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@@ -198,6 +198,30 @@ <h3>Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detecti
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+							<img src="img/ZhongLi_nanjing_university.jpg">
+							<h5> Zhong Li </h5>
+							<p> Nanjing University </p>
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+							<h3>Robust Learning of Deep Predictive Models from Noisy and Imbalanced Software Engineering Datasets</h3>
+								<p> With the rapid development of Deep Learning, deep predictive models have been widely applied to improve Software Engineering tasks, such as defect prediction and issue 
+								classification, and have achieved remarkable success. They are mostly trained in a supervised manner, which heavily relies on high-quality datasets. Unfortunately, due to 
+								the nature and source of software engineering data, the real-world datasets often suffer from the issues of sample mislabelling and class imbalance, thus undermining the 
+								effectiveness of deep predictive models in practice. This problem has become a major obstacle for deep learning-based Software Engineering. In this paper, we propose 
+								RobustTrainer, the first approach to learning deep predictive models on raw training datasets where the mislabelled samples and the imbalanced classes coexist. 
+								RobustTrainer consists of a two-stage training scheme, where the first learns feature representations robust to sample mislabelling and the second builds a classifier robust 
+								to class imbalance based on the learned representations in the first stage. We apply RobustTrainer to two popular Software Engineering tasks, i.e., Bug Report Classification 
+								and Software Defect Prediction. Evaluation results show that RobustTrainer effectively tackles the mislabelling and class imbalance issues and produces significantly better 
+								deep predictive models compared to the other six comparison approaches. </p>
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+							<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"><strong>Monday, December 18, 2023 at 10:30 PM CET</strong></span></p>
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