Ricardo Bezerra de Andrade e Silva

Possui graduação em Bacharelado Em Computação pela Universidade Federal do Ceará (1997) e doutorado em Machine Learning - Carnegie Mellon University (2005). Tem experiência na área de Ciência da Computação, com ênfase em Ciência da Computação, atuando principalmente nos seguintes temas: aprendizado de maquina, causalidade, modelos de variáveis latentes, estatística Bayesiana.

Informações coletadas do Lattes em 10/06/2025

Acadêmico

Formação acadêmica

Mestrado em andamento em Ciências da Computação

1998 - Atual

Universidade Federal de Pernambuco
Título: Sistemas Híbridos de Funções de Base Local
Orientador: Teresa Bernarda Ludermir
Bolsista do(a): Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, Brasil. Palavras-chave: aprendizado de máquina; mineração de dados; redes neurais; classificação.Grande área: Ciências Exatas e da TerraSetores de atividade: Informática.

Doutorado em Machine Learning

2000 - 2005

Carnegie Mellon University
Título: Automatic Discovery of Latent Variable Models
Orientador: Richard Scheines
Coorientador: Clark Glymour. Bolsista do(a): Varios (Microsoft, NASA, Siebel), VARIOS, Estados Unidos.

Graduação em Bacharelado Em Computação

1994 - 1997

Universidade Federal do Ceará
Bolsista do(a): Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES, Brasil.

Pós-doutorado

2007 - 2008

Pós-Doutorado. , University of Cambridge, CAM, Inglaterra. , Bolsista do(a): Engineering Research Council, EPSRC, Grã-Bretanha.

2005 - 2007

Pós-Doutorado. , University College London, UCL, Inglaterra. , Bolsista do(a): Gatsby Charitable Foundation, GCF, Grã-Bretanha.

Idiomas

Inglês

Compreende Bem, Fala Bem, Lê Bem, Escreve Bem.

Áreas de atuação

Grande área: Ciências Exatas e da Terra / Área: Ciência da Computação.

Grande área: Ciências Exatas e da Terra / Área: Probabilidade e Estatística / Subárea: Estatística.

Orientou

Bryan Feeney

Advances in Topic Models; Início: 2014; Tese (Doutorado em PhD Statistics) - University College London, Xerox Research Centre; (Orientador);

Rafael Carmo

Structured Stochastic Processes in Dynamic Latent Variable Models; Início: 2013; Tese (Doutorado em PhD Statistics) - University College London, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; (Orientador);

Samuel Parsons

Computational and Statistical Trade-offs in Estimating Latent Variable Models; Início: 2011; Tese (Doutorado em PhD Statistics) - University College London, Engineering Research Council; (Orientador);

Hiroaki Imai

Using Twitter Data for Sentiment Analysis of the Scottish Referendum; Início: 2014; Trabalho de Conclusão de Curso (Graduação em BSc Statistics) - University College London; (Orientador);

Nithan Mohindra

Estimating causal effects under confounding variables; 2013; Dissertação (Mestrado em MSci Natural Sciences) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Yin Ng

Copula processes for volatily modelling; 2013; Dissertação (Mestrado em MSc Machine Learning) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Xi Wu

Advances in bandit models; 2012; Dissertação (Mestrado em MSc Statistics) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Samuel Parsons

Optimising trade execution with reinforcement learning; 2011; Dissertação (Mestrado em MRes) - University College London, Engineering Research Council; Orientador: Ricardo Bezerra de Andrade e Silva;

Han Zheng

Mixture of Gaussian processes for measurement error problems; 2010; Dissertação (Mestrado em MSc Statistics) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Jian Wang

MCMC methods for copula models of marginal independence; 2010; Dissertação (Mestrado em MSc Statistics) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Xiaoyun Qi

On-line approaches for relational classification; 2010; Dissertação (Mestrado em MSc Statistics) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Alexis Kakoullis

?State-space methods for statistical arbitrage; 2010; Dissertação (Mestrado em MRes) - University College London, Engineering Research Council; Orientador: Ricardo Bezerra de Andrade e Silva;

Xinmei Liang

Bayesian analysis of relational data; 2009; Dissertação (Mestrado em MSc Statistics) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Jacobo Roa Vincens

Bayesian and Adversarial Inverse Reinforcement Learning on Latent Spaces of Limit Order Books; 2024; Tese (Doutorado em Doutorado em Estatística) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Pawel Chilinski

Cumulative Distribution Functions As The Foundation For Probabilistic Models; 2022; Tese (Doutorado em Doutorado em Estatística) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Yin Cheng Ng

Learning Patterns from Sequential and Network Data Using Probabilistic Models; 2019; Tese (Doutorado em Doutorado em Estatística) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Jean-Baptiste Regli

Probabilistic Methods for High Dimensional Signal Processing; 2018; Tese (Doutorado em Doutorado em Estatística) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Rafael Carmo

Models and Algorithms for Episodic Time Series; 2018; Tese (Doutorado em Doutorado em Estatística) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Alex Gibberd

Regularised Inference for Changepoint and Dependency Analysis in Non-Stationary Processes; 2017; Tese (Doutorado em Doutorado em Estatística) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Samuel Parsons

Approximation methods for latent variable models; 2015; Tese (Doutorado em Doutorado em Estatística) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Yi-Da Chiu

Exploratory Studies For Gaussian Process Structural Equation Models; 2014; Tese (Doutorado em PhD Statistics) - University College London, ; Orientador: Ricardo Bezerra de Andrade e Silva;

Alfredo Kalaitzis

2012; University College London, Engineering Research Council; Ricardo Bezerra de Andrade e Silva;

Simon Byrne

Message passing in graphical models; 2008; Monografia; (Aperfeiçoamento/Especialização em Part III Mathematical Tripos) - University of Cambridge; Orientador: Ricardo Bezerra de Andrade e Silva;

Lizi Zheng

Methods for predicting links in networks; 2014; Trabalho de Conclusão de Curso; (Graduação em BSc Statistics) - University College London; Orientador: Ricardo Bezerra de Andrade e Silva;

David Willan

Data imputation with applications to large surveys; 2014; Trabalho de Conclusão de Curso; (Graduação em BSc Statistics) - University College London; Orientador: Ricardo Bezerra de Andrade e Silva;

Benjamin Gordon

Clustering for ordinal data; 2013; Trabalho de Conclusão de Curso; (Graduação em BSc Statistics) - University College London; Orientador: Ricardo Bezerra de Andrade e Silva;

Nithan Mohindra

Multiple comparisons in fMRI - Is the multilevel model the solution?; 2012; Trabalho de Conclusão de Curso; (Graduação em BSc Natural Sciences) - University College London; Orientador: Ricardo Bezerra de Andrade e Silva;

Wei Xue

Bayesian sparse regression models; 2011; Trabalho de Conclusão de Curso; (Graduação em BSc Statistics) - University College London; Orientador: Ricardo Bezerra de Andrade e Silva;

Produções bibliográficas

CABRERA-ARNAU, CARMEN ; ZHONG, CHEN ; BATTY, MICHAEL ; Silva, Ricardo ; KANG, SOONG MOON . Inferring urban polycentricity from the variability in human mobility patterns. Scientific Reports , v. 13, p. 5751, 2023.
PAGANI, ALESSIO ; WEI, ZHUANGKUN ; Silva, Ricardo ; GUO, WEISI . Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics. ACM Transactions on Internet Technology , v. 22, p. 1-18, 2022.
WHITAKER, GAVIN A. ; Silva, Ricardo ; EDWARDS, DANIEL ; KOSMIDIS, IOANNIS . A Bayesian Approach for Determining Player Abilities in Football. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS , v. 70, p. 174-201, 2021.
BARTLETT, THOMAS E. ; KOSMIDIS, IOANNIS ; Silva, Ricardo . Two-way sparsity for time-varying networks with applications in genomics. Annals of Applied Statistics , v. 15, p. 856, 2021.
DIXON, WILLIAM G. ; BEUKENHORST, ANNA L. ; YIMER, BELAY B. ; COOK, LOUISE ; GASPARRINI, ANTONIO ; EL-HAY, TAL ; HELLMAN, BRUCE ; JAMES, BEN ; VICEDO-CABRERA, ANA M. ; MACLURE, MALCOLM ; Silva, Ricardo ; AINSWORTH, JOHN ; PISANIELLO, HUAI LENG ; HOUSE, THOMAS ; LUNT, MARK ; GAMBLE, CAROLYN ; SANDERS, CAROLINE ; SCHULTZ, DAVID M. ; SERGEANT, JAMIE C. ; MCBETH, JOHN . How the weather affects the pain of citizen scientists using a smartphone app. Npj Digital Medicine , v. 2, p. 105, 2019.
COUTROT, ANTOINE ; Silva, Ricardo ; MANLEY, ED ; DE COTHI, WILL ; SAMI, SABER ; BOHBOT, VÉRONIQUE D. ; WIENER, JAN M. ; HÖLSCHER, CHRISTOPH ; DALTON, RUTH C. ; HORNBERGER, MICHAEL ; SPIERS, HUGO J. . Global Determinants of Navigation Ability. CURRENT BIOLOGY , v. 28, p. 2861-2866.e4, 2018.
WHITAKER, GAVIN A. ; Silva, Ricardo ; EDWARDS, DANIEL . Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach. Big Data , v. 6, p. 271-290, 2018.
Silva, Ricardo ; SHIMIZU, S. . Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. JOURNAL OF MACHINE LEARNING RESEARCH , v. 18, p. 1, 2017.
Silva, Ricardo ; EVANS, R. . Causal Inference through a Witness Protection Program. JOURNAL OF MACHINE LEARNING RESEARCH , v. 17, p. 1, 2016.
Silva, Ricardo ; KANG, S. ; AIROLDI, EDOARDO M. . Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (ONLINE) , v. 112, p. 5643, 2015.
Silva, Ricardo ; KALAITZIS, ALFREDO . Bayesian inference via projections. STATISTICS AND COMPUTING , v. 25, p. 739-753, 2015.
SANBORN, ADAM N. ; Silva, Ricardo . Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning. Journal of Mathematical Psychology (Print) , v. 57, p. 94-106, 2013.
Silva, Ricardo ; HELLER, KATHERINE ; GHAHRAMANI, ZOUBIN ; AIROLDI, EDOARDO M. . Ranking relations using analogies in biological and information networks. The Annals of Applied Statistics , v. 4, p. 615-644, 2010.
Silva, Ricardo ; GHAHRAMANI, Z. . The hidden life of latent variables: Bayesian learning with mixed graph models. Journal of Machine Learning Research , v. 10, p. 1187, 2009.
Silva, Ricardo ; SCHEINES, R. ; GLYMOUR, C. ; SPIRTES, P. . Learning the structure of linear latent variable models.. Journal of Machine Learning Research , v. 7, p. 191, 2006.
MOODY, J. ; Silva, Ricardo ; VANDERWAART, J. ; RAMSEY, J. ; GLYMOUR, C. . Classification and filtering of spectra: A case study in mineralogy. Intelligent Data Analysis (Print) , v. 6, p. 517, 2002.
SILVA, R. B. A. ; LUDERMIR, T. B. . Hybrid systems of local basis functions. Intelligent Data Analysis (Print) , v. 5, p. 227, 2001.
Silva, Ricardo . A MCMC approach for learning the structure of Gaussian acyclic directed mixed graphs. In: Paolo Giudici; Salvatore Ingrassia; Maurizio Vichi. (Org.). tatistical Models for Data Analysis. 1ed.: Springer, 2013, v. , p. 1-.
Silva, Ricardo . Measuring latent causal structure. In: Phyllis McKay Illari; Federica Russo ; Jon Williamson. (Org.). Causality in the Sciences. 1ed.: Oxford University Press, 2011, v. , p. 673-.
Silva, Ricardo . Causality. In: Claude Sammut; Geoffrey Webb. (Org.). Springer Reference Encyclopedia of Machine Learning. 1ed.: , 2010, v. , p. 1-.
WATSON, D. ; PENN, J. ; GUNDERSON, L. M. ; BRAVO-HERMSDORFF, G. ; MASTOURI, A. ; Silva, Ricardo . Bounding Causal Effects with Leaky Instruments. In: Uncertainty in Artificial Intelligence, 2024. Proceedings of the Uncertainty in Artificial Intelligence Conference.
GULTCHIN, L. ; GUO, S. ; MALEK, A. ; CHIAPPA, S. ; Silva, Ricardo . Pragmatic Fairness: Developing Policies with Outcome Disparity Control. In: Causal Learning and Reasoning, 2024. Proceedings of the Causal Learning and Reasoning Conference.
PADH, K. ; ZEITLER, J. ; WATSON, D. ; KUSNER, M. ; Silva, Ricardo ; KILBERTUS, N. . Stochastic Causal Programming for Bounding Treatment Effects. In: Causal Learning and Reasoning, 2023. Proceedings of the Causal Learning and Reasoning Conference.
BRAVO-HERMSDORFF, G. ; WATSON, D. ; YU, J. ; ZEITLER, J. ; Silva, Ricardo . Intervention Generalization: A View from Factor Graph Models. In: Neural Information Processing Systems, 2023. Advances in Neural Information Processing Systems.
KADDOUR, J. ; LIU, L. ; KUSNER, M. ; Silva, Ricardo . When Do Flat Minima Optimizers Work?. In: Neural Information Processing Systems, 2022. Advances in Neural Information Processing Systems.
WATSON, D. ; Silva, Ricardo . Causal discovery under a confounder blanket. In: Uncertainty in Artificial Intelligence, 2022. Proceedings of the Uncertainty in Artificial Intelligence Conference.
ZHU, Y. ; GULTCHIN, L. ; GRETTON, A. ; KUSNER, M. ; Silva, Ricardo . Causal inference with treatment measurement error: a nonparametric instrumental variable approach. In: Uncertainty in Artificial Intelligence, 2022. Proceedings of the Uncertainty in Artificial Intelligence Conference.
MASTOURI, A. ; ZHU, Y. ; GULTCHIN, L. ; KORBA, A. ; Silva, Ricardo ; KUSNER, M. ; GRETTON, A. ; MUANDET, K. . Proximal causal learning with kernels: Two-stage estimation and moment restriction. In: International Conference in Machine Learning, 2021. Proceedings of the International Conference in Machine Learning.
KADDOUR, J. ; ZHU, Y. ; LIU, Q. ; KUSNER, M. ; Silva, Ricardo . Causal Effect Inference for Structured Treatments. In: Neural Information Processing Systems, 2021. Advances in Neural Information Processing Systems.
GULTCHIN, L. ; WATSON, D. ; KUSNER, M. ; Silva, Ricardo . Operationalizing Complex Causes: A Pragmatic View of Mediation. In: International Conference on Machine Learning, 2021. Proceedings of the International Conference on Machine Learning.
GULTCHIN, L. ; KUSNER, M. ; KANADE, V. ; Silva, Ricardo . Differentiable Causal Backdoor Discovery. In: Artificial Intelligence and Statistics, 2020. Proceedings of the Artificial Intelligence and Statistics Conference.
SAENGKYONGAM, S. ; Silva, Ricardo . Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders. In: Uncertainty in Artificial Intelligence, 2020. Proceedings of the Uncertainty in Artificial Intelligence Conference.
CHILLINSKI, P. ; Silva, Ricardo . Neural likelihoods via cumulative distribution functions. In: Uncertainty in Artificial Intelligence, 2020. Proceedings of the Uncertainty in Artificial Intelligence Conference.
KILBERTUS, N. ; KUSNER, M. ; Silva, Ricardo . A Class of Algorithms for General Instrumental Variable Models. In: Neural Information Processing Systems, 2020. Advances in Neural Information Processing Systems.
KUSNER, M. ; RUSSELL, C. ; LOFTUS, J. ; Silva, Ricardo . Making Decisions that Reduce Discriminatory Impacts. In: International Conference in Machine Learning, 2019. Procesdings of the International Conference in Machine Learning.
KILBERTUS, N. ; BALL, P. ; KUSNER, M. ; WELLER, A. ; Silva, Ricardo . The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. In: Uncertainty in Artificial Intelligence, 2019. Proceedings of the Uncertainty in Artificial Intelligence Conference.
Ng, Y.-C. ; COLOMBO, N. ; Silva, Ricardo . Bayesian semi-supervised learning with graph Gaussian processes. In: Neural Information Processing Systems, 2018. Advances in Neural Information Processing Systems.
KUSNER, M. ; LOFTUS, J. ; RUSSELL, C. ; Silva, Ricardo . Counterfactual Fairness. In: Neural Information Processing Systems, 2017. Advances in Neural Information Processing Systems.
RUSSELL, C. ; KUSNER, M. ; LOFTUS, J. ; Silva, Ricardo . When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. In: Neural Information Processing Systems, 2017. Advances in Neural Information Processing Systems.
COLOMBO, N. ; Silva, Ricardo ; KANG, S. . Tomography of the London Underground: a Scalable Model for Origin-Destination Data. In: Neural Information Processing Systems, 2017. Advances in Neural Information Processing Systems.
Silva, Ricardo . Observational-interventional priors for dose-response learning. In: Neural Information Processing Systems, 2016. Advances in Neural Information Processing Systems.
Ng, Y.-C. ; CHILLINSKI, P. ; Silva, Ricardo . Scaling factorial hidden markov models: Stochastic variational inference without messages. In: Neural Information Processing Systems, 2016. Advances in Neural Information Processing Systems.
Silva, Ricardo . Bayesian inference in cumulative distribution fields. In: 12th Brazilian Meeting on Bayesian Statistics, 2014, Atibaia. Proceedings of the 12th Brazilian Meeting on Bayesian Statistics, 2014.
Silva, Ricardo ; EVANS, R. . Causal inference through a witness protection program. In: Neural Information Processing Systems (NIPS), 2014, Montreal. Advances in Neural Information Processing Systems, 2014.
KALAITZIS, A. ; Silva, Ricardo . Flexible sampling of discrete data correlations without the marginal distributions. In: Neural Information Processing Systems (NIPS), 2013, Lake Tahoe. Advances in Neural Information Processing Systems, 2013.
Silva, Ricardo . Latent composite likelihood learning for the structured canonical correlation model. In: 28th Conference on Uncertainty in Artificial Intelligence, 2012, Avalon. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, 2012.
Silva, Ricardo ; BLUNDELL, C. ; TEH, Y. W. . Mixed cumulative distribution networks. In: Artificial Intelligence and Statistic, 2011, Clearwater. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics,, 2010.
Silva, Ricardo . Thinning measurement models and questionnaire design. In: Neural Information Processing Systems (NIPS), 2011, Granada. Advances in Neural Information Processing Systems, 2011.
Silva, Ricardo ; GRAMACY, R. B. . Gaussian process structural equation models with latent variables. In: 26th Conference on Uncertainty on Artificial Intelligence, 2010, Avalon. Proceedings of the 26th Conference on Uncertainty on Artificial Intelligence, 2010.
Silva, Ricardo ; ZHANG, J. . Discussion of ``Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables. In: Artificial Intelligence and Statistics, 2010, Clearwater. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 2010.
Silva, Ricardo ; GHAHRAMANI, Z. . Factorial mixture of Gaussians and the marginal independence model. In: Artificial Intelligence and Statistics, 2009, Avalon. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics,, 2009.
Silva, Ricardo ; GRAMACY, R. B. . MCMC methods for Bayesian mixtures of copulas. In: Artificial Intelligence and Statistics, 2009, Clearwater. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics,, 2009.
Silva, Ricardo ; SANBORN, A. . Belief propagation and locally Bayesian learning. In: 31st Annual Conference of the Cognitive Science Society, 2009, Amsterdam. Proceedings of the 31st Annual Conference of the Cognitive Science Society, 2009.
Silva, Ricardo ; HELLER, K. ; GHAHRAMANI, Z. . Analogical reasoning with relational Bayesian sets. In: 11th International Conference on Artificial Intelligence and Statistics,, 2007, San Juan. Proceedings of the 11th International Conference on Artificial Intelligence and Statistics,, 2007.
Silva, Ricardo ; CHU, W. ; GHAHRAMANI, Z. . Hidden common cause relations in relational learning.. In: Neural Information Processing Systems (NIPS), 2007, Vancouver. Advances in Neural Information Processing Systems, 2007.
Silva, Ricardo ; SCHEINES, R. . Bayesian learning of measurement and structural models. In: International Conference in Machine Learning (ICML), 2006, Pittsburgh. Proceedings of the International Conference in Machine Learning, 2006.
Silva, Ricardo ; GHAHRAMANI, Z. . Bayesian inference for Gaussian mixed graph models. In: 22nd Conference on Uncertainty on Artificial Intelligence, 2006, Boston. Proceedings of the 22nd Conference on Uncertainty on Artificial Intelligence, 2006.
Silva, Ricardo ; SCHEINES, R. . Towards association rules with hidden variables. In: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, 2006, Berlin. Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, 2006.
Silva, Ricardo ; ZHANG, J. ; SHANAHAN, J. G. . Probabilistic workflow mining. In: 11th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,, 2005, Chicago. Proceedings of the 11th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,, 2005.
Silva, Ricardo ; SCHEINES, R. . New d-separation identification results for learning continuous latent variable models.. In: International Conference in Machine Learning, 2005, Bonn. Proceedings of the International Conference in Machine Learning,, 2005.
Silva, Ricardo ; SCHEINES, R. ; GLYMOUR, C. ; SPIRTES, P. . Learning measurement models for unobserved variables. In: 19th Conference on Uncertainty on Artificial Intelligence, 2003, Acapulco. Proceedings of the 19th Conference on Uncertainty on Artificial Intelligence, 2003.
MOODY, J. ; Silva, Ricardo ; VANDERWAART, J. ; GLYMOUR, C. . Data filtering for automatic classification of rocks from reflectance spectra. In: 7th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2001, San Francisco. Proceedings of the 7th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2001.
SILVA, R. B. A. ; LUDERMIR, T. B. . Obtaining simplified rules by hybrid learning. In: International Conference in Machine Learning (ICML), 2000, Stanford. Proceedings of the International Conference in Machine Learning, 2000.
SILVA, R. B. A. ; LUDERMIR, T. B. . Neural network methods for rule induction. In: International Joint Conference on Neural Networks 1999, 1999, Washington, EUA. Proceedings of the International Joint Conference on Neural Networks, 1999.
SILVA, R. B. A. ; PRUDÊNCIO, R. B. C. ; TEIXEIRA, I. R. ; PEQUENO, M. C. . Redes neurais aplicadas a sistemas especialistas. In: XVI Encontro de Iniciação à Pesquisa da Universidade Federal do Ceará, 1997, Fortaleza. XVI Encontro de Iniciação à Pesquisa UFC, 1997.
NOGUEIRA, J. H. M. ; SILVA, R. B. A. . Desenvolvimento integrado de sistemas especialistas através de ferramentas RAD. In: XI Simpósio Brasileiro de Engenharia de Software, 1997, Fortaleza. Anais do XI Simpósio Brasileiro de Engenharia de Software, Sessão de Ferramentas, 1997. p. 491-494.
NOGUEIRA, J. H. M. ; SILVA, R. B. A. . Expert SINTA. In: X Simpósio Brasileiro de Engenharia de Software, 1996, São Carlos. Anais do X Simpósio Brasileiro de Engenharia de Software, Sessão de Ferramentas, 1996.
SILVA, R. B. A. ; ALCÂNTARA, J. F. L. ; HOLANDA, S. C. ; ANDRADE, R. C. . Aplicações baseadas no Expert SINTA, uma ferramenta para criação de sistemas especialistas. In: XV Encontro de Iniciação à Pesquisa da Universidade Federal do Ceará, 1996, Fortaleza. XV Encontro de Iniciação à Pesquisa UFC, 1996.

Outras produções

SILVA, R. B. A. ; NOGUEIRA, J. H. M. . Expert SINTA Visual Component Library. 1997.

SILVA, R. B. A. ; NOGUEIRA, J. H. M. ; SILVESTRE, R. S. ; ALCÂNTARA, J. F. L. ; ANDRADE, R. C. . Expert SINTA. 1996.

Prêmios

2005

Siebel Scholarship, Siebell Systems.

2000

Microsoft Research Fellowship, Microsoft.

1998

Bolsa para Mestrado, CNPq.

1997

Melhor Trabalho de Iniciação Científica em Matemática/Computação/Estatística, UFC.

1996

Melhor Trabalho de Iniciação Científica em Matemática/Computação/Estatística, UFC.

1995

Bolsa do Programa Especial de Treinamento, CAPES.

Histórico profissional

Endereço profissional

University College London, Department of Statistical Science. , Gower Street, Bloomsbury, WC1E6BT - London, - Grã-Bretanha, Telefone: (44) 207679200, URL da Homepage: