Zé Vinícius

Sai Kung, 2022

Hong Kong, China

Hi there! I’m Zé Vinícius, a PhD candidate at HKUST, in sunny Hong Kong, working with Prof. Daniel Palomar on research problems involving graphs and financial time series. I design optimization algorithms using elements of graph theory and statistical learning theory to extract knowledge from networks of financial assets.

I have done a few internships along the way:

equity quant at Merrill Lynch: wrote code for portfolio risk optimization and limit order book forecasting;
research scientist at Shell Street Labs: wrote code for portfolio strategy optimization;
scientific software engineer at NASA: wrote code for lightkurve;
Google Summer of Code developer for OpenAstronomy: improved the point spread function photometry capabilities of photutils;
guest researcher at NIST: research on nanophotonics published in Nature and Review of Scientific Instruments;

I spend most of my time doing research and coding. I also act as a reviewer for NeurIPS, ICML, ICLR, JMLR, and IEEE TNNLS. 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 nephew Chico and my dog Pluto.

selected publications

NeurIPS

Learning Bipartite Graphs: Heavy Tails and Multiple Components

Cardoso, J. V. M., Ying, J., and Palomar, D. P.

In Advances in Neural Information Processing Systems 2022

HTML PDF Code
NeurIPS

Graphical Models in Heavy-Tailed Markets

Cardoso, J. V. M., Ying, J., and Palomar, D. P.

In Advances in Neural Information Processing Systems 2021

HTML PDF Code
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
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