Veeraraju ElluruI am a Computer Science Senior at the Indian Institute of Technology, Jodhpur. I love AI research and Math. My main areas of interest are Generative Modeling for Computer Vision, Scene Understanding, and Multimodal Mechanistic Interpretability. When it comes to research, my primary agenda is to achieve "multimodal privacy". I am extremely grateful to be jointly mentored by Dr. Shivang Agarwal and Dr. Mayank Vatsa at the Image Analytics and Biometrics (IAB) Lab, IIT Jodhpur. Throughout my undergraduate studies, I've actively worked on side-quests where AI research can help significantly contribute. This has given me the privilege to explore projects at some really cool places:
I'm a strong believer of Bruce Lee's 10000-hour to mastery rule; and am forever grateful to all my mentors who have shaped my research. |
"Whereof one cannot speak, thereof one must be silent." ~ Ludwig Wittgenstein |
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Lie Algebra-Based Semantic Flow for Incompleteness Detection in SummarizationManikandan Ravikiran, Veeraraju Elluru, Karrtik Iyer, Prasanna Pendse, Shayan Mohanty Springer / Blog Evaluating the incompleteness of machine-generated summaries remains a critical challenge in natural language generation. In domains such as healthcare, education, and policy, summaries that omit or distort key content can appear fluent yet undermine reliability and trust. We introduce a Lie Algebra-based Semantic Flow framework that treats summarization as a geometric transformation in embedding space, where incompleteness manifests as segments with low semantic flow magnitudes. Unlike prior metrics that collapse evaluation into a single similarity score, our approach provides interpretable, segment-level signals through a dynamic mean-scaled thresholding mechanism, requiring no supervision. We evaluate our method on UniSumEval and SIGHT benchmarks, showing average improvements of +10.6 macro-F1 over existing methods. Theoretical analysis establishes rotation invariance, bounded flow magnitudes, and a severity-coverage Pareto frontier that explains observed trade-offs. Qualitative case studies further demonstrate that our framework identifies variety of incompleteness often missed by existing methods. |
Bias-Aware Machine Unlearning: Towards Fairer Vision Models via Controllable ForgettingSai Siddhartha Chary Aylapuram, Veeraraju Elluru, Shivang Agarwal arXiv / slides Deep neural networks often rely on spurious correlations in training data, resulting in biased or unfair predictions, particularly in safety-critical applications. While conventional bias mitigation methods typically require retraining from scratch or redesigning the data pipeline, recent advances in machine unlearning offer a promising alternative for post-hoc model correction. In this work, we explore the trifecta of efficiency, fairness, and model utility post unlearning via Bias-Aware Machine Unlearning, a paradigm that selectively forgets biased samples or feature representations to address various forms of bias in vision models. |
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