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New webinar talk series: Data Science on Graphs (DEGAS)
4 November 2021: Start of the DSI Webinar series on Data Science on Graphs. These biweekly talks provide the SP community with updates and advances in learning and inference on graphs. Signal processing and machine learning often deal with data living in regular domains such as space and time. This webinar series will cover the extension of these methods to network data.
First presentation is November 4, 2021: "Network GPS - A Perturbative Theory of Network Dynamics" by Baruch Barzel (Bar-Ilan University)
2022 IEEE Data Science & Learning Workshop (DSLW 2022)
DSLW 2022 is co-located with ICASSP 2022, and will be held at Nanyang Technological University (NTU), Singapore, on May 22-23, 2022. The workshop is organized by the IEEE Signal Processing Society (supported by the SPS Data Science Initiative). Evolved from the IEEE Data Science Workshop, DSLW 2022 is a high-quality workshop that brings together researchers in academia and industry to share the most recent and exciting advances in data science and learning theory, and applications in various domains (e.g., health care, earth and environmental science, applied physics, finance and economics, intelligent manufacturing).
Deadline for paper submission: 10 November 2021
2021-2022 Brain Space Initiative talk series
The Brain Space Initiative talk series continues in Fall 2021 with a new series of talks in the domain of non-invasive brain imaging techniques. You can also join one of the 8 study/discussion groups. Also, don't miss the Cognitive Neuroscience Journal Club presentations!
Brain Space Initiative Talk Series: Leveraging biological knowledge: From Brain Mapping to predictive models
September 25, 2020: The Brain Space Initiative Talk Series: Leveraging biological knowledge: From Brain Mapping to predictive models, as part of the activities of the Brain Space Initiative, co-sponsored by the Data Science Initiative, IEEE Signal Processing Society. Presented by Dr. Simon Eickhoff. The long predominant paradigm in neuroimaging has been to compare (mean) local volume or activity between groups, or to correlate these to behavioral phenotypes. Such approach, however, is intrinsically limited in terms of possible insight into inter-individual differences and application in clinical practice. Recently, the increasing availability of large cohort data and tools for multivariate statistical learning, allowing the prediction of individual cognitive or clinical phenotypes in new subjects, have started a revolution in imaging neuroscience.
2021 IEEE Data Science and Learning Workshop (DSLW 2021)
June 5-6, 2021: The 2021 IEEE Data Science & Learning Workshop (DSLW 2021), to be co-located with ICASSP 2021, will be held at the University of Toronto on June 05-06, 2021. The workshop is organized by the IEEE Signal Processing Society. It aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science and learning theory and applications. The workshop provides a venue for innovative data science & learning studies in various academic disciplines, including signal processing, statistics, machine learning, data mining and computer vision.
Collaboration at ICASSP
Collaborative Sessions at ICASSP 2020.
In order to highlight the fact that "Data Science" really comprises a lot of signal processing and cuts across numerous areas within signal processing, the DSI organized two "collaborative sessions in data science" at ICASSP 2020 consisting of papers that consider common problems in data science:
These sessions featured 12 papers with topics ranging from financial engineering to music recommendations to dictionary learning, showcasing the broad range of topics in data science.
First TReNDS Neuroimaging Competition
The First TReNDS Neuroimaging Competition: Multiscanner normative age and assessments prediction with brain function, structure, and connectivity
Human brain research is among the most complex areas of study for scientists. With much of the research using MRI scans, data scientists are well positioned to support future insights. In particular, neuroimaging specialists look for measurable markers of behavior, health, or disorder to help identify relevant brain regions and their contribution to typical or symptomatic effects. In this competition, participants predict multiple assessments plus age from multimodal brain MRI features.