IEEE Data Science and Learning Workshop (DSLW)
Event submitted on Monday, October 12th 2020, approved by Henry Dalzel ✓
This event has been tagged as follows:
This is a highly recommended event because it is organized by the IEEE Signal Processing Society (supported by the SPS Data Science Initiative). Its main focus is to bring together researchers in academia and industry to share the most recent and exciting advances in data science and learning theory and applications. This workshop provides innovative data science & learning studies in academic disciplines, that includes signal processing, statistics, machine learning, data mining and computer vision.
The following description was either submitted by the Conference Organizer on Monday, October 12th 2020, or created by us.
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 (supported by the SPS Data Science Initiative). Though evolved from the IEEE Data Science Workshop, DSLW 2021 has been reformatted as a new 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.