Publications

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

Ganguly, D., Singh, V., Sankar, S., Zhang, B., Zhang, X., Iyengar, S., Han, X., Sharma, A., Kalyanaraman, S., Chaudhary, V.

arXiv preprint, May 2025.

arXiv Code BibTeX


@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}, 
}

K⁴: Online Log Anomaly Detection Via Unsupervised Typicality Learning

Chen, W., Singh, V., Rahmani, Z., Ganguly, D., Hariri, M., Chaudhary, V.

arXiv preprint, July 2025.

arXiv Code BibTeX


@misc{chen2025k4onlineloganomaly,
      title={$K^4$: Online Log Anomaly Detection Via Unsupervised Typicality Learning}, 
      author={Weicong Chen and Vikash Singh and Zahra Rahmani and Debargha Ganguly and Mohsen Hariri and Vipin Chaudhary},
      year={2025},
      eprint={2507.20051},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2507.20051}, 
}

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.