2021 IEEE Data Science and Learning Workshop DSLW

活动简介

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 are welcome.

Sponsor Type:1

组委会

Honorary Chair

Li Deng, Citadel, USA

General Chairs

Stark Draper, University of Toronto
Z. Jane Wang, University of British Columbia

Technical Program Chairs

Purang Abolmaesumi, University of British Columbia
Qiang Yang, HKUST / WeBank
Dong Yu, Tencent AI Lab, USA
Ivan V. Bajić, Simon Fraser University
Parvin Mousavi, Queen’s University

Finance Chair

Gene Cheung, York University

Publication Chairs

Xun Chen, USTC, China

International Liaison Chair

Chunyan Miao, Nanyang Technological University

SPS Liaison

Peter Schreier, Universität Paderborn

Advisory Committee Chair

Rabab Ward, University of British Columbia

征稿信息

重要日期

2020-10-28

初稿截稿日期

2021-02-15

初稿录用日期

征稿范围

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)