IEEE Data Science and Learning Workshop
IEEE DSLW 2022
- URL: https://conferences.ece.ubc.ca/dslw2022/
- Event Date: 2022-05-22 ~ 2022-05-23
- Submission Date: 2021-11-10
- Location: Singapore, Singapore
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.
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.
Journal Promotion
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| Acta Koreana (Acta Koreana) |
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| Acta Via Serica (AVS) |
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| Computers and Concrete (CAC) |
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| Journal of Information Processing Systems (JIPS) |
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| Journal of Internet Computing and Services (KSII - JICS) |
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| Journal of Smart Tourism (Smart Tourism) |
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| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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