Preprints & Publications:

  1. Budget-limited distribution learning in multifidelity problems (with A. Narayan), submitted (2021). [arxiv] [demo]
  2. A bandit-learning approach to multifidelity approximation (with V. Keshavarzzadeh, R.M. Kirby and A. Narayan), submitted (2021). [arxiv]
  3. Randomized weakly admissible meshes (with A. Narayan), submitted (2021). [arxiv]
  4. Analysis of the ratio of l1 and l2 norms in compressed sensing (with A. Narayan, H. Tran and C. Webster), submitted (2020). [arxiv]
  5. A general pairwise comparison model for extremely sparse networks (with R. Han and K. Chen), submitted (2020). [arxiv]
  6. Consistency of archetypal analysis (with B. Osting, D. Wang and D. Zosso), SIAM Journal on Mathematics of Data Science 3 (2021), no. 1, 1-30. [doi]

Study notes:

  1. Notes on bandit learning [link]
  2. Forecasting Power Outages for Tropical Cyclones (with D. Arokiasamy, L. Damiano, M. Dao, S. Gailliot, A. Horiguchi and R. Kesawan), SAMSI Final Report (2019) [link]
  3. A note on information bottleneck principle [link]
  4. A brief note on discrete-time Markov chains [link]

Academic involvement:

  1. Participant, Mathematics of Big Data Summer School, MSRI, Berkeley, CA, 2021.06
  2. Participant, Advanced Short-Term Research Opportunity Program (ASTRO), Oak Ridge, TN, 2020.06-08 (suspended due to Covid-19)
  3. Participant, Industrial Math/Stat Modeling Workshop for Graduate Students, SAMSI, Raleigh, NC, 2019.07
  4. Participant, Michigan Summer School on Random Matrices, Ann Arbor, MI, 2018.06
  5. Participant, Frontier Probability Days, Corvallis, OR, 2018.03


  1. Optimization & Machine learning Seminar, CUHK, Shenzhen, 2021.05
  2. SIAM CSE21 minisymposium on Theory and Applications of Graph-based Learning, 2021.03
  3. BYU/USU Applied Math/PDE Seminar, 2020.10
  4. Poster session, Computational Statistics and Data-Driven Models, ICERM, 2020.04