Venkata Krishnaveni Chennuru received her Ph.D. in Machine Learning from the University of Hyderabad. She is currently working as a Lecturer in Computer Science, with research interests centered on imbalanced data learning, machine learning algorithms, and applied artificial intelligence.

Her research focuses on the development of robust and scalable methods for handling class imbalance, including adaptive relabeling techniques and self-supervised augmentation frameworks. She is particularly interested in designing unified approaches that integrate data-level and algorithm-level strategies to enhance classification performance on skewed datasets.

Her work also involves the systematic evaluation of models using metrics such as macro-F1 score, balanced accuracy, and AUC-ROC, along with the analysis of dataset characteristics that influence algorithm behavior and generalization. Her research has applications in areas such as educational data analytics, anomaly detection, and real-world predictive modeling.

In addition to her research contributions, she is actively engaged in developing reproducible workflows and open-access resources to support the broader dissemination of machine learning methodologies within the academic community.