Publications

Select a paper and use the Abstract or BibTeX buttons to reveal details.

• 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.

  NeurIPS 2025 • May 2025

  HTML arXiv Code Abstract BibTeX

  This paper addresses the epistemological gap between probabilistic LLMs and deterministic formal verification by introducing a PCFG framework to model LLM outputs. A systematic evaluation of five frontier LLMs reveals domain-specific impacts of SMT-based autoformalization and demonstrates that task-dependent uncertainty signals enable selective verification with drastically reduced errors.

  @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

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

  *Equal contribution

  HiPC 2025 • July 2025

  HTML arXiv Code Abstract BibTeX

  K⁴ introduces an unsupervised typicality learning paradigm for online log anomaly detection. By modeling normal operational patterns rather than enumerating anomalies, it detects novel, previously unseen failures with low computational overhead suitable for production streaming environments.

  @misc{chen2025k4onlineloganomaly,
    title={$K^4$: Online Log Anomaly Detection Via Unsupervised Typicality Learning},
    author={Vikash Singh and Weicong Chen 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}
  }

• Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM

  Zhang, B., Zheng, M., Ganguly, D., Zhang, X., Singh, V., Chaudhary, V., Zhang, Z.

  HiPC 2025 • September 2025

  HTML arXiv Abstract BibTeX

  This work decomposes distributed LLM training into granular operations to build an accurate, efficient GPU performance model. The framework achieves prediction error under 10% while informing optimal parallelization and hardware configuration strategies for large-scale training.

  @article{zhang2025efficient,
    title={Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM},
    author={Zhang, Biyao and Zheng, Mingkai and Ganguly, Debargha and Zhang, Xuecen and Singh, Vikash and Chaudhary, Vipin and Zhang, Zhao},
    journal={arXiv preprint arXiv:2509.22832},
    year={2025}
  }

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.