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@@ -174,6 +174,29 @@ <h2>Upcoming Seminars<h2>
 
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+							<img src="img/aashish_yadavally.jpg"> 
+							<h5> Aashish Yadavally </h5>
+							<p> UT Dallas </p>
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+						<div class="event-info">
+							<h3>Contextuality of Code Representation Learning</h3>
+								<p> Advanced machine learning models (ML) have been successfully leveraged in several software engineering (SE) applications. The existing SE techniques have used the embedding models ranging from static 
+								to contextualized ones to build the vectors for program units. The contextualized vectors address a phenomenon in natural language texts called polysemy, which is the coexistence of different meanings of 
+								a word/phrase. However, due to different nature, program units exhibit the nature of mixed polysemy. Some code tokens and statements exhibit polysemy while other tokens (e.g., keywords, separators, and 
+								operators) and statements maintain the same meaning in different contexts. A natural question is whether static or contextualized embeddings fit better with the nature of mixed polysemy in source code. 
+								The answer to this question is helpful for the SE researchers in selecting the right embedding model. We conducted experiments on 12 popular sequence-/tree-/graph-based embedding models and on the samples 
+								of a dataset of 10,222 Java projects with +14M methods. We present several contextuality evaluation metrics adapted from natural-language texts to code structures to evaluate the embeddings from those models. 
+								Among several findings, we found that the models with higher contextuality help a bug detection model perform better than the static ones. Neither static nor contextualized embedding models fit well with the 
+								mixed polysemy nature of source code. Thus, we develop Hycode, a hybrid embedding model that fits better with the nature of mixed polysemy in source code. </p>
+
+							<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"><strong>Monday, January 15, 2024 at 3:00 PM CET</strong></span></p>
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