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About

About

I am a lecturer in the Department of Computer Science, School of Sciences of University of Porto, Portugal. I obtained a B.Sc. degree in Computer Science from State University of Rio de Janeiro, Brazil, in 1985, and an M.Sc. degree in the Systems Engineering and Computer Science department of Federal University of Rio de Janeiro, Brazil, in 1988. My Ph.D. degree was obtained from Bristol University, UK, in 1995. In 1998, I started as a lecturer in the Department of Systems Engineering and Computer Science of COPPE, an institution for postgraduate studies in Engineering, at Federal University of Rio de Janeiro, where I taught courses on Operating Systems, Concurrent Programming and Topics on High Performance Computing, at M.Sc. and Ph.D. levels, and Artificial Intelligence and Logic Programming, at undergraduate level. In Februrary 2007 I moved to Portugal where I am now located. During the periods between October 2001 and December 2002, April 2004 and March 2005, Aug 2010 and Feb 2011, and Oct 2014 and Mar 2015, I worked as a visiting researcher at University of Wisconsin-Madison, USA, in the department of Biostatistics and Medical Informatics, and at the Radiology Department of the School of Sciences and Public Health. During these periods, I worked for machine learning projects funded by NSF, DARPA and American Air Force (projects COLLEAGUE, EELD and EAGLE), and NLM (Project ABLe) and started to work with applications that demanded a huge amount of resources. At this time, I had the opportunity to work with the Condor team, and to largely use the Condor resource manager to run experiments. My main research areas are Logic programming, Inductive Logic Programming, and Parallel Logic Programming systems. I served as Program Comittee member of several workshops and conferences in these areas. I supervised several M.Sc. and Ph.D. students in these areas. I have more than 80 publications in conferences and journals. I also participated or was the principal investigator of several projects funded by CNPq (Brazil), FCT (Portugal) and the EU. I am a member of the EELA (E-science grid facility for Europe and Latin America) initiative, whose main objective is to promote and maintain the infrastructure of hardware and software between Europe and Latin America. Currently, I have been working on machine learning techniques based on Inductive Logic programming, but still using parallelzation and grid environments to be able to perform machine learning experiments.

Interest
Topics
Details

Details

  • Name

    Inês Dutra
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st January 2009
005
Publications

2018

On applying probabilistic logic programming to breast cancer data

Authors
Côrte Real, J; Dutra, I; Rocha, R;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Medical data is particularly interesting as a subject for relational data mining due to the complex interactions which exist between different entities. Furthermore, the ambiguity of medical imaging causes interpretation to be complex and error-prone, and thus particularly amenable to improvement through automated decision support. Probabilistic Inductive Logic Programming (PILP) is a particularly well-suited tool for this task, since it makes it possible to combine the relational nature of this field with the ambiguity inherent in human interpretation of medical imaging. This work presents a PILP setting for breast cancer data, where several clinical and demographic variables were collected retrospectively, and new probabilistic variables and rules reflecting domain knowledge were introduced. A PILP predictive model was built automatically from this data and experiments show that it can not only match the predictions of a team of experts in the area, but also consistently reduce the error rate of malignancy prediction, when compared to other non-relational techniques. © Springer International Publishing AG, part of Springer Nature 2018.

2018

atSNPInfrastructure, a Case Study for Searching Billions of Records While Providing Significant Cost Savings over Cloud Providers

Authors
Harrison, C; Keles, S; Hudson, R; Shin, S; Dutra, I;

Publication
2018 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPS Workshops 2018, Vancouver, BC, Canada, May 21-25, 2018

Abstract
We explore the feasibility of a database storage engine housing up to 307 billion genetic Single Nucleotide Polymorphisms (SNP) for online access. We evaluate database storage engines and implement a solution utilizing factors such as dataset size, information gain, cost and hardware constraints. Our solution provides a full feature functional model for scalable storage and query-ability for researchers exploring the SNP's in the human genome. We address the scalability problem by building physical infrastructure and comparing final costs to a major cloud provider. © 2018 IEEE.

2017

Managing Diabetes: Counselling Supported by User Data in a Mobile Platform

Authors
Machado, D; Dutra, I; Brandão, P; Costa, VS;

Publication
Proceedings of the Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR 2017 hosted by International Joint Conference on Rules and Reasoning 2017 (RuleML+RR 2017), London, UK, July 11-15, 2017.

Abstract
Diabetes management is a complex problem. The patient needs to monitor several parameters in order to react in the most appropriate way. Different situations require the diabetic to understand and evaluate different rules. The main source of knowledge for those rules arises from medical practice and is usually transmitted through medical appointments. Given this initial advice, most patient are on a continuous process of managing the disease, toward achieving the best possible quality of life. Motivated by recent aadvances in diabetes monitoring devices, we introduce a diabetes support system designed to accompany the user, advising her and providing early guidance to avoid some of the many complications associated with diabetes. To accomplish this goal, we incorporate standard medical protocols, advice and directives in a Rule Based System (RBS). This RBS which we call Advice Rule Based System (ARBS) is capable of advising and uncovering possible causes for different occurrences. We believe that this solution is not only beneficial to the patient, but may also may be of use to the clinitians advising the patient. The device has continuous contact with the patient, thus it can provide early response if/where needed, Moreover, the system can provide useful data, that an authorized medical expert can use while prescribing a particular treatment, or even when investingating this health problem. We have started to add data-mining algorithms and methods, to uncover hidden behavioural patterns that may lead to crisis situations. Ultimately, through refining the rule systems base don human and machine learning, our approach has the potential for personalising the system according to the habits and phenotype of its user. The system is to be incorporated in a currently developed diabetes management application for Android.

2017

Managing diabetes: Pattern discovery and counselling supported by user data in a mobile platform

Authors
Machado, D; Paiva, T; Dutra, I; Costa, VS; Brandao, P;

Publication
2017 IEEE Symposium on Computers and Communications, ISCC 2017, Heraklion, Greece, July 3-6, 2017

Abstract
Diabetes management is a complex and a sensible problem as each diabetic is a unique case with particular needs. The optimal solution would be a constant monitoring of the diabetic's values and automatically acting accordingly. We propose an approach that guides the user and analyses the data gathered to give individual advice. By using data mining algorithms and methods, we uncover hidden behaviour patterns that may lead to crisis situations. These patterns can then be transformed into logical rules, able to trigger in a particular context, and advise the user. We believe that this solution, is not only beneficial for the diabetic, but also for the doctor accompanying the situation. The advice and rules are useful input that the medical expert can use while prescribing a particular treatment. During the data gathering phase, when the number of records is not enough to attain useful conclusions, a base set of logical rules, defined from medical protocols, directives and/or advice, is responsible for advise and guiding the user. The proposed system will accompany the user at start with generic advice, and with constant learning, advise the user more specifically. We discuss this approach describing the architecture of the system, its base rules and data mining component. The system is to be incorporated in a currently developed diabetes management application for Android. © 2017 IEEE.

2017

Estimation-based search space traversal in PILP environments

Authors
Côrte Real, J; Dutra, I; Rocha, R;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Probabilistic Inductive Logic Programming (PILP) systems extend ILP by allowing the world to be represented using probabilistic facts and rules, and by learning probabilistic theories that can be used to make predictions. However, such systems can be inefficient both due to the large search space inherited from the ILP algorithm and to the probabilistic evaluation needed whenever a new candidate theory is generated. To address the latter issue, this work introduces probability estimators aimed at improving the efficiency of PILP systems. An estimator can avoid the computational cost of probabilistic theory evaluation by providing an estimate of the value of the combination of two subtheories. Experiments are performed on three real-world datasets of different areas (biology, medical and web-based) and show that, by reducing the number of theories to be evaluated, the estimators can significantly shorten the execution time without losing probabilistic accuracy. © Springer International Publishing AG 2017.

Supervised
thesis

2017

Improving the search for multi-relational concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP

2017

Execução e Gestão de Aplicações Conteinerizadas

Author
Diogo Cristiano dos Santos Reis

Institution
UP-FCUP

2017

Weighted Multiple Kernel Learning for Breast Cancer Diagnosis applied to Mammograms

Author
Tiago André Guedes Santos

Institution
UP-FCUP

2016

0

Author
Rosária Maria Afonso Rodrigues de Melo

Institution
UP-FCNA

2016

Implementação e Avaliação do Algoritmo MCTS-UCT para o jogo Chinese Checkers

Author
Jhonny Manuel Campos Moreira

Institution
UP-FCUP