DaSSWeb – Data Science and Statistics Webinar
Dia 16 de Outubro, 15:00h
Orador: Bernardo Marques FEP, Univ. Porto & Católica Porto Business School
Título: Using clustering ensemble to identify banking business models
Link Zoom: videoconf-colibri.zoom.us/j/89184028924

DaSSWeb_Bernardo_Marques.pdf

DaSSWeb – Data Science and Statistics Webinar
Date: September, 22, 2020, 14h30m
Speaker: Pedro Ribeiro
Title: “A gentle introduction to the world of network science”
Abstract: We will start by introducing the field of network science as an emerging pivotal interdisciplinary research topic. We will talk about the ubiquity of complex networks and how the huge increase of the available data contributed to the appearance of this field as a separate entity. We will give a brief historical perspective and revise some common terminology, along with the main associated tasks. We will then proceed with an hands-on experience on graph visualization, walking the audience through the steps needed to go from a confusing ‘hairball’ to a more polished and organized graph visual able to convey a message. We will showcase several graph filters, statistics, layouts, ranking and partition mechanisms, while interacting with the data in real time in an exploratory fashion. Finally we will give an overview of some of the work our group has done in this are, focusing on the subgraph patterns and their applicability.
Short Bio: Pedro Ribeiro is an Assistant Professor at the University of Porto, Computer Science Department and a Senior Researcher at the Center for Research in Advanced Computing Systems. He holds a PhD from the University of Porto in the field of Network Science, with a thesis centered around efficient and scalable detection of network motifs. His main research topics are in the fields of advanced algorithms and data structures, graph mining, network analysis and parallel and distributed computing. He has organized several network science related events, such as the Porto Winter School on Graph Analytics and Applications or the Porto Winter School on Network Science.
ZOOM link :
videoconf-colibri.zoom.us/j/3372995632

Special Issue on Foundations of Data Science – Machine Learning Journal
Data science is currently a very active topic with an extensive scope, both in terms of theory and applications. Machine Learning is one of its core foundational pillars. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the synergy between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science are welcome.
This special issue solicits the attention of a broad research audience. Since it brings together a variety of foundational issues and real-world best practices, it is also relevant to practitioners and engineers interested in machine learning and data science.
Accepted papers will be presented at the IEEE DSAA conference in Porto, October 2021.
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Topics of Interest
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We welcome original research papers on all aspects of data science in relation to machine learning, including the following topics:
*Machine Learning Foundations of Data Science
Auto-ML
Fusion of information from disparate sources
Feature engineering, Feature embedding and data preprocessing
Learning from network data
Learning from data with domain knowledge
Reinforcement learning
Evaluation of Data Science systems
Risk analysis
Causality, learning causal models
Multiple inputs and outputs: multi-instance, multi-label, multi-target
Semi-supervised and weakly supervised learning
Data streaming and online learning
Deep Learning
*Emerging Applications
Autonomous systems
Analysis of Evolving Social Networks
Embedding methods for Graph Mining
Online Recommender Systems
Augmented Reality, Computer Vision
Real-Time Anomaly, Failure, image manipulation and fake detection
*Human Centric Data Science
Privacy preserving, Ethics, Transparency
Fairness, Explainability, and Algorithm Bias
Accountability and responsibility
Reproducibility, replicability and retractability
Green Data Sciences
*Infrastructures
IoT data analytics and Big Data
Large-scale processing and distributed/parallel computing;
Cloud computing
*Data Science for the Next Digital Frontier
in: Telecommunications and 5G
Retail,
Green Transportation
Finance, Blockchains, Cryptocurrencies
Manufacturing, Predictive Maintenance, Industry 4.0
Energy, Smart Grids, Renewable energies
Climate change and sustainable environment
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning journal’s mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
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Submission Instructions
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Submit manuscripts to: MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/journal/10994
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
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Key Dates
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Continuous submission/review process
Cutoff dates: 30 September, 30 December and 1st March
Last paper submission deadline: 1 March 2021
Paper acceptance: 1 June 2021
Camera-ready: 15 June 2021
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Guest Editors
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Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada

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