KEYNOTE TALKS

Signal Processing and Machine Learning on Graphs: A Filter based Perspective.

Ph.D. Mario Coutiño

Mario Coutiño received his M.Sc. and Ph.D. degree (cum laude) in electrical engineering from the Delft University of Technology, Delft, The Netherlands, in July 2016 and April 2021, respectively. Since 2020, he has been a Signal Processing and Machine Learning Researcher with the Netherlands Organisation for Applied Scientific Research (TNO) in the fields of Radar and Sonar sensing. He has held temporally positions with Thales Netherlands, during 2015, and Bang & Olufsen, during 2016. He was Visiting Researcher with RIKEN AIP and with the Digital Technological Center, University of Minnesota, in 2018 and 2019, respectively. His research interests include array and graph signal processing , optimization theory, machine learning and sensing technology. He was recipient of the CONACYT excellence scholarship, of the Best Student Paper Award at IEEE CAMSAP 2017, of the EDA RTI Papers Award 2023, of the NATO SET 318 Best Paper Award and of the IEEE Signal Processing Society Best PhD Dissertation Award 2023.

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Selected time series and their spectra in geosciences

dr Maciej J. Mendecki

Natural processes often generate time series data across various phenomena. One common type of time series, which can be effectively analyzed through spectral techniques, is ground motion evoked by both natural and anthropogenic events. The characteristics of these events may vary, encompassing waveforms produced by seismic events or ambient seismic noise. In seismology, spectral analysis can be used to filter waveforms for clear signals, identify dominant frequencies that reflect focal characteristics, and even recognize near-surface site effects influenced by local geology. Beyond seismology, time series analysis has applications across other areas of geoscience, where patterns may be less immediately apparent. 

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Integrated Sensing and Communications (ISAC): Goals and Examples

prof. dr hab. inż Tomasz P. Zieliński

Tomasz Piotr Zieliński received the M.Sc. and D.Sc. degrees in electronics and electrical engineering from the AGH University of Science and Technology (AGH-UST), Krakow, Poland, in 1982 and 1996, respectively, and the Ph.D. degree in electrical engineering from the Bulgarian Academy of Sciences, Sofia, Bulgaria, in 1988.,Since 2006, he has been a Full Professor with the Department of Telecommunications, AGH-UST. In 2021, he authored the textbook Starting Digital Signal Processing in Telecommunication Engineering. A Laboratory-Based Course published by Springer Nature. His research interests include advanced digital signal processing in telecommunication, biomedical and smart power delivery systems, particularly time-frequency, and time-scale signal analysis.

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