IEEE/ACM/ASA DSAA’2021
Porto, Portugal, 06-09 October 2021
Website: dsaa2021.dcc.fc.up.pt/
Submission website: cmt3.research.microsoft.com/DSAA2021
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Important Dates Research, Application and Tutorials: submission deadline: 23 May 2021 Research and Application notification: 25 Jul. 2021 Research and Application camera ready due: 8 Aug. 2021
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Highlights of DSAA’2021 *A strong interdisciplinary research program spanning the areas of data science, including statistics, machine learning, computing, and analytics. *Strong Journal, Research, and Applications tracks with reproducible and open results. *Industry track session with lightning results highlighting research advances and industry’s best practices. *Special sessions on the foundations and emerging areas for data science. *Tutorials on hot topics, and hands-on tutorials *Special panel on the trends and controversies of data science and analytics. *Strong cross-domain interactions among researchers and industry and government policymakers and practitioners. *Industry and research exhibits. *Financially sponsored by IEEE CIS, proceedings by IEEE Xplore and EI indexed. *Technically supported by ACM SIGKDD and ASA.
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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.
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About DSAA’2021 The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA’2021) will provide a premier forum that brings together researchers, industry and government practitioners, as well as developers and users in statistics, computing science, and intelligence science for the exchange of the latest theoretical developments in Data Science and Analytics and the best practice for a wide range of applications. The conference invites submission of papers describing innovative research on all aspects of data science and advanced analytics as well as application-oriented papers that make significant, original, and reproducible contributions to improving the practice of data science and analytics in real-world scenarios. DSAA’2021 is a multi-track conference consisting of a Journal Track, Research Track, Application Track, and Industry Track. DSAA’2021 will also feature a peer-reviewed Poster session whose purpose is to showcase recent and early-stage research developments on topics that are of interest to students and industry/government practitioners in data science and analytics.
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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 Analytics Methods *Mathematics, Statistics for data science and analytics *Understanding data characteristics and complexities *Deep Learning *Data quality and misinformation *Bayesian learning, optimization, inference, and regularization *Infrastructures, and systems *Evaluation, explanation, visualization, and presentation *Fairness, accountability, and trustworthy Machine Learning *Survey and review The paper submission deadline for the Research Track is May 23, 2021 (no separate abstract deadline) and the acceptance notifications will be sent out by July 25, 2021.
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Applications Track The Application track solicits original, impactful, and actionable application results of Data Science and Advanced Analytics across various disciplines and domains, including business, government, healthcare and medical science, physical sciences, and social sciences. Submissions address a real problem on real-life data that is reproducible ideally through a public git repository, providing inspiring results to policymakers, 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 and software demonstrations *Social and economic impact modelling *Ethics, social issues, privacy, trust, and bias
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Industrial Track The IEEE DSAA’2021 Industry track aims to highlight the challenges of putting together production-grade software solutions to different business verticals based on data science. Our goal is to promote the debate, the exchange of ideas and/or collaborations on how to add business value to society through data science.
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Submission Submissions to the main conference including Research Track & Application Track are available from CMT: cmt3.research.microsoft.com/DSAA2021.
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

DaSSWeb – Data Science and Statistics Webinar
Tuesday, 23 February, 14:30
Speaker: Anabela Carneiro (FEP, U.Porto and CEF.UP)
Title: Decomposition Methods in Economics to Assess which Covariates Matter
Zoom link: videoconf-colibri.zoom.us/j/81745596464
Abstract: It is very common in empirical research to estimate several regression models to check the robustness of the results or to evaluate how the estimate of the coefficient of interest changes as we add a set of covariates to a baseline model. For example, in explaining the sources of the gender wage gap, very often researchers estimate multiple wage equations in order to evaluate how the gender-dummy coefficient changes as individual and job characteristics are added to the model and then attribute this difference to the new set of variables included in the model. This approach is not exempt of criticism as the order in which covariates are added is not irrelevant. In this seminar, I will present a decomposition technique, proposed by Gelbach (2016), that appeals to the omitted variable bias formula to unambiguously disentangle the contribution of each covariate to the change in the estimate of the coefficient of the variable under scrutiny. This procedure was applied to matched employer-employee data in Portugal to decompose the sources of the wage losses of displaced workers (Raposo, Portugal & Carneiro, 2021).

DaSSWeb_Anabela_Carneiro.pdf