News

My former PhD student Junxiao Song pushing the limits with DeepSeek
As we all know, DeepSeek has recently pushed the limits in LLMs through groundbreaking innovations. I’m proud to share that my former PhD student Junxiao Song serves as a Principal Researcher at DeepSeek AI, where he:
Jan 31, 2025 Papers, Software
My former PhD student Junxiao Song pushing the limits with DeepSeek
Paper award at Asilomar
Yifan Yu has been awarded the Best Student Paper Award (Bronze) at the Asilomar Conference on Signals, Systems, and Computers! 🎉 Our paper, titled “Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach,” introduces efficient methods for extending the Kalman filter to handle non-Gaussian observations—particularly useful in applications such as financial data analysis.
Nov 9, 2024 Papers
Paper award at Asilomar
Portfolio Optimization book available online
Daniel P. Palomar. Portfolio Optimization. Cambridge University Press, 2025.
Aug 15, 2024 Papers
Portfolio Optimization book available online
Paper published in Nature Communications
Syed Awais W. Shah, Daniel P. Palomar, Ian Barr, Leo L. M. Poon, Ahmed Abdul Quadeer, and Matthew R. McKay, “Seasonal antigenic prediction of influenza A H3N2 using machine learning,” Nature Communications, vol.
May 7, 2024 Papers
Paper published in Nature Communications
Paper on graph learning accepted in IEEE Trans. on Signal Processing
Amirhossein Javaheri, Arash Amini, Farokh Marvasti, and Daniel P. Palomar, “Learning Spatio-Temporal Graphical Models From Incomplete Observations,” IEEE Trans. on Signal Processing, vol. 72, pp. 1361-1374, 2024.
Jan 23, 2024 Papers
Paper on graph learning accepted in IEEE Trans. on Signal Processing
Paper published in NeurIPS
Jian-Feng Cai, José Vinícius de M. Cardoso, Daniel P. Palomar, and Jiaxi Ying, “Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec.
Dec 10, 2023 Papers
Paper published in NeurIPS
Paper published in NeurIPS
Xiwen Wang, Jiaxi Ying, and Daniel P. Palomar, “Learning Large-Scale MTP2 Gaussian Graphical Models via Bridge-Block Decomposition,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec. 2023.
Dec 10, 2023 Papers
Paper published in NeurIPS
Paper on high-order portfolios accepted in IEEE Trans. on Signal Processing
Xiwen Wang, Rui Zhou, Jiaxi Ying, and Daniel P. Palomar, “Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution,” IEEE Trans. on Signal Processing, vol. 71, pp. 3726-3740, 2023.
Aug 4, 2023 Papers
Paper on high-order portfolios accepted in IEEE Trans. on Signal Processing
Paper published in IEEE Trans. on Signal Processing
Shengjie Xiu, Xiwen Wang, and Daniel P. Palomar, “A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization,” IEEE Trans. on Signal Processing, vol. 71, pp. 2713-2727, July 2023.
Jul 1, 2023 Papers
Paper published in IEEE Trans. on Signal Processing
R package intradayModel published in CRAN
R package intradayModel for design of High-Order Portfolios published in CRAN.
May 19, 2023 Software
R package intradayModel published in CRAN
Keynote talk at the ICAIF'22 Workshop
Keynote Talk entitled “Learning Financial Graphs” at the ICAIF'22 Workshop on NLP and Network Analysis in Financial Applications, ACM Conference on AI in Finance, New York, USA.
Nov 2, 2022 Talk
Keynote talk at the ICAIF'22 Workshop
R package highOrderPortfolios published in CRAN
R package highOrderPortfolios for design of High-Order Portfolios published in CRAN.
Oct 20, 2022 Software
R package highOrderPortfolios published in CRAN
Plenary talk at the SLSIP Workshop 2022
Plenary Talk entitled “High-Order Portfolios: The Role of Heavy Tails and Skewness” at the Statistical Learning for Signal and Image Processing (SLSIP) Workshop 2022, Cadaques, Catalunya, Spain.
Oct 12, 2022 Talk
Plenary talk at the SLSIP Workshop 2022
Paper accepted in NeurIPS!
José Vinícius de M. Cardoso, Jiaxi Ying, and Daniel P. Palomar, “Learning Bipartite Graphs: Heavy Tails and Multiple Components,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec.
Sep 15, 2022 Papers
Paper accepted in NeurIPS!
Jiaxi's belated graduation dinner
Jiaxi YING defended his PhD thesis on 8-March-2022, but only today, 9-Sep-2022, we were able to celebrate it properly!
Sep 9, 2022
Jiaxi's belated graduation dinner
Yifan joins our research group
Yifan YU has joined our group to pursue her PhD.
Sep 2, 2022
Yifan joins our research group
R package portfolioBacktest updated in CRAN
R package portfolioBacktest for portfolio backtesting. As usual, it has been regularly updated with new features.
Apr 22, 2022 Software
R package portfolioBacktest updated in CRAN
R package fitHeavyTail updated in CRAN
R package fitHeavyTail for mean and covariance matrix estimation under heavy tails. Now it can fit skewed distributions as well! Xiwen has now become a co-author of the package.
Apr 16, 2022 Software
R package fitHeavyTail updated in CRAN
Paper accepted in AAAI!
Xiwen Wang, Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “Efficient Algorithms for General Isotone Optimization,” in The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), Feb. 2022.
Nov 1, 2021 Papers
Paper accepted in AAAI!
Awais and Pau join our research group
Syed Awais Wahab Shah has joined our group as a co-advised PhD student. His main PhD advisor is Prof. Matthew McKay. Pau Cartanya Caro is working toward his final year degree thesis at HKUST.
Oct 15, 2021
Awais and Pau join our research group
Paper accepted in NeurIPS!
José Vinícius de M. Cardoso, Jiaxi Ying, and Daniel P. Palomar, “Graphical Models for Heavy-Tailed Markets,” Advances in Neural Information Processing Systems (NeurIPS), Virtual, Dec. 2021. [26% acceptance rate] [video] [supplemental material] [slides] [poster]
Sep 29, 2021 Papers
Paper accepted in NeurIPS!
Runhao joins our research group
Runhao SHI has joined our research group to pursue her PhD.
Sep 1, 2021
Runhao joins our research group
New webpage for the convex group!
I spent the last week 😫 migrating the group webpage to one ☝️ based on the free open source website builder Wowchemy. I hope you like it!
Aug 8, 2021
New webpage for the convex group!
Rui Zhou has graduated!
Rui Zhou has obtained his PhD degree with his thesis “Optimization in Time Series Modeling and Portfolio Design.” He is now with the Shenzhen Research Institute of Big Data.
Jun 28, 2021
Rui Zhou has graduated!
Talk by Vini on "Learning Graphs in Financial Markets"
“Learning Graphs in Financial Markets” presented by PhD student Jose Vinicius de M. Cardoso. Talk (virtually) delivered at the Hong Kong Machine Learning Meetup, Hong Kong, Jan. 2021. https://www.meetup.com/Hong-Kong-Mach… Related papers: José Vinícius de M.
Jan 27, 2021 Videos
Talk by Vini on "Learning Graphs in Financial Markets"
Our paper has received the 2020 Young Author Best Paper Award by the IEEE Signal Processing Society!!
🏆 Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning,” IEEE Trans. on Signal Processing, vol. 65, no. 3, pp. 794-816, Feb. 2017.
Dec 12, 2020 Papers
Our paper has received the 2020 Young Author Best Paper Award by the IEEE Signal Processing Society!!
Paper accepted in NeurIPS!
Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model,” Advances in Neural Information Processing Systems (NeurIPS), Dec. 2020. [2-min video] [slides] [poster] [R package]
Sep 26, 2020 Papers
Paper accepted in NeurIPS!
R package imputeFin updated in CRAN
R package imputeFin for imputation of financial time series updated with outlier detection.
Jul 11, 2020 Software
R package imputeFin updated in CRAN
Check out my new YouTube channel!
Check out my YouTube channel in the making. Subscribe to be notified of my upcoming posted talks.
May 13, 2020 Videos
Check out my new YouTube channel!
Plenary Talk - "Learning graphs of stocks: From iid to time-varying models"
Plenary Talk by Prof. Daniel P Palomar on “Learning graphs of stocks: From iid to time-varying models,” Graph Signal Processing (GSP) Workshop, Madrid, Spain. Plenary Talk (virtually) delivered at the Graph Signal Processing (GSP) Workshop 2020, Madrid, Spain, May 2020.
May 1, 2020 Videos
Plenary Talk - "Learning graphs of stocks: From iid to time-varying models"
R package imputeFin published in CRAN
R package imputeFin for imputation of financial time series with missing values published in CRAN.
Dec 12, 2019 Software
R package imputeFin published in CRAN
R package fitHeavyTail published in CRAN
R package fitHeavyTail for covariance matrix estimation under heavy tails published in CRAN.
Nov 22, 2019 Software
R package fitHeavyTail published in CRAN
R package portfolioBacktest published in CRAN
R package portfolioBacktest for portfolio backtesting published in CRAN.
Jun 19, 2019 Software
R package portfolioBacktest published in CRAN
R package sparseIndexTracking published in CRAN
R package sparseIndexTracking for computation of sparse portfolio to track an index published in CRAN.
May 17, 2018 Software
R package sparseIndexTracking published in CRAN

Past News

  • Nov. 6, 2019: Invited talk at Apple Inc.: “A Unified Framework for Structured Graph Learning via Spectral Constraints,” Silicon Valley, California, USA

  • Sep. 4, 2019: graph paper “Structured Graph Learning Via Laplacian Spectral Constraints” accepted in NeurIPS [2-min video] [slides] [arxiv] [R package].​

  • Sept. 2, 2019: Tutorial “Portfolio Optimization in Financial Markets,” European Signal Processing Conference (EUSIPCO), A Coruña, Spain.

  • May 17, 2019: Talk in  R/Finance 2019 on the package riskParityPortfolio.
  • May 8, 2019: R package for graph learning via spectral constraints published in CRAN: spectralGraphTopology (GitHub).
  • Dec. 25, 2018: R package for the design of risk parity portfolios published in CRAN: riskParityPortfolio (GitHub​). 600 downloads in 3 days!!
  • Dec. 21, 2018: Invited Talk “Imputation of Time Series with Missing Values under Heavy-Tailed AR Model via Stochastic EM,” International Workshop on Mathematicl Issues on Information Sciences (MIIS), The Chinese University of Hong Kong, Shenzhen, China.
  • Nov. 28, 2018: Invited Talk “Imputation of Time Series with Missing Values under Heavy-Tailed AR Model via Stochastic EM,” Big Data Challenges for Predictive Modeling of Complex Systems, Institute of Mathematical Research, University of Hong Kong, Hong Kong.
  • Fall 2018 semester: teaching the new course MAFS6010R – Portfolio Optimization with R​ as part of the MSc in Financial Mathematics in the Math department at HKUST.
  • June 14, 2018, Invited Talk “Financial Engineering Playground: Signal Processing, Robust Estimation, Kalman, HMM, Optimization, et Cetera,” Aalto University, Finland.
  • June 11, 2018, Plenary Talk “Financial Engineering Playground: Signal Processing, Robust Estimation, Kalman, HMM, Optimization, et Cetera,” IEEE Statistical Signal Processing Workshop (SSP), Freiburg, Germany.
  • 2018 Monograph on Financial Engineering:

​Konstantinos Benidis, ​Yiyong Feng, and Daniel P. Palomar, Optimization Methods for Financial Index Tracking: From Theory to Practice, Foundations and Trends® in Optimization, Now Publishers, 2018.

  • Dec. 20, 2017: R package for computation of sparse eigenvectors published in CRAN: sparseEigen (GitHub).
  • Dec. 11, 2017: Plenary Talk “A Signal Processing and Optimization Perspective on Financial Engineering,” IEEE Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curaçao, Dutch Antilles.
  • Nov. 13-15, 2017: Invited Speaker at the db Global Quant Conference, Deutsche Bank, Hong Kong.
  • 2016 Monograph on Financial Engineering:

​Yiyong Feng and Daniel P. Palomar,  A Signal Processing Perspective on Financial Engineering, Foundations and Trends® in Signal Processing, Now Publishers, vol. 9, no. 1-2, 2016. [pdf]

  • 2015-16 Research Excellence Award at HKUST.
  • 2015 Young Author Best Paper Award (co-author) by the IEEE Signal Processing Society.
  • Oct. 2015: Guest Editor on the Special Issue on Financial Signal Processing and Machine Learning for Electronic Trading in  IEEE Journal of Selected Topics in Signal Processing.
  • Aug. 2015: Invited Talk at ICIAM 2015, Beijing, China.
  • June-July 2015: Organizing Committee for SPAWC 2015,Stockholm, Sweden.
  • April 2015: Organizing Committee for ICASSP 2015, Brisbane, Australia.
  • June 2014: Promoted to Full Professor.
  • Jan. 2013: Became Fellow of the Institute for Advanced Study (IAS) at HKUST.
  • Nov. 2012: Elected IEEE Fellow for “contributions to convex optimization-based signal processing for communications”.
  • July 18-24, 2012: Invited Speaker at the Workshop on Advances in Wireless Communications (WAWC'12), Univ. of Electronic Science and Technology of China (UESTC), Chengdu, China. (Organized by Geoffrey Li and Shaoqian Li.)
  • July 13-17, 2012: Invited Speaker at the Workshop on Advances in Wireless Communications (WAWC'12), Southeast Univ., Nanjing, China. (Organized by Zhi Ding and Xiaohu You.)
  • July 7-13, 2012: Invited Speaker at the International Workshop on Mathematical Issues in Information Sciences (MIIS'12), Xi’an, China. (Organized by Tom Luo and Hong Wei Liu.)
  • Jan. 2012: The research center CTTC-HK becomes operative in Hong Kong.
  • Aug-Sept. 2011: Organizing committee of EUSIPCO 2011, Barcelona, Spain.
  • March 2011: Invited Instructor at the 2011 Winter School of Information Theory, Barcelona, Spain.
  • Aug. 2010: Consultant of research center CTTC-HK in Hong Kong.
  • Dec. 2010: Invited Speaker at the 2010 Workshop on Applications of Optimization in Science and Engineering, Institute for Pure and Applied Mathematics (IPAM), UCLA campus, LA, USA.
  • Aug 2010: Presented tutorial “Convex Optimization, Game Theory, and Variational Inequality Theory in Multiuser Communication Systems” in EUSIPCO 2010, Aalborg, Denmark.
  • July 2010: Promoted to Associate Professor.
  • Jan. 2010: Chair of the IEEE Hong Kong Information Theory Society.
  • May 2010: Guest editor of a special issue in the IEEE Signal Processing Magazine on “Convex Optimization for Signal Processing.”
  • March 2010: Invited Speaker at the 2010 Workshop on Distributed Decision-Making and Control, LCCC, Lunds University, Sweden.
  • Dec. 2009: Publication of the edited the book “Convex Optimization in Signal Processing and Communications” by Cambridge University Press together with Yonina Eldar.
  • Dec. 2009: General Co-Chair of the 2009 IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) in Aruba.
  • June 2009: Organizing Committee of SPAWC 2009, Perugia, Italy.
  • Feb. 2009: Key Note Speaker at the Australian Communication Theory Workshop (AusCTW2009), University of New South Wales, Sydney, Australia.
  • Feb. 2009: Taught an intensive one-day seminar on “Convex Optimization with Applications to Communications” at the University of New South Wales, Sydney, Australia.
  • Highly cited paper (ISI Web of Knowledge) status for my 2003 IEEE Trans. SP paper (coauthored by Lagunas and Cioffi) on majorization theory and convex optimization applied to MIMO transceiver design.
  • Dec. 2008: Taught an intensive 18-hour course on “Convex Optimization for Wireless Communications and Signal Processing” at the National Chiao Tung Univ., Hsinchu, Taiwan. (Co-taught with Ken Ma.)
  • July 2008: Plenary Speaker at the 2008 IEEE Workshop on Signal Proc. Advances in Wireless Comm. (SPAWC), Recife, Brazil.
  • April 2008: Invited Speaker at the “Panel for Convex Optimization and Applications,” ICASSP 2008, Las Vegas, USA.
  • Dec. 2007: Invited Speaker at the 2007 Workshop on Optimization and Signal Processing (WOSP), Hong Kong.