Zé Vinícius

Tsim Sha Tsui

Hong Kong, China

Hi there! I’m Zé Vinícius, a PhD candidate at HKUST, in sunny Hong Kong, working with Prof. Daniel Palomar on interesting problems involving graphs and financial time series. More precisely, I design optimization algorithms using elements of graph theory and statistical learning theory to extract knowledge from networks of financial assets. Previously, I interned as a scientific software engineer at NASA in Silicon Valley, California, and NIST in Gaithersburg, Maryland. I was a Google Summer of Code developer for OpenAstronomy.

I spend most of my time doing research and coding. In my free time, there is nothing better than swimming and crab hunting in the waters of Clear Water Bay and video-chatting with my dog, Pluto.

Résumé

news

Sep 1, 2021 I’ve completed my internship at SSL :) Now, I’m back to HKUST where I’ll be a TA for the course Portfolio Optimization with R!
Jul 12, 2021 Our paper “A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields” has been accepted to Asilomar 2021!
Jun 1, 2021 Excited to say that I’m interning as an AI researcher with Shell Street Labs this Summer!
Jan 27, 2021 Our paper Minimax Estimation of Laplacian Constrained Precision Matrices has been accepted to AISTATS 2021! R code lives at github.com/mirca/sparseGraph.
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.

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
    HTML PDF Code
  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