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IEEE DSLW 2022 : IEEE Data Science and Learning Workshop

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Link: https://conferences.ece.ubc.ca/dslw2022/
 
When May 22, 2022 - May 23, 2022
Where Singapore
Submission Deadline Nov 10, 2021
Notification Due Feb 15, 2022
Final Version Due Mar 10, 2022
Categories    machine learning   signal processing   data science   optimization
 

Call For Papers

The 2022 IEEE Data Science & Learning Workshop (DSLW 2022), to be co-located with ICASSP 2022, will be held at Nanyang Technological University (NTU), Singapore, on May 22-23, 2022. The workshop is organized by the IEEE Signal Processing Society and supported by the SPS Data Science Initiative. DSLW 2022 is envisioned and implemented by the SPS Data Science Initiative as a workshop with a low acceptance rate. 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 various domains - such as 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.

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