List of Papers for Presentation

  1. C. Barnaby, Q. Chen, R. Ramalingam, O. Bastani, and I. Dillig. Active Learning for Neurosymbolic Program Synthesis. Proceedings of the ACM on Programming Languages (PACMPL), Volume 9, Issue OOPSLA2. 2025.
    [DOI]

  2. A. Blasi, A. Goffi, K. Kuznetsov, A. Gorla, M. D. Ernst, M. Pezzè, and S. D. Castellanos.
    Translating Code Comments to Procedure Specifications.
    Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), 2018.
    [DOI]

  3. B. Caulfield, M. N. Rabe, S. A. Seshia, and S. Tripakis.
    What’s Decidable about Syntax-Guided Synthesis?
    arXiv preprint arXiv:1510.08393, 2016.
    [arXiv]

  4. I. Drosos, T. Barik, P. J. Guo, R. DeLine, and S. Gulwani.
    Wrex: A Unified Programming-by-Example Interaction for Synthesizing Readable Code for Data Scientists.
    Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI), 2020.
    [DOI]

  5. K. Ellis, C. Wong, M. Nye, M. Sablé-Meyer, L. Morales, L. Hewitt, L. Cary, and A. Solar-Lezama.
    DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning.
    Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), 2021.
    [DOI]

  6. M. Endres, S. Fakhoury, S. Chakraborty, and S. K. Lahiri.
    Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?
    Proceedings of the ACM on Software Engineering, Volume 1, Issue FSE. 2024.
    [DOI]

  7. S. Jha, S. Gulwani, S. A. Seshia, and A. Tiwari.
    Oracle-Guided Component-Based Program Synthesis.
    Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering (ICSE), 2010.
    [DOI]

  8. S. Jha and S. A. Seshia.
    A Theory of Formal Synthesis via Inductive Learning.
    Acta Informatica, 54(7), 2017.
    [DOI]

  9. R. Ji, Y. Zhao, N. Polikarpova, Y. Xiong, and Z. Hu.
    Superfusion: Eliminating Intermediate Data Structures via Inductive Synthesis.
    Proceedings of the ACM on Programming Languages (PACMPL), Volume 8, Issue PLDI. 2024.
    [DOI]

  10. D. Lee, W. Lee, and K. Yi. Inductive Program Synthesis by Meta-Analysis-Guided Hole Filling. Proceedings of the ACM on Programming Languages (PACMPL), Volume 10, Issue POPL. 2026.
    [DOI]

  11. Z. Li, J. Huang, and M. Naik.
    Scallop: A Language for Neurosymbolic Programming.
    Proceedings of the ACM on Programming Languages (PACMPL), Volume 7, Issue PLDI. 2023.
    [DOI]

  12. S. Lu and R. Bodík. HieraSynth: A Parallel Framework for Complete Super-Optimization with Hierarchical Space Decomposition. Proceedings of the ACM on Programming Languages (PACMPL), Volume 9, Issue OOPSLA2. 2025.
    [DOI]

  13. R. Meyer and J. Tepe. Oriented Metrics for Bottom-Up Enumerative Synthesis. Proceedings of the ACM on Programming Languages (PACMPL), Volume 10, Issue POPL. 2026.
    [DOI]

  14. M. R. H. Misu, C. V. Lopes, I. Ma, and J. Noble.
    Towards AI-Assisted Synthesis of Verified Dafny Methods.
    Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE), 2024.
    [DOI]

  15. S. Pailoor, Y. Wang, and I. Dillig.
    Semantic Code Refactoring for Abstract Data Types.
    Proceedings of the ACM on Programming Languages (PACMPL), Volume 8, Issue POPL. 2024.
    [DOI]

  16. R. Singh, R. Singh, Z. Xu, R. Krosnick, and A. Solar-Lezama.
    Modular Synthesis of Sketches Using Models.
    In Verification, Model Checking, and Abstract Interpretation (VMCAI), Lecture Notes in Computer Science, vol. 8318, 2014.
    [DOI]

  17. S. Srivastava, S. Gulwani, and S. Foster.
    From Program Verification to Program Synthesis.
    Proceedings of the ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL), 2010.
    [DOI]

  18. E. Torlak and R. Bodík.
    A Lightweight Symbolic Virtual Machine for Solver-Aided Host Languages.
    Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2014.
    [DOI]

  19. A. Verma, V. Murali, R. Singh, P. Kohli, and S. Chaudhuri.
    Programmatically Interpretable Reinforcement Learning.
    Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
    [URL]

  20. A. Thakur, G. Tsoukalas, Y. Wen, J. Xin, and S. Chaudhuri.
    An In-Context Learning Agent for Formal Theorem-Proving.
    Proceedings of the 2025 Conference on Language Modeling (COLM), 2024. [URL]

  21. H. Xin, Z. Z. Ren, J. Song, Z. Shao, W. Zhao, H. Wang, B. Liu, L. Zhang, X. Lu, Q. Du, W. Gao, H. Zhang, Q. Zhu, D. Yang, Z. Gou, Z. F. Wu, F. Luo, and C. Ruan.
    DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search.
    Proceedings of the 13th International Conference on Learning Representations (ICLR), 2025. [URL]

  22. Z. Ye, R. Ji, Y. Xiong, and X. Zhang. Accelerating Syntax-Guided Program Synthesis by Optimizing Domain-Specific Languages. Proceedings of the ACM on Programming Languages (PACMPL), Volume 10, Issue POPL. 2026.
    [DOI]