Thuy-Duong "June" Vuong

Email:   thvuong at ucsd dot edu Links: CV, Google Scholar

Publications and Preprints:

1. Parallel sampling via autospeculation, joint with Nima Anari, Carlo Baronio, CJ Chen, Alireza Haqi, Frederic Koehler, and Anqi Li. ACM Symposium on the Theory of Computing (STOC 2026). [arXiv:2511.07869]. Slides at Simons MPG Reunion Workshop (20 minutes). Slides at Caltech Combinatorics Seminar (60 minutes)
2. On quantum to classical comparison for Davies generators, joint with Joao Basso, Shirshendu Ganguly, Alistair Sinclair, Nikhil Srivastava, Zachary Stier. Quantum Information Processing (QIP 2026) (Regular Talk). [arXiv:2510.07267]. Slides at QIP (30 minutes). Slides at UC Davis Probability Seminar (1 hour).
3. Composable Coresets for Constrained Determinant Maximization and Beyond, with Sepideh Mahabadi. International Conference on Artificial Intelligence and Statistics (AISTATS 2026) (Spotlight). [arXiv:2211.00289]
4. Efficiently learning and sampling multimodal distributions with data-based initialization, joint with Frederic Koehler and Holden Lee. Conference on Learning Theory (COLT 2025). [arXiv:2411.09117]. Slides
5. Fast parallel sampling under isoperimetry, joint with Nima Anari and Sinho Chewi. Conference on Learning Theory (COLT 2024). [arXiv:2401.09016]. Slides
6. Trickle-Down in Localization Schemes and Applications, joint with Nima Anari and Frederic Koehler. ACM Symposium on the Theory of Computing (STOC 2024). [arXiv:2407.16104]. Slides
7. Fairness in submodular maximization over a matroid constraint*, joint with Marwa El Halabi, Jakub Tarnawski and Ashkan Norouzi-Fard. International Conference on Artificial Intelligence and Statistics (AISTATS 2024). [arXiv:2312.14299]
8. Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization, joint with Frederic Koehler. International Conference on Learning Representations (ICLR 2024). [arXiv:2310.01762]
9. Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses, joint with Nima Anari, Vishesh Jain , Frederic Koehler, and Huy Tuan Pham. ACM-SIAM Symposium on Discrete Algorithms (SODA 2024). [arXiv:2307.10466]
10. Optimal mixing of the down-up walk on independent sets of a given size, joint with Vishesh Jain , Marcus Michelen, and Huy Tuan Pham. IEEE Symposium on Foundations of Computer Science (FOCS 2023). [arXiv:2305.06198]
11. Parallel Discrete Sampling via Continuous Walks, joint with Nima Anari, Yizhi Huang, Tianyu Liu, Brian Xu and Katherine Yu. ACM Symposium on the Theory of Computing (STOC 2023).
12. Quadratic Speedups in Parallel Sampling from Determinantal Distributions, joint with Nima Anari, Callum Burgess and Kevin Tian. ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2023).
13. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, joint with Nima Anari and Yang P. Liu. IEEE Symposium on Foundations of Computer Science (FOCS 2022). [arXiv:2102.05347v3]. Slides.
14. On the Complexity of Sampling Redistricting Plans, joint with Moses Charikar , Paul Liu and Tianyu Liu. [arXiv:2206.04883]. Slides.
15. Dimension reduction for maximum matchings and the Fastest Mixing Markov Chain, joint with Vishesh Jain and Huy Tuan Pham. Comptes Rendus. Mathématique, 361:869–876, 2023. [arXiv:2203.03858]
16. Entropic Independence II: Optimal Sampling and Concentration via Restricted Modified Log-Sobolev Inequalities, joint with Nima Anari, Vishesh Jain , Frederic Koehler, and Huy Tuan Pham. ACM Symposium on the Theory of Computing (STOC 2022) as a merge with Entropic Independence I. [arXiv:2111.03247]
17. Entropic Independence I: Modified Log-Sobolev Inequalities for Fractionally Log-Concave Distributions and High-Temperature Ising Models, joint with Nima Anari, Vishesh Jain , Frederic Koehler, and Huy Tuan Pham. ACM Symposium on the Theory of Computing (STOC 2022). [arXiv:2106.04105]. Slides. Video.
18. Spectral independence, coupling, and the spectral gap of the Glauber dynamics, joint with Vishesh Jain and Huy Tuan Pham. Information Processing Letters. [arXiv:2105.01201]
19. Domain Sparsification of Discrete Distributions using Entropic Independence, joint with Nima Anari, Michał Dereziński, and Elizabeth Yang. Innovations in Theoretical Computer Science (ITCS 2022). [arXiv:2102.05347v3]. Slides.
20. On the sampling Lovász Local Lemma for atomic constraint satisfaction problems, joint with Vishesh Jain and Huy Tuan Pham. [arXiv:2102.08342]
21. Towards the sampling Lovász Local Lemma, joint with Vishesh Jain and Huy Tuan Pham. IEEE Symposium on Foundations of Computer Science (FOCS 2021). [arXiv:2011.12196].
22. Fractionally log-concave and sector-stable polynomials: counting planar matchings and more, joint with Yeganeh Alimohammadi, Nima Anari, and Kirankumar Shiragur. ACM Symposium on the Theory of Computing (STOC 2021). [arXiv:2102.02708v2]. Slides. Video.
23. Log-concave polynomials IV: approximate exchange, tight mixing times, and near-optimal sampling of forests, joint with Nima Anari, Kuikui Liu, Shayan Oveis Gharan and Cynthia Vinzant. ACM Symposium on the Theory of Computing (STOC 2021). [arXiv:2004.07220]. Slides.
24. From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization, with Nima Anari. Conference on Learning Theory (COLT 2022). [arXiv:2102.05347v3]. Slides.
25. An Extension of Plucker Relations with Applications to Subdeterminant Maximization, joint with Nima Anari. International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2020). [arXiv:2004.13018]. Slides.