Introduction

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

Committee

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

Call for paper

Important date

2020-10-28
Draft paper submission deadline
2021-02-15
Draft paper acceptance notification

Submission Topics

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)

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Important Date
  • Conference Date

    Jun 05

    2021

    to

    Jun 06

    2021

  • Oct 28 2020

    Draft paper submission deadline

  • Feb 15 2021

    Draft Paper Acceptance Notification

  • Jun 06 2021

    Registration deadline

Sponsored By
IEEE Signal Processing Society