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.
|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.|
- AISTATSMinimax Estimation of Laplacian Constrained Precision MatricesIn 24th International Conference on Artificial Intelligence and Statistics 2021
- arXivAlgorithms for Learning Graphs in Financial MarketsIn arXiv e-prints 2020
- AsilomarLearning Undirected Graphs in Financial MarketsIn Asilomar Conference on Signals, Systems, and Computers 2020
- arXivDoes the L1 norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?In arXiV e-prints 2020
- NeurIPSNonconvex Sparse Graph Learning under Laplacian Constrained Graphical ModelIn Advances in Neural Information Processing Systems 2020
- JMLRA Unified Framework for Structured Graph Learning via Spectral ConstraintsJournal of Machine Learning Research 2020
- NeurIPSStructured Graph Learning Via Laplacian Spectral ConstraintsIn Advances in Neural Information Processing Systems 2019