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Data Science and Big Data: brothers, yes, twins, no

Invited Speaker (ENIAC): André Carlos Ponce de Leon Ferreira de Carvalho

Tuesday, October 3, 2017, 8:30 am

With the recent great expansion in data generation and the growing importance of exploring the knowledge contained in these data, Data Science is one of the fastest growing area of Exact Sciences. Large companies, like Amazon, Apple, Disney, Facebook, Google and Microsoft are hiring a large number of scientists, engineers and statisticians to work in this area. The ability to acquire, store and transmit data from the most diverse human activities, in the public and private sectors, has grown exponentially. This is generating massive volumes of data. These massive volumes of data, handled by Big Data technologies, come from a variety of sources and therefore have a wide variety of structures, ranging from traditional attribute-value tables to videos and messages on social networks. Analyzing these massive volumes of data can generate valuable information for decision making, enabling the extraction of new and useful knowledge. The difficulty of this analysis by traditional data analysis techniques has led to the development of new techniques, expanding the area of Data Science. This talk will present the main aspects, challenges and applications of Big Data and Data Science.

Ciência de Dados e Big Data: irmãos, sim, gêmeos, não

Com a grande expansão recente na geração de dados e a crescente importância de exploração do conhecimento contido nesses dados, Ciência de Dados é uma das áreas que mais cresce nas Ciências Exatas. Grandes empresas, como Amazon, Apple, Disney, Facebook, Google e Microsoft estão contratando um grande número de cientistas, engenheiros e estatísticos para trabalhar nesta área. A capacidade de adquirir, armazenar e transmitir dados sobre as mais diferentes atividades humanas, nos setores públicos e privados, tem crescido de forma exponencial. Isso está gerando volumes massivos de dados. Esses volumes massivos de dados, tratados por tecnologias para Big Data, são provenientes de diferentes fontes e, por isso, apresentam uma grande variedade de estruturas, desde as tradicionais tabelas atributo-valor, a vídeos e mensagens em redes sociais. A análise desses volumes massivos de dados pode gerar informações preciosas para a tomada de decisão, permitindo a extração de conhecimentos novos e úteis. A dificuldade dessa análise por técnicas tradicionais de análise de dados tem levado ao desenvolvimento de novas técnicas, dando impulso à área de Ciência de Dados. Esta palestra apresentará os principais aspectos, desafios e aplicações de Big Data e Ciência de Dados.


Short-Bio: É professor titular do Instituto de Ciências Matemáticas e de Computação, Departamento de Ciências de Computação, da Universidade de São Paulo (USP), campus São Carlos, onde também é diretor do centro de Aprendizado de Máquina em Análise de Dados, e bolsista de Produtividade em Pesquisa 1A do CNPq. Possui graduação (1987) e mestrado em Ciências da Computação (1990) pela Universidade Federal de Pernambuco, e doutorado em Electronic Engineering pela University of Kent at Canterbury (1994). Tem mais de 300 publicações em Ciência de Dados, Aprendizado de Máquina e Mineração de Dados, incluindo 10 best papers, em congressos organizados pela ACM, IEEE e SBC. Já orientou mais de 25 teses de doutorado em diferentes universidades do Brasil e de Portugal e supervisionou cerca de 15 pós-doutorados. Faz parte do Comitê Editorial e do Comitê de Programa dos principais periódicos e congressos da área de Inteligência Artificial, Ciência de Dados, Mineração de Dados e Aprendizado de Máquina, tais como AAAI, KDD, ECML/PKDD, IJCAI e SDM. É revisor ad hoc de várias fundações nacionais e internacionais de apoio à pesquisa. É vice-diretor do Centro de Ciências Matemáticas Aplicadas a Industria. Seus principais interesses de pesquisa são Aprendizado de Máquina (Machine Learning), Mineração de Dados (Data Mining) e Ciência de Dados (Data Science), atuando principalmente nos seguintes temas: detecção de novidades, meta-aprendizado, pré-processamento de dados e metaheurísticas, com aplicações em Bioinformática, Engenharia, Finanças, Medicina e Meio Ambiente.


On calibration in machine learning

Invited Speaker (BRACIS): Peter Flach

Tuesday, October 3, 2017, 2:00 pm

Classifier calibration is concerned with the scale on which a classifier's scores are expressed. The advantage of calibrating these scores to a known, domain-independent scale is that the decision rule then also takes a domain-independent form and does not have to be learned. The best-known example of this occurs when the classifier's scores approximate, in a precise sense, the posterior probability over the classes; the main advantage of this is that the optimal decision rule is to predict the class that minimises expected cost averaged over all possible true classes. An alternative view of calibration holds that a well-calibrated classifier calculates the cost parameters under which the expected cost for the instance under consideration is the same regardless of the predicted class; the main advantage of this more general view is that it can be adapted to different loss measures. In this talk I will review recent work in classifier calibration, including the use of precision-recall-gain curves to obtain scores calibrated for the F-score, and Beta-calibration which is suited for classifiers scoring on a bounded scale.


M Kull, T Silva Filho, P Flach. Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. Artificial Intelligence and Statistics, pp. 623-631, 2017.

P Flach. Classifier Calibration. Encyclopedia of Machine Learning and Data Mining, 2016.

P Flach, M Kull. Precision-recall-gain curves: PR analysis done right. Advances in Neural Information Processing Systems, pp. 838-846, 2015.


Short Bio: Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. An internationally leading researcher in the areas of mining highly structured data and the evaluation and improvement of machine learning models using ROC analysis, he has also published on the logic and philosophy of machine learning, and on the combination of logic and probability. He is author of Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012). Professor Flach is the Editor-in-Chief of the Machine Learning journal, one of the two top journals in the field that has been published for over 25 years by Kluwer and now Springer. He was Programme Co-Chair of the 1999 International Conference on Inductive Logic Programming, the 2001 European Conference on Machine Learning, the 2009 ACM Conference on Knowledge Discovery and Data Mining, and the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases in Bristol.


Massive Online Analytics for the Internet of Things (IoT)

Invited Speaker (BRACIS): Albert Bifet

Wednesday, October 4, 2017, 8:30 am

Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.


Short-Bio: Albert Bifet is Associate Professor at Telecom ParisTech and Honorary Research Associate at the WEKA Machine Learning Group at University of Waikato. Previously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2015, 2014, 2013, 2012), and ACM SAC Data Streams Track (2017, 2016, 2015, 2014, 2013, 2012).


Evolutionary algorithms, shared assets, and sustainable global freight transport

Invited Speaker (BRACIS): David Corne

Wednesday, October 4, 2017, 2:00 pm

Transport accounts for 20% of global final energy consumption, and road-freight is a rapidly growing component of that, especially in developing countries. Recently I have been working on a project, led by the World Business Council for Sustainable Development (WBCSD --, aimed at de-carbonising road freight transport. The project's goals are to deliver approaches that (a) lead to substantial reductions in emissions, and (b) are acheivable and sensible for businesses to adopt. In this talk I will focus on one of the major findings of the project, which centres on the concept of asset-sharing between freight operators -- for example, company A making use of trucks from company B's fleet, or company C sharing space in company D's depot. In a nutshell, such asset sharing can lead to cost and emissions savings of, typically, 20%, but occasionally far greater (e.g. 80%) for all the businesses involved. Another finding of the project concerns the effects of delivery windows -- relaxing tght delivery windows, e.g. from 1 hour to 5 hours, can lead to cost and emissions savings of 25%. Such potential savings are very significant, and could play a major role in ensuring that countries meet their emissions targets. I will explain why these findings, and their realisation in practice, are underpinned by advanced logistics optimisation algorithms, especially: evolutionary multi-objective optimization. After discussing the algorithms, I will describe a new project, involving some major freight operators, called 'Freight Share Lab', which aims to share and facilitate these ideas at scale.


Short-Bio: David Corne is a Professor of Computer Science at the School of Mathematical & Computer Sciences, Heriot-Watt University (HWU), Edinburgh. He leads the Intelligent Systems Lab and is the CS Director of Enterprise, Impact and Innovation. His research interests involve evolutionary computing, optimization, multiobjective optimization, machine learning and data mining. His current focus is on working with industry to apply state of the art optimization and data science techniques to global sustainability goals, particularly in the areas of energy, transport and climate. He also works with others in exploiting his research from advising start-ups through to major industry-supported projects. He is /was a member of the Editorial Board of several journals including Evolutionary Computation (MIT Press), Theoretical Computer Science (Elsevier), Natural Computing (Springer/Elsevier), Applied Intelligence (Elsevier), Applied Soft Computing (Elsevier), IEEE Transactions on Evolutionary Computation (IEEE), Journal of Scheduling (founding co-editor), International Journal of Hybrid Intelligent Systems (IOS Press), International Journal of Metaheuristics (Inderscience), International Journal of Bioinformatics Research and Applications (Inderscience), Computational Intelligence Magazine (IEEE). He has been involved in the steering comittee of several events such as PPSN (Parallel Problem Solving from Nature, since 2002) and EMO (Evolutionary Multiobjective Optimization, since 2001).


Talk Cancelled

Invited Speaker (STIL): Andreas Vlachos.

The speaker Andreas Vlachos will unfortunately not be able to attend STIL 2017 due to more urgent health-related procedures that conflict with the conference's schedule.