Curriculum Vitae: Armando Vieira, PhD

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An accomplished Data Science Director with extensive experience leading complex data initiatives and building high-performing data science teams. My focus is on leveraging advanced AI technologies to solve business challenges, drive strategic decision-making, and create a significant impact. With a strong back-ground in data-driven leadership while being fully hands-on, I specialize in aligning scientific expertise with business strategy to ensure that data science initiatives deliver measurable business value.

I have co-founded two startups and have a strong entrepreneurial spirit, which allows me to be both a visionary and an executor. I am also an author of two books (”Business Applications of Deep Learning”

(2018) and ”Thinking Machines: The Rise of AI and its Impact on society and Economy” - to be released

2025. Have had the privilege of speaking at TEDx and several major conferences, where I’ve shared my passion for the transformative power of AI.

Key Skills and Competencies

• Leadership and Strategic Vision: Proven success in setting data strategy, managing teams, and ensuring alignment between data initiatives and business objectives.
• Team Building and Mentoring: Extensive experience in hiring, mentoring, and developing high-performing data science teams.
• AI and Machine Learning Expertise: In-depth experience with deep learning, Bayesian statis- tics, predictive modeling, and AI bias mitigation.
• Business Acumen and Product Development: Skilled in translating business goals into data- driven initiatives that directly impact the bottom line. Expertise in working with cross-functional teams to bring data products to market.
• Communication and Stakeholder Management: Effective communicator able to articulate complex technical solutions to non-technical stakeholders, driving buy-in and ensuring alignment.

Academic Experience

• Tartu University - Professor Artificial Intelligence Aug 2024 - Present Responsible and lecture on Business Data Analytics.
• Associate Professor, Universidade Lusófona, Lisboa: 2022–2025 Teaching advanced machine learning algorithms to Master’s students in Data Science, with an emphasis on practical applications of machine learning and statistical modeling.
• Full Professor, Instituto Superior de Engenharia do Porto (ISEP): 2000–2010 Taught undergraduate courses in Physics and contributed to the development of the Biomedical Engineering degree.
• PhD Supervision: Advisor for two PhD theses:
• ”Large Language Models in Legal Reasoning and Applications” (2023 -), Universidade Luso- fona, Research focused on the integration of LLMs in legal analysis and decision-making.
• ”Neural Networks for Ion Beam Analysis” (2005-2009) – Universidade de Lisboa, Study on the application of neural networks to enhance ion beam data processing and material charac- terization.

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• PhD Jury Member: Served as a jury member for the evaluation of a PhD thesis ”SVMs for Natural Language Processing”.

Programming and Software Skills

• Languages & Tools: Advanced Python, R, Matlab, SQL, Keras, TensorFlow, PyTorch, Amazon Sagemaker, Hugging Face Transformers, OpenAI GPT, LangChain, Docker
• Expertise Areas: Prompt Engineering, Deep Learning, Bayesian Networks, Generative Ad- versarial Networks (GANs), Variational Autoencoders (VAEs), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Autonomous AI Agents, Graph Neural Networks (GNNs)
• Domain Knowledge: Statistical Analysis, Complex Systems, Pattern Recognition, Natural Lan- guage Processing (NLP), Computer Vision, Speech Processing Agentic AI Systems, AI Safety and Ethics, Model Interpretability, Scalable AI Systems AI for Scientific Discovery

Selected [nom] Experience

Tartu University - Professor Artificial Intelligence · Aug 2024 - Present

Responsible and lecture on Business Data Analytics.

Medgical - Co-founder and Chief Data Scientist · Sep 2023 – Present

• Architecting and overseeing the development of end-to-end AI pipelines for healthcare operations, integrating multimodal data sources including EHRs, medical imaging, and clinical notes.
• Designing and deploying large language model (LLM)-based agents for autonomous processing of medical documentation, appointment triage, and real-time patient query handling.
• Aligning technical architecture with strategic business outcomes, contributing to investor engage- ment, regulatory compliance strategy, and product-market fit refinement.

Vamstar - Senior Data Scientist · Jul 2022 - Aug 2023

• Developed algorithms for price prediction of pharmaceutical products to optimize supply chain decisions.
• Contributed to the data science strategy, focusing on improving model accuracy and ensuring compliance with industry standards and worked closely with cross-functional teams to develop AI-driven solutions for the pharmaceutical industry.

[lien] - Consultant AI · Dec 2021 - Jun 2022

• Provided consulting services for implementation of generative models for complex time-series data, helping clients harness the power of synthetic data and privacy-enhancing technologies.
• Collaborated with data teams to design and develop innovative machine learning models, with a focus on mitigating bias and enhancing data quality.

[lien] - Head of Data Science · Jan 2019 - Nov 2021

• Led a data science team in developing and deploying synthetic data generation models, enhancing data privacy while mitigating AI bias.
• Defined the strategic direction for AI capabilities and aligned technical work with business goals to ensure high ROI. Oversaw the inclusion of Hazy in the Crunchbase list of the most promising AI companies in 2022.

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Contract Roles — Data Science & AI Applications · 2017–2019

• Tesco – Data Scientist (Contractor): Used Dynamical Bayesian Networks to predict stock levels in supermarkets, achieving cost savings of €10M in stock routines.
• British Telecom – Data Scientist (Contractor): Applied Machine Learning algorithms to analyse and optimize information flow and user behaviour.
• Direct Line – Deep Learning Specialist: Collaborated in a cross-functional team applying deep learning to medical image and video segmentation with time-dependent CNNs, contributing to healthcare innovation.
• MainAd (Italy) – Retargeting Marketing Project Manager: Designed and led an LSTM- based system to optimize digital campaigns, integrating AI with DSP platforms and improving marketing KPIs.
• Contextvision (Sweden) Collaborated in a cross-functional team to apply deep learning algo- rithms to medical images and videos segmentation with time dependent CNNs, contributing to advancements in healthcare technology.

BUPA Insurance, London - Lead Data Scientist · 2015 - 2016

• Built and led a data science unit, developing innovative data strategies that transformed BUPA Insurance into a data-driven organization. Focus areas included customer experience, risk manage- ment, and marketing.

RedZebra Analytics / Closer - Chief Data Scientist · 2012 - 2015

• Directed the development of customer behavior prediction tools using banking transactional data, leading to better customer segmentation and marketing alignment.
• Developed and scaled models, contributing to an increase in profitability through predictive accu- racy.

Instituto Superior de Engenharia do Porto (ISEP) - Full Professor of Physics

2000 - 2011

• Taught undergraduate courses in Physics, contributing to the education of future engineers.
• Assisted in the development of the Biomedical Engineering degree at ISEP in 2007.
• Coordinated and secured funding for 2 National Research Projects from FCT with budget 450 000 Euros.

Education

• PhD in Theoretical Physics

University of Coimbra, approved with distinction

• MSc in Physics & Engineering of Plasmas

Nuclear Fusion Centre, Lisbon, 17/20

• BSc in Physics

Instituto Superior Técnico, Lisbon, 15/20

Key Achievements

• Public interventions: Author of the book ”Business Applications of Deep Learning” (Apress, 2018). Speaker at TEDx and major data science conferences, including PyData and EARL.
• Entrepreneurship: Co-founded three startups (Sairmais, [lien] and Medgical), demonstrat- ing a keen ability to innovate and execute in dynamic environments.

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• Publications: Over 160 publications in refereed international journals and peer-reviewed confer- ence proceedings, with an h-Index: of 32 and i10-Index: 53.

Training Courses Given

• Programming with Artificial Intelligence - Tecnico Mais, IST Lisbon, 2024
• Artificial Intelligence for Bioinformatics, Universidade Pompeu Fabra, Master class, 2020.
• Generative Adversarial Neural Networks (GANs) for Insurance - Generali, Italy, 2018
• Retargeting Ad Optimization with LSTMs - MainAd, Italy, 2017
• Nuclear Radiations for Operators of Experimental Nuclear Reactors - ITN Lisboa, 1999

Membership

• (ISO)- member of ISO international technical committee on Artificial Intelligence Safety
• (IEEE) - Senior member of IEEE society.

Languages

• Portuguese (Native)
• English (Fluent)
• Spanish (Fluent)
• French (Intermediate)

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References

[1] Armando Vieira. Thinking Machines: Understanding AI Without Losing Your Mind. Apress, 2025.

[2] M. Felizardo M. Matos N.P. Barradas, A. Vieira. Nuclide identification of radioactive sources from gamma spectra using artificial neural networks. Radiation Physics and Chemistry, 6(145):18–36, 2025.

[3] N Pessoa Barradas, N Farjallah, A Vieira, and M Blaauw. Artificial neural networks for naa:

proof of concept on data analysed with k0-based software. Journal of Radioanalytical and Nuclear

Chemistry, 332(8):3421–3429, 2023.

[4] Polina Mamoshina, Armando Vieira, Evgeny Putin, and Alex Zhavoronkov. Applications of deep learning in biomedicine. Molecular pharmaceutics, 13(5):1445–1454, 2016.

[5] Antonio Gaspar-Cunha and Armando Vieira. A multi-objective evolutionary algorithm using neural networks to approximate fitness evaluations. Int. J. Comput. Syst. Signals, 6(1):18–36, 2005.

[6] N Pessoa Barradas and A Vieira. Artificial neural network algorithm for analysis of rutherford backscattering data. Physical Review E, 62(4):5818, 2000.

[7] Jelle Demeulemeester, Dries Smeets, Charlotte Van Bockstael, Christophe Detavernier, CM Com-rie, N Pessoa Barradas, Armando Vieira, and André Vantomme. Pt redistribution during ni (pt) silicide formation. Applied Physics Letters, 93(26), 2008.

[8] Armando Vieira and Nuno Barradas. A training algorithm for classification of high-dimensional data. Neurocomputing, 50:461–472, 2003.

[9] António Gaspar-Cunha and Armando Vieira. A hybrid multi-objective evolutionary algorithm using an inverse neural network. In Hybrid Metaheuristics, pages 25–30, 2004.

[10] Bernardete Ribeiro, Catarina Silva, Armando Vieira, and João Neves. Extracting discriminative features using non-negative matrix factorization in financial distress data. In Adaptive and Natural

Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April

23-25, 2009, Revised Selected Papers 9, pages 537–547. Springer Berlin Heidelberg, 2009.

[11] Bernardete Ribeiro, Armando Vieira, João Duarte, Catarina Silva, João Carvalho Das Neves, Qingzhong Liu, and Andrew H Sung. Learning manifolds for bankruptcy analysis. In Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New

Zealand, November 25-28, 2008, Revised Selected Papers, Part I 15, pages 723–730. Springer Berlin

Heidelberg, 2009.

[12] Ning Chen and Armando Vieira. Bankruptcy prediction based on independent component anal-ysis. In International Conference on Agents and Artificial Intelligence, volume 1, pages 150–155.

SCITEPRESS, 2009.

[13] J Demeulemeester, D Smeets, NP Barradas, A Vieira, CM Comrie, K Temst, and A Vantomme.

Artificial neural networks for instantaneous analysis of real-time rutherford backscattering spectra.

Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 268(10):1676–1681, 2010.

[14] Ning Chen, Armando S Vieira, João Duarte, Bernardete Ribeiro, and João C Neves. Cost-sensitive learning vector quantization for financial distress prediction. In Progress in Artificial Intelligence:

14th Portuguese Conference on Artificial Intelligence, EPIA 2009, Aveiro, Portugal, October 12-15, 2009. Proceedings 14, pages 374–385. Springer Berlin Heidelberg, 2009.

[15] N Pessoa Barradas, A Vieira, and R Patricio. Artificial neural networks for automation of rutherford backscattering spectroscopy experiments and data analysis. Physical Review E, 65(6):066703, 2002.

[16] A Vieira, NP Barradas, and E Alves. Analysis of sapphire implanted with different elements using artificial neural networks. Nuclear Instruments and Methods in Physics Research Section B: Beam

Interactions with Materials and Atoms, 190(1-4):241–246, 2002.

[17] Armando Vieira and Carlos Fiolhais. Shell effects on fission barriers of metallic clusters: A sys-tematic description. Physical Review B, 57(12):7352, 1998.

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[18] Ning Chen, Bernardete Ribeiro, Armando S Vieira, João Duarte, and João C Neves. A genetic algorithm-based approach to cost-sensitive bankruptcy prediction. Expert Systems with Applica-tions, 38(10):12939–12945, 2011.

[19] A Gaspar-Cunha, A Vieira, and Carlos M Fonseca. Multi-objective optimization: Hybridization of an evolutionary algorithm with artificial neural networks for fast convergence. In Workshop on design and evaluation of advanced hybrid meta-heuristics, 2004.

[20] A Vieira and NP Barradas. Neural network analysis of rutherford backscattering data. Nuclear

Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 170(1-2):235–238, 2000.

[21] A Vieira and N Pessoa Barradas. Composition of nitac films on si using neural networks analysis of elastic backscattering data. Nuclear Instruments and Methods in Physics Research Section B:

Beam Interactions with Materials and Atoms, 174(3):367–372, 2001.

[22] A Gaspar-Cunha, Armando Vieira, G Bugeda, J A-Dsidri, J Periaux, M Schoenauer, and G Winter.

A multi-objective evolutionary algorithm using approximate fitness evaluation. Proc. Int. Congr.

Evol. Methods for Design, Optimization and Control With Applications to Industrial Problems

EUROGEN, 2003.

[23] Armando S Vieira, João Duarte, Bernardete Ribeiro, and João C Neves. Accurate prediction of financial distress of companies with machine learning algorithms. In Adaptive and Natural

Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April

23-25, 2009, Revised Selected Papers 9, pages 569–576. Springer Berlin Heidelberg, 2009.

[24] Armando Vieira and Carlos Fiolhais. The two-centre shell model for the fission of metallic clusters.

Zeitschrift für Physik D Atoms, Molecules and Clusters, 37:269–276, 1996.

[25] Armando Vieira, Marta Brajczewska, and Carlos Fiolhais. Decay of charged stabilized jellium clusters. International Journal of Quantum Chemistry, 56(4):239–246, 1995.

[26] Bernardete Ribeiro, Armando Vieira, and Joao Carvalho das Neves. Sparse bayesian models:

Bankruptcy-predictors of choice? In The 2006 IEEE International Joint Conference on Neural

Network Proceedings, pages 3377–3381. IEEE, 2006.

[27] Ning Chen, Bernardete Ribeiro, Armando Vieira, and An Chen. Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert systems with applications, 40(1):385–393, 2013.

[28] Marta Brajczewska, Armando Vieira, Carlos Fiolhais, and JP Perdew. Volume shift and charge instability of simple-metal clusters. Progress in Surface Science, 53(2-4):305–313, 1996.

[29] Armando Vieira and Carlos Fiolhais. Fission of metallic clusters in the liquid drop model. Physics

Letters A, 220(4-5):231–236, 1996.

[30] Ning Chen, Bernardete Ribeiro, Armando Vieira, João Duarte, and João Neves. Weighted learning vector quantization to cost-sensitive learning. In Artificial Neural Networks–ICANN 2010: 20th

International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part III 20, pages 277–281. Springer Berlin Heidelberg, 2010.

[31] A Vieira, NP Barradas, and C Jeynes. Error performance analysis of artificial neural networks applied to rutherford backscattering. Surface and Interface Analysis: An International Journal devoted to the development and application of techniques for the analysis of surfaces, interfaces and thin films, 31(1):35–38, 2001.

[32] V Matias, G Öhl, JC Soares, NP Barradas, A Vieira, S Cardoso, and PP Freitas. Determina-tion of the composition of light thin films with artificial neural network analysis of rutherford backscattering experiments. Physical Review E, 67(4):046705, 2003.

[33] Bernardete Ribeiro, Armando Vieira, and João Carvalho das Neves. Supervised isomap with dissimilarity measures in embedding learning. In Progress in Pattern Recognition, Image Analysis and Applications: 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Havana, Cuba, September 9-12, 2008. Proceedings 13, pages 389–396. Springer Berlin Heidelberg, 2008.

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[34] NP Barradas, A Vieira, and R Patrıcio. Rbs without humans. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 190(1-4):231–236, 2002.

[35] FF Karpeshin, Armando Vieira, Carlos Fiolhais, and J da Providencia Jr. On the ternary fission of atomic clusters. Europhysics Letters, 42(2):149, 1998.

[36] Armando Vieira, Pedro A Castillo, and Juan J Merelo. Application of hlvq and g-prop neural networks to the problem of bankruptcy prediction. In International Work-Conference on Artificial

Neural Networks, pages 655–662. Springer Berlin Heidelberg Berlin, Heidelberg, 2003.

[37] Armando Vieira. Kick-off. The Physics Teacher, 44(5):286–289, 2006.

[38] Ning Chen, Armando Vieira, Bernardete Ribeiro, Joao Duarte, and João Neves. A stable credit rating model based on learning vector quantization. Intelligent Data Analysis, 15(2):237–250, 2011.

[39] Bernardete Ribeiro, Catarina Silva, Armando Vieira, António Gaspar-Cunha, and Joao C das

Neves. Financial distress model prediction using svm+. In The 2010 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE, 2010.

[40] HFR Pinho, A Vieira, NR Nené, and NP Barradas. Artificial neural network analysis of multiple iba spectra. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 228(1-4):383–387, 2005.

[41] Bernardete Ribeiro, Catarina Silva, Ning Chen, Armando Vieira, and Joao Carvalho das Neves.

Enhanced default risk models with svm+. Expert Systems with Applications, 39(11):10140–10152, 2012.

[42] NP Barradas, A Vieira, and E Alves. Artificial neural network analysis of rbs data of er-implanted sapphire. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 175:108–112, 2001.

[43] NR Nené, A Vieira, and N Pessoa Barradas. Artificial neural network analysis of rbs and erda spectra of multilayered multielemental samples. Nuclear Instruments and Methods in Physics

Research Section B: Beam Interactions with Materials and Atoms, 246(2):471–478, 2006.

[44] João Carvalho das Neves and Armando Vieira. Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization. 2004.

[45] Armando Vieira, João C Neves, and Bernardete Ribeiro. A method to improve generalization of neural networks: application to the problem of bankruptcy prediction. In Adaptive and Natural

Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, 2005, pages 417–420. Springer Vienna, 2005.

[46] Ning Chen, Bernardete Ribeiro, Armando Vieira, João Duarte, and João C Neves. Extension of learning vector quantization to cost-sensitive learning. International Journal of Computer Theory and Engineering, 3(3):352, 2011.

[47] Armando Vieira and Baldomero Oliva. Protein loop classification using artificial neural networks.

In Brazilian Symposium on Bioinformatics, pages 222–225. Springer Berlin Heidelberg Berlin, Heidelberg, 2005.

[48] Armando Vieira, Carlos Fiolhais, Marta Brajczewska, and John P Perdew. Self-expansion and compression of charged clusters of stabilized jellium. International journal of quantum chemistry, 60(7):1537–1548, 1996.

[49] ARMANDO Vieira, A Fernandes, and RAFAEL PATRÍCIO. Optimization of neutron beams for bnct using artificial neural networks. In Proceedings of the WSES International Conference on Neural Networks and Applications, Tenerife, page 38, 2001.

[50] NR Nené, A Vieira, AC Marques, RM Almeida, AR Ramos, E Alves, and NP Barradas. Analysis of sol–gel silica–titania films doped with ag and er using artificial neural networks. Nuclear Instru-ments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 249(1-2):804–807, 2006.

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[51] B Ribeiro, A Vieira, J Duarte, C Silva, J Carvalho das Neves, Q Liu, and AH Sung. Bankruptcy analysis for credit risk using manifold learning. In Proc. of the ICONIP 2008, International Con-ference on Neural Information Processing, Auckland, New Zealand, 2008.

[52] Fernando Mendes, João Duarte, Armando Vieira, and António Gaspar-Cunha. Feature selection for bankruptcy prediction: A multi-objective optimization approach. In Soft Computing in Indus-trial Applications: Algorithms, Integration, and Success Stories, pages 109–115. Springer Berlin

Heidelberg, 2010.

[53] G Öhl, V Matias, A Vieira, and NP Barradas. Artificial neural network analysis of rbs data with roughness: Application to ti0. 4al0. 6n/mo multilayers. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 211(2):265–273, 2003.

[54] NP Barradas, RN Patrıcio, HFR Pinho, and A Vieira. A general artificial neural network for analysis of rbs data of any element with z between 18 and 83 implanted into any lighter one-or two-element target. Nuclear Instruments and Methods in Physics Research Section B: Beam

Interactions with Materials and Atoms, 219:105–109, 2004.

[55] Ning Chen, Bernardete Ribeiro, Armando S Vieira, João Duarte, and João C Neves. Hybrid genetic algorithm and learning vector quantization modeling for cost-sensitive bankruptcy prediction. In 2010 Second International Conference on Machine Learning and Computing, pages 213–217. IEEE, 2010.

[56] Armando Vieira, M Begona Torres, Carlos Fiolhais, and L Carlos Balbás. Comparison of the spherically averaged pseudopotential model with the stabilized jellium model. Journal of Physics

B: Atomic, Molecular and Optical Physics, 30(15):3583, 1997.

[57] António Gaspar-Cunha, Fernando Mendes, João Duarte, Armando Vieira, Bernardete Ribeiro, André Ribeiro, and João Neves. Multi-objective evolutionary algorithms for feature selection: ap-plication in bankruptcy prediction. In Simulated Evolution and Learning: 8th International Con-ference, SEAL 2010, Kanpur, India, December 1-4, 2010. Proceedings 8, pages 319–328. Springer

Berlin Heidelberg, 2010.

[58] António Gaspar-Cunha, Fernando Mendes, J Duarte, Armando Vieira, Bernardete Ribeiro, André

Ribeiro, and J Neves. Feature selection for bankruptcy prediction: A multi-objective optimization approach. International Journal of Natural Computing Research (IJNCR), 1(2):71–91, 2010.

[59] J Demeulemeester, D Smeets, CM Comrie, NP Barradas, A Vieira, Charlotte Van Bockstael, Christophe Detavernier, K Temst, and A Vantomme. On the growth kinetics of ni (pt) silicide thin films. Journal of Applied Physics, 113(16), 2013.

[60] Armando Vieira, Bernardete Ribeiro, and Ning Chen. Credit scoring for sme using a manifold supervised learning algorithm. In Intelligent Data Engineering and Automated Learning-IDEAL

2012: 13th International Conference, Natal, Brazil, August 29-31, 2012. Proceedings 13, pages

763–770. Springer Berlin Heidelberg, 2012.

[61] Ning Chen, Bernardete Ribeiro, and Armando S Vieira. Bankruptcy trajectory analysis on french companies using self-organizing map. In Progress in Artificial Intelligence: 15th Portuguese Con-ference on Artificial Intelligence, EPIA 2011, Lisbon, Portugal, October 10-13, 2011. Proceedings

15, pages 407–417. Springer Berlin Heidelberg, 2011.

[62] Armando Vieira. Dimensionamento simplificado de uma linha de distribuição eléctrica em corrente contı́nua. Revista Brasileira de Ensino de Fı́sica, 27:625–627, 2005.

[63] NP Barradas, A Vieira, V Matias, G Öhl, JC Soares, S Cardoso, and PP Freitas. Development of artificial neural networks for thin alxnyoz films measured by rutherford backscattering. Ion beam techniques for the analysis of light elements in thin films, including depth profiling, page 29, 2004.

[64] A Vieira, PA Castillo, and JJ Merelo. Application of a new algorithm for training neural networks to bankruptcy prediction.

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[65] A Vieira, A Ramalho, IC Gonçalves, A Fernandes, N Barradas, JG Marques, J Prata, and Ch Chaussy. Monte carlo calculations for neutron and gamma radiation fields on a fast neutron irradiation device. In Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications: Proceedings of the Monte Carlo 2000 Conference, Lisbon, 23–26 October 2000, pages 919–923. Springer Berlin Heidelberg Berlin, Heidelberg, 2001.

[66] C Fiolhais and A Vieira. Shell correction effects on fission barrier heights of metallic clusters: a systematic description. In APS March Meeting Abstracts, pages J28–07, 1997.

[67] G Ohl, V Matias, CJ Tavares, L Rebouta, E Alves, A Vieira, and NP Barradas. Rutherford backscattering analysis of roughness in tialn/mo multilayers with artificial neural networks. In EU- ROPHYSICS CONFERENCE ABSTRACTS ECA, volume 26, pages 230–230. European Physical

Society; 1999, 2002.

[68] Armando Vieira. An iterative artificial neural network for high dimensional data analysis. In Ar-tificial Neural Networks: Formal Models and Their Applications–ICANN 2005: 15th International

Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II 15, pages 691–696.

Springer Berlin Heidelberg, 2005.

[69] Ning Chen, Bernardete Ribeiro, Armando Vieira, Joao Duarte, and Joao C Neves. Incorporate cost matrix into learning vector quantization modeling: A comparative study of genetic algorithm, simulated annealing and particle swarm optimization. International Journal of Computer theory and engineering, 3(1):122, 2011.

[70] Armando Vieira and Carlos Fiolhais. Fission of metallic clusters within the shell correction method.

In AIP Conference Proceedings, volume 416, pages 407–410. American Institute of Physics, 1997.

[71] Armando Vieira. Elementos de radiação e propagação de ondas electromagnéticas, 2007.

[72] AS Vieira, João Duarte, B Ribeiro, and JC Neves. Accurate prediction of financial distress with machine learning algorithms.

[73] Marta Brajczewska, Carlos Fiolhais, Armando Vieira, and John P Perdew. Compression of metallic clusters in the stabilized jellium model. In Many-body physics: proceedings of the International

Conference, pages 455–458. World Scientific, 1994.

[74] Armando Vieira, João Duarte, Bernardete Ribeiro, and Joao Carvalho Neves. Improving personal credit scoring with hlvq-c. In International Conference on Neural Information Processing, pages

97–103. Springer Berlin Heidelberg Berlin, Heidelberg, 2008.

[75] V Matias, G Ohl, PP Freitas, S Cardoso, JC Soares, A Vieira, and NP Barradas. Artificial neural network study of alo˜ xn˜ y thin films analysed with rutherford backscattering. In EUROPHYSICS

CONFERENCE ABSTRACTS ECA, volume 26, pages 186–186. European Physical Society; 1999, 2002.

[76] Fernando Nogueira, Armando Vieira, Marta Brajczewska, and Carlos Fiolhais. Charged metal clusters: atomistic versus continuous background continuous descriptions. In INTERNATIONAL

WORKSHOP ON CONDENSED MATTER THEORIES, 21, Luso; PROVIDÊNCIA, João da, ed.; MALIK, FB, ed.–Condensed matter theories, pages 333–342. Nova Science, 1998.

[77] Carlos Fiolhais and Armando Vieira. Fission of metallic clusters. Collective motion and nuclear dynamics: proceedings of the Predeal International Summer School, pages 523–537, 1996.

[78] Armando Vieira. 9h45 Abertura. PhD thesis, Universidade do Minho.

[79] Armando Vieira. Comment on “kick-off”. The Physics Teacher, 44(7):406–407, 2006.

[80] Armando Vieira. The art of technical and scientific writing. Revista de Sistemas de Informação da FSMA, (8):22–30, 2011.

[81] Armando Vieira. A arte da escrita técnica. Revista de Sistemas de Informação da FSMA, 8:20–30, 2011.

[82] Armando Vieira and Carlos Fiolhais. Ciência e tecnologia em portugal: Métricas e impacto (1995-

2011), 2015.

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[83] Carlos Costa and A Vieira. Tourism planning and organisation in portugal. European Tourism

Planning and Organisation Systems the EU Member States. Bristol, UK: Channel View Publica-tions, pages 384–98, 2014.

[84] A Fernandes, A Vieira, IC Gonçalves, and A Ramalho. Monte carlo characterisation of the radiation field in the thermal column of the portuguese research reactor. In Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications: Proceedings of the Monte Carlo

2000 Conference, Lisbon, 23–26 October 2000, pages 731–736. Springer Berlin Heidelberg, 2001.

[85] Armando Vieira and Rita Lavos. Sairmais. com, a new web 2.0 portal for tourism information with a recommender system. Revista Turismo & Desenvolvimento, 2(13/14):895–904, 2010.

[86] A Ramalho, J Marques, IC Goncalves, IF Goncalves, A Fernandes, J Prata, and A Vieira. Use of the rpi to test the behaviour under irradiation of electronic circuits and components for cern.

In Proceedings of IAEA International Symposium on Research Reactor Utilization, Safety and Management, IAEA-SM-360, Lisboa, 1999.

[87] S Mukkamala, DD Tilve, AH Sung, B Ribeiro, and AS Vieira. Computational intelligent tech-niques for financial distress detection. International Journal of Computational Intelligence Re-search, 2(1):60–65, 2006.

[88] Joao Carvalho Neves and Armando Vieira. Improving bankruptcy prediction with hidden layer learning vector quantization. European Accounting Review, 15(2):253–271, 2006.

[89] Srinivas Mukkamala, Andrew H Sung, Ram B Basnet, Bernadette Ribeiro, and Aarmando S Vieira.

Model selection for financial distress classification. In Advances In Pattern Recognition, pages 299–

303. 2007.

[90] AS Vieira, B Ribeiro, S Mukkamala, JC Neves, and AH Sung. On the performance of learning machines for bankruptcy detection. In Second IEEE International Conference on Computational

Cybernetics, 2004. ICCC 2004., pages 323–327. IEEE, 2004.

[91] Armando Vieira. Predicting online user behaviour using deep learning algorithms. arXiv preprint arXiv:[téléphone], 2015.

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