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IEEE DSLW 2021 : IEEE Data Science and Learning Workshop

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Link: https://conferences.ece.ubc.ca/dslw2021/
 
When Jun 5, 2021 - Jun 6, 2021
Where Toronto, Canada
Submission Deadline Oct 15, 2020
Notification Due Feb 15, 2021
Final Version Due Mar 5, 2021
Categories    machine learning   signal processing   data science   optimization
 

Call For Papers

The 2021 IEEE Data Science & Learning Workshop (DSLW 2021), to be co-located with ICASSP 2021, will be held at the University of Toronto, June 5-6, 2021. The workshop is organized by the IEEE Signal Processing Society (supported by the SPS Data Science Initiative). 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. Both studies on theoretical and methodological foundations and application studies in different domains (e.g., health care, earth and environmental science, applied physics, finance and economics, intelligent manufacturing) are welcome.

The technical program will include invited plenary talks, as well as regular oral and poster sessions with contributed research papers. Papers are solicited in, but not limited to, the following areas:

- Statistical learning algorithms, models and theories
- Machine learning theories, models and systems
- Computational models and representation for data science
- Visualization, summarization, and analytics
- Acquisition, storage, and retrieval for big data
- Large scale optimization
- Learning, modeling, and inference with data
- Data science process and principles
- Ethics, privacy, fairness, security and trust in data science and learning (explainable AI, federated learning, collaborative learning, etc)
- Applications: biology and medicine; audio, image, and video analytics; social media; business and finance; applications leveraging domain knowledge for data science.

The organizing committee is closely monitoring the worldwide health and travel situation and will be considering novel hybrid and, if necessary, fully virtual conference models.

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