Zé Vinícius

Clear Water Bay

Hong Kong SAR, China

Zé Vinícius is a PhD candidate at HKUST, in sunny Hong Kong, working with Prof. Daniel Palomar on interesting problems involving graphs and financial time series. Previously, he was a scientific software engineer intern at NASA, in Mountain View, California, and NIST. He was a Google Summer of Code student for OpenAstronomy in 2016.

He spends most of his time doing research and coding that thing dppalomar asked about. In his free time, there is nothing better for him than swimming and crab hunting in the waters of Clear Water Bay and video-chatting with his dog, Pluto.

Résumé

news

Jan 27, 2021 Our paper Minimax Estimation of Laplacian Constrained Precision Matrices has been accepted to AISTATS 2021!
Dec 31, 2020 Our paper Algorithms for Learning Graphs in Financial Markets has been pushed to the arXiv on the last day of 2020. R code lives at github.com/mirca/fingraph.
Sep 26, 2020 Our paper Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model has been accepted to NeurIPS 2020! R code lives at github.com/mirca/sparseGraph.

selected publications

  1. AISTATS
    Minimax Estimation of Laplacian Constrained Precision Matrices
    Ying, J., Cardoso, J. V. M., and Palomar, D. P.
    In 24th International Conference on Artificial Intelligence and Statistics 2021
  2. arXiv
    Algorithms for Learning Graphs in Financial Markets
    Cardoso, J. V. M., Ying, J., and Palomar, D. P.
    In arXiv e-prints 2020
    HTML PDF Code
  3. Asilomar
    Learning Undirected Graphs in Financial Markets
    Cardoso, J. V. M., and Palomar, D. P.
    In Asilomar Conference on Signals, Systems, and Computers 2020
    HTML PDF
  4. arXiv
    Does the L1 norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?
    Ying, J., Cardoso, J. V. M., and Palomar, D. P.
    In arXiV e-prints 2020
    HTML PDF Code
  5. NeurIPS
    Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model
    Ying, J., Cardoso, J. V. M., and Palomar, D. P.
    In Advances in Neural Information Processing Systems 2020
    HTML PDF Code
  6. JMLR
    A Unified Framework for Structured Graph Learning via Spectral Constraints
    Kumar, S., Ying, J., Cardoso, J. V. M., and Palomar, D. P.
    Journal of Machine Learning Research 2020
    HTML PDF Code
  7. NeurIPS
    Structured Graph Learning Via Laplacian Spectral Constraints
    Kumar, S., Ying, J., Cardoso, J. V. M., and Palomar, D. P.
    In Advances in Neural Information Processing Systems 2019
    HTML PDF Code