Publications

@misc{ganguly2025grammarsformaluncertaintytrust,
      title={Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks}, 
      author={Debargha Ganguly and Vikash Singh and Sreehari Sankar and Biyao Zhang and Xuecen Zhang and Srinivasan Iyengar and Xiaotian Han and Amit Sharma and Shivkumar Kalyanaraman and Vipin Chaudhary},
      year={2025},
      eprint={2505.20047},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20047}, 
}
                    

Research Interests

• Formal Reasoning and Verification: Developing rigorous formal logic methodologies, leveraging SMT‐LIB encodings and solver frameworks to verify, interpret, and enhance the correctness of LLM-generated reasoning.
• Fine-Tuning LLMs and Vision Models: Leveraging techniques such as LoRA to optimize large language and vision models for specific tasks while maintaining computational efficiency.
• Redundancy Mitigation in LLMs: Investigating approaches to reduce redundancy in large language models, enhancing performance and efficiency.
• Model Optimization: Developing strategies for optimizing machine learning models, including pruning and hyperparameter tuning, to improve both accuracy and resource utilization.
• Explainable AI: Advancing interpretability in AI models, focusing on enhancing transparency and providing actionable insights for users.