About Me

I am third-year Ph.D. student in PKU-PLL, School of Computer Science, Peking University. I am currently advised by Prof. Yingfei Xiong. I also worked with Prof. Hongfei Fu and Prof. Bingkai Lin. I am interested in problems about programs and programming languages, especially in program synthesis, decision procedures and probabilistic program verification. I also have some work on inapproximatability under the parameterized complexity regime.

Education

  • Ph.D. Student in Computer Science, 2021-

    PKU-PLL, School of Computer Science, Peking University

  • BSc in Computer Science, 2017-2021

    Turing Class, School of EECS, Peking University

Publications

(2024). Almost Optimal Time Lower Bound for Approximating Parameterized Clique, CSP, and More, under ETH. Preprint.

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(2024). Parameterized Inapproximability Hypothesis under ETH. The 56th Annual ACM Symposium on Theory of Computing (STOC'24, Best Paper Award).

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(2023). Synthesizing Efficient Memoization Algorithms. Object-Oriented Programming, Systems, Languages, and Applications 2023 (OOPSLA'23, Round 2).

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(2023). Improved Hardness of Approximating k-Clique under ETH. 64th IEEE Symposium on Foundations of Computer Science (FOCS'23).

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(2023). Automated Tail Bound Analysis for Probabilistic Recurrence Relations. 35th International Conference on Computer Aided Verification (CAV'23).

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(2022). Constant Approximating Parameterized k-SetCover is W[2]-hard. ACM-SIAM Symposium on Discrete Algorithms (SODA'23).

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(2022). On Lower Bounds of Approximating Parameterized k-Clique. The 49th International Colloquium on Automata, Languages, and Programming (ICALP'22).

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(2021). Quantitative Analysis of Assertion Violations in Probabilistic Programs. Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI'21).

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(2020). Guiding Dynamic Programing via Structural Probability for Accelerating Programming by Example. Object-Oriented Programming, Systems, Languages, and Applications 2020 (OOPSLA'20).

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(2019). TreeGen: A Tree-Based Transformer Architecture for Code Generation. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI'20).

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