Halyun Jeong

University of California, Los Angeles
Email: hajeong@math.ucla.edu

I am an assistant adjunct professor in the mathematics department at UCLA. My research mentor is Professor Deanna Needell.
Previously, I was a PIMS postdoc at the University of British Columbia. I received my Ph.D. degree in Mathematics from Courant Institute of Mathematical Sciences at New York University in 2017.


My research interests span mathematical aspect of signal processing and machine learning including geometry of high-dimensional data sets, nonlinear signal recovery such as compressed sensing, and computationally efficient optimization. As a postdoc at UBC, I worked on concentration of random matrices on sets, performance analysis of iterative algorithms for one-bit compressed sensing, and manifold identification properties of proximal gradient methods and gauge-dual based algorithms. For my Ph.D. thesis, I studied phase retrieval, and quantization of phaseless measurements, and analyzed a randomized A/D conversion algorithm that eliminates spectral artifacts.


  • Zhenan Fan, Halyun Jeong, Babhru Joshi, and Michael P. Friedlander, Polar Deconvolution of mixed signals, Accepted for publication at IEEE Transactions on Signal Processing [ArXiv link]
  • Michael Friedlander, Halyun Jeong, Yaniv Plan, and Ozgur Yilmaz, NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate, IEEE Transactions on Information Theory, 2022 [journal link]
  • Halyun Jeong, Xiaowei Li, Yaniv Plan, Ozgur Yilmaz, Sub-Gaussian Matrices on Sets: Optimal Tail Dependence and Applications, Communications on Pure and Applied Mathematics, 2021 [journal link]
  • Zhenan Fan, Halyun Jeong, Michael Friedlander, Yifan Sun, Polar Alignment and Atomic Decomposition, Foundations and Trends in Optimization, Volume 3:280-366, 2020 [journal link] [pdf]
  • Halyun Jeong, Xiaowei Li, Yaniv Plan and Ozgur Yilmaz, Non-Gaussian Random Matrices on Sets: Optimal Tail Dependence and Applications, Proceedings of International Conference on Sampling Theory and Applications (SampTA), 2019
  • Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt, Are we there yet? Manifold identification of gradient-related proximal methods, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 link.
  • Halyun Jeong and C. Sinan Güntürk, Convergence of the randomized Kaczmarz method for phase retrieval, Preprint.
  • Halyun Jeong, Thang Huynh, and C. Sinan Güntürk, Distributed noise-shaping quantization for phase retrieval, In preparation.
  • Halyun Jeong, Spectral analysis of ΣΔ modulation with dithering, In preparation.
  • Halyun Jeong and Young-Han Kim, Sparse linear representation , Proceedings of the IEEE International Conference on Symposium on Information Theory (ISIT) - volume 1, 2009, pp. 329–333 arXiv: 0905.1990

  • Teaching at UCLA

    Math156 Machine Learning

    Math170E Probability and Statistics: Probability

    Math170S Probability and Statistics: Statistics

    Math151B Applied Numerical Methods

    Teaching at UBC

    Winter 2019 Term 2: Math307 (Applied linear algebra)

    Winter 2018 Term 2: Math221 (Matrix algebra)

    Winter 2018 Term 1: Math307 (Applied linear algebra)

    Winter 2017 Term 1: Math307 (Applied linear algebra)

    Teaching at NYU

    Fall 2016: Calculus 1 recitation

    Fall 2015: Honors III (Fourier analysis) recitation

    Fall 2014: Algebra and Calculus (Precalculus) recitation