Daniel P. Palomar

 Hong Kong University of Science and Technology, Hong Kong
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Financial Engineering Playground: Signal Processing, Robust Estimation, Kalman, HMM, Optimization, et Cetera
Video recording available for Signal Processing Society Members
Monday, 11 June
Emperors’ Hall
09:00 – 10:00

Abstract: (more)
Financial engineering may seem alien to many in the signal processing community, but this is a misconception....
Financial engineering may seem alien to many in the signal processing community, but this is a misconception. The underlying connections between financial engineering and signal processing as well as optimization are too strong to be ignored. At the core, engineers try to model the system they deal with, be it a wireless communication channel or the price fluctuations in the financial markets. With a model of the reality in hand, one can then start making forecasts and design strategies for the future. In a wireless link, one may want to optimize the statistics of the signal to be transmitted by the antennas, whereas in a financial market one may attempt to optimize the investment strategies. This talk will provide a glimpse of financial engineering from a signal processing and optimization perspective, including topics on robust estimation, Kalman filtering, and discrete-state HMM, while exploring connections to other engineering disciplines.
 

Bio Sketch: (more)
Daniel P. Palomar is a Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST), and a Fellow of the Institute for Advance Study (IAS) at HKUST.
Daniel P. Palomar is a Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST), and a Fellow of the Institute for Advance Study (IAS) at HKUST.

Daniel P. Palomar received the Electrical Engineering and Ph.D. degrees from the Technical University of Catalonia (UPC), Barcelona, Spain, in 1998 and 2003, respectively.

He is a Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science andTechnology (HKUST), Hong Kong, which he joined in 2006. Since 2013 he is a Fellow of the Institute for Advance Study (IAS) at HKUST. He had previously held several research appointments, namely, at King’s College London (KCL), London, UK; Stanford University, Stanford, CA; Telecommunications Technological Center of Catalonia (CTTC), Barcelona, Spain; Royal Institute of Technology (KTH), Stockholm, Sweden; University of Rome “La Sapienza”, Rome, Italy; and Princeton University, Princeton, NJ. His current research interests include applications of convex optimization theory, game theory, and variational inequality theory to financial systems, big data systems, and communication systems.

Dr. Palomar is an IEEE Fellow, a recipient of a 2004/06 Fulbright Research Fellowship, the 2004 and 2015 (co-author) Young Author Best Paper Awards by the IEEE Signal Processing Society, the 2015-16 HKUST Excellence Research Award, the 2002/03 best Ph.D. prize in Information Technologies and Communications by the Technical University of Catalonia (UPC), the 2002/03 Rosina Ribalta first prize for the Best Doctoral Thesis in Information Technologies and Communications by the Epson Foundation, and the 2004 prize for the best Doctoral Thesis in Advanced Mobile Communications by the Vodafone Foundation and COIT.

He has been a Guest Editor of the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2016 Special Issue on “Financial Signal Processing and Machine Learning for Electronic Trading”, an Associate Editor of IEEE TRANSACTIONS ON INFORMATION THEORY and of IEEE TRANSACTIONS ON SIGNAL PROCESSING, a Guest Editor of the IEEE SIGNAL PROCESSING MAGAZINE 2010 Special Issue on “Convex Optimization for Signal Processing,” the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2008 Special Issue on “Game Theory in Communication Systems,” and the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2007 Special Issue on “Optimization of MIMO Transceivers for Realistic Communication Networks.”