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IEEE DSAA 2021 : 8th IEEE International Conference on Data Science and Advanced Analytics

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Link: https://dsaa2021.dcc.fc.up.pt/
 
When Oct 6, 2021 - Oct 9, 2021
Where Porto, Portugal
Submission Deadline May 23, 2021
Notification Due Jul 25, 2021
Final Version Due Aug 8, 2021
Categories    data science   bayesian learning   optimization   deep learning
 

Call For Papers

Highlights of DSAA 2021

Strong Research and Applications tracks with reproducible and open results.
Industry Poster sessions with lightning results emphasizing research advances and industry’s best practices.
One-day Industry Day
Special sessions on the foundations and emerging areas of data science.
A special panel on challenges, trends and controversies of data science and analytics.
A strong interdisciplinary research program spanning the areas of data science, including statistics, machine learning, computing, and analytics.
Strong cross-domain interactions among researchers, industry, government, policy-makers and practitioners.
Industry and research exhibits.
Financially sponsored by IEEE CIS, proceedings by IEEE Xplore and EI indexed.

Precaution of COVID-19

Due to ongoing uncertainty about future travel due to COVID-19, DSAA'2021 commits to allowing video presentations of accepted papers by authors who are unable to attend due to COVID-19 travel restrictions.
Research Track

The Research Track solicits the latest, original and significant contributions related to foundations and theoretical developments of Data Science and Advanced Analytics. Topics of interest include but are not limited to:

Data Science and Advanced Analytic Methods: active learning, classification, clustering, dimensionality reduction, feature selection, missing data imputation, multi-label and multi-dimensional classification, multi-output regression, online learning, quantum machine learning, regression, reinforcement learning, regularization, semi-supervised learning, spatial statistics, statistical learning theory, time series analysis, autoML, transfer learning.
Bayesian Learning: Bayesian approaches, Bayesian networks, Markov networks, probabilistic graphical models, Bayesian inference, Monte Carlo methods, nonparametric Bayesian methods.
Deep Learning: architectures, convolutional neural networks, deep Boltzman machines, deep neural networks, deep reinforcement learning, generative models, tensor deep stacking networks.
Optimization: Bayesian optimization, convex and non-convex optimization, heuristic optimization, matrix/tensor methods.
Trustworthy Machine Learning: accountability, causality, explainability, fairness, interpretability, privacy, robustness, transparency.
Infrastructures, and systems.
Evaluation, explanation, visualization, and presentation.
Surveys and reviews.





Applications Track

The Application track solicits original, impactful and actionable application results of Data Science and Advanced Analytics across various disciplines and domains, including industry, government, healthcare and medical science, physical sciences, and social sciences. Submissions must address a real problem on real-life data that is reproducible ideally through a public git repository, providing inspiring results to policy-makers, end-users or practitioners or highlighting new practical challenges for researchers. Topics of interest include, but are not limited to:

Domain-driven data science and analytics practice.
Real-world applications and case studies.
Operationalizable infrastructures, platforms and tools.
Deployment, management and decision-making.
System, tool and software demonstrations.
Social, environmental and economic impact modeling.
Ethics, transparency, social issues, privacy, trust, and bias.
Reflections and lessons for better practice.

Paper Formatting, Length, and Double-blind Reviewing, and Reproducible

The paper length allowed for the papers in the Research and Application tracks is a maximum of ten (10) pages.

The format of papers is the standard 2-column U.S. letter style IEEE Conference template. See the IEEE Proceedings Author Guidelines: https://www.ieee.org/conferences/publishing/templates.html for further information and instructions.

All submissions will be blind reviewed by the Program Committee on the basis of technical quality, relevance to the conference’s topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews.

Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for DSAA’2021. An exception to this rule applies to arXiv papers that were published in arXiv at least a month prior to DSAA’2021 submission deadline. Authors can submit these arXiv papers to DSAA provided that the submitted paper’s title and abstract are different from the one appearing in arXiv. Papers that appear in arXiv from the DSAA’2021 submission deadline until the review process has ended, will be rejected without reviews.

Authors are also encouraged to support their papers by providing through a git-type public repository the code and data to support the reproducibility of their results.
Proceedings, Indexing and Special Issues

All accepted full-length papers will be published by IEEE and will be submitted for inclusion in the IEEE Xplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Top-quality papers accepted and presented at the conference will be selected for extension and invited to the special issues of the International Journal of Data Science and Analytics (JDSA, Springer) and some other journals we are working on.
Submission

Submissions to the main conference, including Research track and Applications track, should be made to CMT: https://cmt3.research.microsoft.com/DSAA2021
Important Policies

Reproducibility & supplementary: The advancement of data science depends heavily on reproducibility. We strongly recommend that the authors release their code and data to the public. Authors can provide an optional two (2) page supplement at the end of their submitted paper (it needs to be in the same PDF file and start on page 11). This supplement can only be used to include (i) information necessary for reproducing the experimental results reported in the paper (e.g., various algorithmic and model parameters and configurations, hyper-parameter search spaces, details related to dataset filtering and train/test splits, software versions, detailed hardware configuration, etc.), and (ii) any data, pseudo-code and proofs that due to space limitations, could not be included in the main manuscript. Papers entering into the Best Research Paper and Best Application Paper awards must show solid evidence for reproducibility.

Authorship: The list of authors at the time of submission is final and cannot be changed.

Dual submissions: DSAA is an archival publication venue as such submissions that have been previously published, accepted, or are currently under-review at peer-review publication venues (i.e., journals, conferences, workshops with published proceedings, etc.) are not permitted. DSAA has a strict no dual submission policy.

Conflicts of interest (COI): COIs must be declared at the time of submission. COIs include employment at the same institution at the time of submission or in the past three years, collaborations during the past three years, advisor/advisee relationships, plus family and close friends. Program chairs, poster chairs, special session chairs and tutorial chairs are not allowed to submit proposals to their managed tracks and sessions.

Attendance: At least one author of each accepted paper must register in full and attend the conference to present the paper. No-show papers will be removed from the IEEE Xplore proceedings.

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