publications | Zheng Guo

2026

JCP
Hierarchical search of tree tensor networks for high-dimensional data

Zheng Guo, Aditya Deshpande, Xinyu Wang, Brian C. Kiedrowski, and Alex A. Gorodetsky

Journal of Computational Physics, 2026

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Tensor network methods provide a scalable solution to represent high-dimensional data. However, their efficacy is often limited by static, expert-defined structures that fail to adapt to evolving data correlations. We address this limitation by formalizing the structural rounding problem for tree tensor networks and introducing a hierarchical search algorithm HiST, which automatically identifies optimized structures with index reshaping for input tree tensor networks. To navigate the combinatorial explosion of the structural search space, HiST integrates stochastic sub-network sampling with hierarchical refinement. This approach utilizes entropy-guided index clustering to reduce dimensionality and targeted reshaping to expose latent data correlations. Numerical experiments on analytical functions and real-world physics applications, including thermal radiation transport, neutron diffusion, and computational fluid dynamics, demonstrate that HiST exhibits empirical polynomial scaling with dimensionality relative to the sampling budget, bypassing the scalability barriers in prior work. HiST achieves compression ratios 2.5x to 100x higher than standard fixed formats such as Tensor Trains and Hierarchical Tuckers (peaking at 1000x). Furthermore, HiST discovers structures that generalize effectively: applying a structure optimized for one data instance to a related target data typically maintains compression performance within 10% of the result obtained by performing structure search on that target data. These results highlight HiST as a robust, automated tool for adaptive data representation and high-dimensional simulation compression with tensor network methods.
@article{htnss, doi = {10.1016/j.jcp.2026.115230}, author = {Guo, Zheng and Deshpande, Aditya and Wang, Xinyu and Kiedrowski, Brian C. and Gorodetsky, Alex A.}, title = {Hierarchical search of tree tensor networks for high-dimensional data}, journal = {Journal of Computational Physics}, volume = {566}, pages = {115230}, year = {2026}, issn = {0021-9991}, keywords = {Tensor network structure search, Tensor networks, Low-rank tensor approximation, Adaptive data compression, Simulation compression} }
PLDI
Presynthesis: Towards Scaling Up Program Synthesis with Finer-Grained Abstract Semantics

Rui Dong, Qingyue Wu, Danny Ding, Zheng Guo, Ruyi Ji, and Xinyu Wang

Proc. ACM Program. Lang., 2026

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Abstract semantics has proven to be instrumental for accelerating search-based program synthesis, by enabling the sound pruning of a set of incorrect programs (without enumerating them). One may expect faster synthesis with increasingly finer-grained abstract semantics. Unfortunately, to the best of our knowledge, this is not the case, yet. The reason is because, as abstraction granularity increases – while fewer programs are enumerated – pruning becomes more costly. This imposes a fundamental limit on the overall synthesis performance, which we aim to address in this work. Our key idea is to introduce an offline presynthesis phase, which consists of two steps. Given a DSL with abstract semantics, the first semantics modeling step constructs a tree automaton A for a space of inputs – such that, for any program P and for any considered input I, A has a run that corresponds to P’s execution on I under abstract semantics. Then, the second step builds an oracle O for A. This O enables fast pruning during synthesis, by allowing us to efficiently find exactly those DSL programs that satisfy a given input-output example under abstract semantics. We have implemented this presynthesis-based synthesis paradigm in a framework, Foresighter. On top of it, we have developed three instantiations for SQL, string transformation, and matrix manipulation. All of them significantly outperform prior work in the respective domains.
@article{presynthesis, doi = {10.1145/3808288}, author = {Dong, Rui and Wu, Qingyue and Ding, Danny and Guo, Zheng and Ji, Ruyi and Wang, Xinyu}, title = {Presynthesis: Towards Scaling Up Program Synthesis with Finer-Grained Abstract Semantics}, year = {2026}, journal = {Proc. ACM Program. Lang.}, volume = {10}, number = {PLDI}, pages = {1029--1053}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {2475-1421}, keywords = {Program Synthesis, Abstract Semantics, Tree Automata} }

2025

arXiv
Tensor Network Structure Search Via Canonical Dimension Tree Enumeration

Zheng Guo, Aditya Deshpande, Brian Kiedrowski, Xinyu Wang, and Alex Gorodetsky

arXiv preprint arXiv:2502.02711, 2025

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Tensor networks provide a powerful framework for compressing multi-dimensional data. The optimal tensor network structure for a given data tensor depends on both data characteristics and specific optimality criteria, making tensor network structure search a challenging problem. Existing solutions typically rely on sampling and compressing numerous candidate structures; these procedures are computationally expensive and therefore limiting for practical applications. We address this challenge by decoupling topology enumeration from rank assignment search. We first represent the search space using canonical dimension trees, a hierarchical structure that encodes potential network topology through nested index partitions. This representation inherently rules out redundant and suboptimal topologies by construction. To mitigate the assessment bottleneck, we introduce a mechanism powered by the precomputation of a singular value map. By archiving the singular values of all feasible tensor matricizations, we transform the evaluation of any candidate dimension tree into a constraint-solving problem. This formulation yields an empirically near-optimal rank assignment via simple metadata lookups, allowing us to compute structural costs directly and bypass expensive on-the-fly tensor decompositions for all but the final selected candidate. Experimental results show that our approach accelerates the structure search by up to 10x while achieving highly competitive compression ratios, outperforming standard tensor trains and hierarchical tuckers by up to 10x, and matching or exceeding state-of-the-art structure search tools. Notably, our approach scales to larger tensors that are unattainable by prior work. Furthermore, the discovered topologies generalize well to similar data; they achieve compression ratios up to 2.4x better than tensor trains or hierarchical tuckers, while maintaining a search time of approximately 110 seconds for 6D tensors of 1-2GB disk size.
@article{tnss, author = {Guo, Zheng and Deshpande, Aditya and Kiedrowski, Brian and Wang, Xinyu and Gorodetsky, Alex}, title = {Tensor Network Structure Search Via Canonical Dimension Tree Enumeration}, year = {2025}, journal = {arXiv preprint arXiv:2502.02711}, keywords = {Tensor network structure search, Tensor networks, Low-rank tensor approximation, AI for Science} }

AIOps

Automated Lifting for Cloud Infrastructure-as-Code Programs

Jingjia Peng, Yiming Qiu, Patrick Tser Jern Kon, Pinhan Zhao, Yibo Huang, Zheng Guo, Xinyu Wang, and Ang Chen

In 6th International Workshop on Cloud Intelligence / AIOps (AIOps ’25), ICSE , 2025

Bib HTML

@inproceedings{iac_lifting,
  author = {Peng, Jingjia and Qiu, Yiming and Kon, Patrick Tser Jern and Zhao, Pinhan and Huang, Yibo and Guo, Zheng and Wang, Xinyu and Chen, Ang},
  title = {Automated Lifting for Cloud Infrastructure-as-Code Programs},
  year = {2025},
  booktitle = {6th International Workshop on Cloud Intelligence / AIOps (AIOps '25), ICSE},
  keywords = {Infrastructure-as-Code, Program Lifting, Cloud}
}

2022

ICFP

Searching Entangled Program Spaces

James Koppel, Zheng Guo, Edsko Vries, Armando Solar-Lezama, and Nadia Polikarpova

Proc. ACM Program. Lang., Aug 2022

Bib PDF Extended Code

@article{hectare,
  author = {Koppel, James and Guo, Zheng and de Vries, Edsko and Solar-Lezama, Armando and Polikarpova, Nadia},
  title = {Searching Entangled Program Spaces},
  year = {2022},
  issue_date = {August 2022},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {6},
  number = {ICFP},
  url = {https://doi.org/10.1145/3547622},
  doi = {10.1145/3547622},
  journal = {Proc. ACM Program. Lang.},
  month = aug,
  articleno = {91},
  numpages = {29},
  keywords = {type systems, Haskell, program synthesis, e-graphs}
}

PLDI

Type-Directed Program Synthesis for RESTful APIs

Zheng Guo, David Cao, Davin Tjong, Jean Yang, Cole Schlesinger, and Nadia Polikarpova

In Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation , San Diego, CA, USA, Aug 2022

Bib PDF Extended Code Slides

@inproceedings{apiphany,
  author = {Guo, Zheng and Cao, David and Tjong, Davin and Yang, Jean and Schlesinger, Cole and Polikarpova, Nadia},
  title = {Type-Directed Program Synthesis for RESTful APIs},
  year = {2022},
  isbn = {9781450392655},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3519939.3523450},
  doi = {10.1145/3519939.3523450},
  booktitle = {Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation},
  pages = {122–136},
  numpages = {15},
  keywords = {RESTful API, Type Inference, Program Synthesis},
  location = {San Diego, CA, USA},
  series = {PLDI 2022}
}

2020

OOPSLA

Digging for Fold: Synthesis-Aided API Discovery for Haskell

Michael B. James, Zheng Guo, Ziteng Wang, Shivani Doshi, Hila Peleg, Ranjit Jhala, and Nadia Polikarpova

Proc. ACM Program. Lang., Nov 2020

Bib PDF Code

@article{hplus,
  author = {James, Michael B. and Guo, Zheng and Wang, Ziteng and Doshi, Shivani and Peleg, Hila and Jhala, Ranjit and Polikarpova, Nadia},
  title = {Digging for Fold: Synthesis-Aided API Discovery for Haskell},
  year = {2020},
  issue_date = {November 2020},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {4},
  number = {OOPSLA},
  url = {https://doi.org/10.1145/3428273},
  doi = {10.1145/3428273},
  journal = {Proc. ACM Program. Lang.},
  month = nov,
  articleno = {205},
  numpages = {27},
  keywords = {Type Inference, Program Synthesis, Human-Computer Interaction}
}

2019

POPL

Program Synthesis by Type-Guided Abstraction Refinement

Zheng Guo, Michael James, David Justo, Jiaxiao Zhou, Ziteng Wang, Ranjit Jhala, and Nadia Polikarpova

Proc. ACM Program. Lang., Dec 2019

Bib PDF Code Slides

@article{tygar,
  author = {Guo, Zheng and James, Michael and Justo, David and Zhou, Jiaxiao and Wang, Ziteng and Jhala, Ranjit and Polikarpova, Nadia},
  title = {Program Synthesis by Type-Guided Abstraction Refinement},
  year = {2019},
  issue_date = {January 2020},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {4},
  number = {POPL},
  url = {https://doi.org/10.1145/3371080},
  doi = {10.1145/3371080},
  journal = {Proc. ACM Program. Lang.},
  month = dec,
  articleno = {12},
  numpages = {28},
  keywords = {Abstract Interpretation, Program Synthesis, Type Systems}
}