Daniel Palomar
Professor at HKUST
Hong Kong Island, Hong Kong, China
Summary
👤
Senior🎓
Top SchoolDaniel Palomar is a professor of optimization at HKUST who applies statistical modeling and deep learning to portfolio optimization and financial data analytics. An IEEE Fellow and EURASIP Fellow, he combines deep theoretical expertise—evident from a PhD and international research visits at Princeton, Stanford and European institutions—with a decades-long academic career at HKUST. His work has earned multiple IEEE Young Author Best Paper Awards and a Fulbright Research Fellowship, reflecting impact across signal processing and finance. Beyond publications, he maintains an active public presence through a well-curated homepage, YouTube channel, and the convexfi GitHub, bridging research, teaching, and open-source tools for optimization.
9 years of coding experience
13 years of employment as a software developer
PhD exchange, Electrical and Electronics Engineering, PhD exchange, Electrical and Electronics Engineering at Stanford UniversityDoctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at UPC - ETSETB TelecomBCNMaster's Degree, Master's Degree at King's College LondonFulbright Scholar, Fulbright Scholar at Princeton University
English, Spanish, Catalan, Italian, Chinese, French
Github Skills (86)
epub10
bookdown10
variance10
latex10
financial-markets10
rmarkdown10
gitbook10
markdown9
sparse-matrix9
stochastic-processes9
html9
datasets9
backtesting9
monte-carlo9
r-package9
Programming languages (5)
Github contributions (5)
dppalomar/sparseEigen
Nov 2017 - Dec 2018
Computation of Sparse Eigenvectors of a Matrix
Contributions:1 release, 80 commits, 1 PR in 1 year 1 month
computationeigenvalueslinear-algebrasparse-matrixsparse
dppalomar/pob
Jun 2021 - Nov 2022
Supporting package for the Portfolio Optimization Book
Contributions:24 commits, 17 pushes in 1 year 5 months
portfolio-optimizationoptimizationsupporting
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