Graph Learning with Applications in Financial Markets
We invite you to attend the upcoming CTTC seminar, “Graph Learning with Applications in Financial Markets” by the Dr. Daniel Palomar.
- Speaker: Dr. Daniel Palomar from Hong Kong University of Science and Technology (HKUST)
- Date & Time: Friday, July 3, 2026 | 10:00 AM CET
- Location: CTTC Auditorium (Building B6)
- Available online
- Duration: Approximately 45 minutes and Q&A
About the Seminar
Graphs are a powerful mathematical tool for representing relationships between entities across domains such as finance, biology, and social networks. In practice, the underlying graph structure is often unknown and must be inferred from observed data, spurring the development of numerous graph learning algorithms over the past decade. Financial markets pose unique challenges, generating high-dimensional, non-Gaussian, cross-asset structured, and time-varying data that strain classical statistical models. This talk surveys recent advances in graph learning with an emphasis on financial data and the unique statistical challenges it presents.
Daniel Palomar received the Ph.D. degree from the Technical University of Catalonia (UPC), Barcelona, Spain, in 2003 and was a Fulbright Scholar at Princeton University during 2004-2006. He is a Professor in the Departments of Electronic & Computer Engineering and Industrial Engineering & Decision Analytics at the Hong Kong University of Science and Technology (HKUST), Hong Kong, which he joined in 2006. His current research interests include optimization methods, high-frequency financial data, and deep learning in financial systems. Dr. Palomar is a EURASIP Fellow (2024) and IEEE Fellow (2012), and has received the 2004, 2015, and 2020 Young Author Best Paper Awards from the IEEE Signal Processing Society.