Thuy-Duong "June" Vuong


Email:   tdvuong at berkeley dot edu
Links: CV, Google Scholar

I am a postdoctoral fellow at the Miller Institute, Berkeley, hosted by Alistair Sinclair. I previously received my PhD student in Computer Science at Stanford University in 2024, where I was very fortunate to be advised by Nima Anari and Moses Charikar. I got my Bachelor's degrees in Mathematics and Computer Science at the Massachusetts Institute of Technology (MIT) in 2019.
I am broadly interested in theoretical computer science. My current research interest is in algorithms for sampling from complex high-dimensional distributions, with applications to statistical physics, generative AIs, and other fields.
I will become an assistant professor at UC San Diego CSE in January 2026. Email me if you are interested in my research!

Publications and Preprints

Counting, sampling, and Markov chains

  1. Efficiently learning and sampling multimodal distributions with data-based initialization , joint with Frederic Koehler and Holden Lee. To appear in the 38th Conference on Learning Theory (COLT 2025).
  2. Fast parallel sampling under isoperimetry, joint with Nima Anari and Sinho Chewi. Appear in the 37th Conference on Learning Theory (COLT 2024).
  3. Trickle-Down in Localization Schemes and Applications, joint with Nima Anari and Frederic Koehler. Appear in the 56th ACM Symposium on the Theory of Computing (STOC 2024).
  4. Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization, joint with Frederic Koehler. Appear in the International Conference on Learning Representations (ICLR 2024). [arXiv:2310.01762]
  5. Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses, joint with Nima Anari, Vishesh Jain , Frederic Koehler, and Huy Tuan Pham. Appear in the ACM-SIAM Symposium on Discrete Algorithms (SODA 2024). [arXiv:2307.10466]
  6. Optimal mixing of the down-up walk on independent sets of a given size, joint with Vishesh Jain , Marcus Michelen, and Huy Tuan Pham. Appear in the 64th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2023). [arXiv:2305.06198]
  7. Parallel Discrete Sampling via Continuous Walks, joint with Nima Anari, Yizhi Huang, Tianyu Liu, Brian Xu and Katherine Yu. Appear in the 55th ACM Symposium on the Theory of Computing (STOC 2023).
  8. Quadratic Speedups in Parallel Sampling from Determinantal Distributions, joint with Nima Anari, Callum Burgess and Kevin Tian. Appear in the 35th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2023).
  9. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, joint with Nima Anari and Yang P. Liu. Appear in the 63rd Annual IEEE Symposium on Foundations of Computer Science (FOCS 2022). [arXiv:2102.05347v3]. Slides.
  10. On the Complexity of Sampling Redistricting Plans, joint with Moses Charikar , Paul Liu and Tianyu Liu. [arXiv:2206.04883]. Slides.
  11. Dimension reduction for maximum matchings and the Fastest Mixing Markov Chain, joint with Vishesh Jain and Huy Tuan Pham. Appear in Comptes Rendus. Mathématique, 361:869–876, 2023. [arXiv:2203.03858]
  12. 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. Appear in the 54th ACM Symposium on the Theory of Computing (STOC 2022) as a merge with Entropic Independence I. [arXiv:2111.03247]
  13. 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. Appear in the 54th ACM Symposium on the Theory of Computing (STOC 2022). [arXiv:2106.04105]. Slides. Video.
  14. Spectral independence, coupling, and the spectral gap of the Glauber dynamics, joint with Vishesh Jain and Huy Tuan Pham. Appear in Information Processing Letters. [arXiv:2105.01201]
  15. Domain Sparsification of Discrete Distributions using Entropic Independence, joint with Nima Anari, Michał Dereziński, and Elizabeth Yang. Appear in the 14th Innovations in Theoretical Computer Science (ITCS 2022). [arXiv:2102.05347v3]. Slides.
  16. On the sampling Lovász Local Lemma for atomic constraint satisfaction problems, joint with Vishesh Jain and Huy Tuan Pham. Submitted. [arXiv:2102.08342]
  17. Towards the sampling Lovász Local Lemma, joint with Vishesh Jain and Huy Tuan Pham. Appear in the 62nd Annual IEEE Symposium on Foundations of Computer Science (FOCS 2021). [arXiv:2011.12196].
  18. Fractionally log-concave and sector-stable polynomials: counting planar matchings and more, joint with Yeganeh Alimohammadi, Nima Anari, and Kirankumar Shiragur. Appear in the 53rd ACM Symposium on the Theory of Computing (STOC 2021). [arXiv:2102.02708v2]. Slides. Video.
  19. 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. Appear in the 53rd ACM Symposium on the Theory of Computing (STOC 2021). [arXiv:2004.07220]. Slides.

Optimization

  1. Fairness in submodular maximization over a matroid constraint, joint with Jakub Tarnawski and Ashkan Norouzi-Fard, Appear in the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024).
  2. Composable Coresets for Constrained Determinant Maximization and Beyond, joint with Sepideh Mahabadi . [arXiv:2211.00289]
  3. From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization, joint with Nima Anari. Appear in 35th Conference on Learning Theory (COLT 2022). [arXiv:2102.05347v3]. Slides.
  4. An Extension of Plucker Relations with Applications to Subdeterminant Maximization, joint with Nima Anari. Appear in the 23rd International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2020). [arXiv:2004.13018]. Slides.
During my undergraduate studies, I had the pleasure of doing research with Virginia Vassilevska Williams and Vinod Vaikuntanathan at MIT, and with students and mentors at the REU at University of Minnesota, Twin Cities. These projects have resulted in the following papers.

Undergraduate projects

  1. Lattice trapdoors and IBE from middle-product LWE, joint with Alex Lombardi and Vinod Vaikuntanathan. Appear in Theory of Cryptography Conference (TCC19). [ePrint:2019/1067]
  2. Graph pattern detection: Hardness for all induced patterns and faster non-induced cycles, joint with Mina Dalirrooyfard and Virginia V. Williams. Appear in the 51st ACM Symposium on the Theory of Computing (STOC 2019) and accepted to SIAM Journal on Computing, Volume 50. [arXiv:1904.03741]
  3. Toric Mutations in the dP2 Quiver and Subgraphs of the dP2 Brane Tiling, joint with Yibo Gao, Zhaoqi Li and Lisa Yang. Appear in the Electronic Journal of Combinatorics. [arXiv:1611.05320]

Recent talks

  1. CANADAM Invited Minisymposium on Log-concave polynomials, May 2025
  2. Yale Statistics & Data Science Semina, March 2025
  3. New York University CS Theory Seminar, March 2025
  4. Harvard Probabilitas Seminar, March 2025
  5. Spectral Theory Seminar, UC Berkeley, February 2025
  6. Connections Workshop: Probability and Statistics of Discrete Structures, Simon-Laufer Mathematical Institute, January 2025
  7. SIAM Minisymposium on “Mathematical and Computational Redistricting: Algorithms and Analysis", July 2024
  8. AMS Special Session on Thresholds in Random Structures, January 2024
  9. AMS Special Session on Recent Progress in Inference and Sampling, January 2024
  10. Northwestern Junior Theorists Workshop 2023
  11. Algorithmic & Randomness Center (ARC) Colloquium, Georgia Tech

Services

  1. Journal: Reviewer SIAM Journal on Computing (SICOMP), Transactions on Algorithms (TALG)
  2. Conference: PC Member for STOC 25, SODA 25, RANDOM 25. Reviewer for STOC, FOCS, SODA, ICALP, COLT, NeurIPS, RANDOM, APPROX.
  3. Journal: Reviewer SIAM Journal on Computing (SICOMP), Transactions on Algorithms (TALG)
  4. Mentoring: Stanford Women in Math Mentoring (SWIMM), CURIS, LINXS.

Teaching

  1. Design and Analysis of Algorithms (CS 161), Winter 2022
  2. Counting and Sampling (CS 263), Autumn 2020