 2004 from the State University of New York at Stony Brook. He has previously held visiting researcher positions at ETH Zurich and Ofcom London. He is a Distinguished Member of the ACM and a Senior Member of the IEEE.
-</font><br>
+            <font size="2"><b>Abstract:</b> The architecture and deployment of mobile networks, including the Radio Access Network (RAN), has undergone a dramatic transformation over the past decade, driven by the adoption of “disaggregation” at multiple levels. This shift has fostered a more diverse and open telecom ecosystem. Today, we are entering a new era for the RAN, where another major opportunity awaits: the transition to data-driven, AI-powered RAN operations with even greater disaggregation and openness. However, this evolution also brings new challenges, much like the earlier phase of disaggregation. In this talk, I will focus on two key challenges — the efficient acquisition of RAN data and troubleshooting in a disaggregated RAN — and outline our GenAI-based solutions to address them.</font><br>
+            <font size="2"><b>Bio:</b> Mahesh Marina is a Professor in the School of Informatics at the University of Edinburgh, where he leads the Networked Systems Research Group, and a Visiting Professor in the Department of Computer Science at Johns Hopkins University. Previously, he was a Turing Fellow at the Alan Turing Institute (the UK’s national institute for data science and AI) for five years (2018-23) and has served as the Director of the Institute for Computing Systems Architecture within Informatics@Edinburgh for four years (2018-22). Earlier, he has held visiting roles at ETH Zurich and at Ofcom (the UK’s telecoms regulator). Prior to joining Edinburgh, he had a two-year postdoctoral stint at the UCLA Computer Science Department after earning his PhD in Computer Science from the State University of New York at Stony Brook. He is an ACM Distinguished Member and an IEEE Senior Member.
+            </font><br>
                 
             
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