Laura O. Moraes

Biography

Laura O. Moraes is a professor at Universidade Federal do Estado do Rio de Janeiro (UNIRIO). She has a master's degree and a Ph.D. in Systems Engineering and Computer Science at COPPE / UFRJ in the artificial intelligence research field. She graduated in Electronic and Computer Engineering from the Universidade Federal do Rio de Janeiro, during which she acted as a software engineer in the UFRJ-CERN collaboration. From 2011 to 2021, she was a co-founding partner of the data science start-up Twist Systems. In 2018, she participated in the Data Science for Social Good program at the University of Chicago, developing a predictive model to identify individuals at high risk of long-term unemployment. In 2019, she was a finalist for the 2nd EPFL Engineering Ph.D. Summit and presented her doctoral research at École polytechnique fédérale de Lausanne (EPFL), Switzerland. She worked as a lecturer for the MBA in Big Data & Analytics at Fundação Getúlio Vargas in 2019 and 2020 and taught Computer Science classes at Colégio Pedro II.

Interests

Computer Science Education
Learning Analytics
Educational Data Mining
Student Performance Prediction

Education

PhD in Systems Engineering and Computer Science, 2021, Universidade Federal do Rio de Janeiro
MEng in Systems Engineering and Computer Science, 2016, Universidade Federal do Rio de Janeiro
BSc in Electronic and Computer Engineering, 2013, Universidade Federal do Rio de Janeiro

Publications

Arthur Sasse, Laura O. Moraes, Carla Delgado, Carlos Pedreira (2024). Analisando a efetividade da formação de clusters na avaliação de exercícios de programação. In Anais do IV Simpósio Brasileiro de Educação em Computação, abril 22, 2024, Evento Online, Brasil. SBC, Porto Alegre, Brasil, 325-335.
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Bruno Hryniewicz, Fernando França, João Paixão, Laura O. Moraes (2024). Biblioteca para exemplificação no ensino de Álgebra Linear. In Anais do IV Simpósio Brasileiro de Educação em Computação, abril 22, 2024, Evento Online, Brasil. SBC, Porto Alegre, Brasil, 336-345.
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Henrique Rodrigues, Eduardo Santiago, Gabriel Wanderley, Laura Moraes, Carlos Eduardo Mello, Reinaldo Alvares, Pereira dos Santos, Rodrigo (2024). Artificial Intelligence Algorithms to Predict College Students’ Dropout: A Systematic Mapping Study. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 344-351, 2024 , Rome, Italy.
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Gabriel Xará, Laura O. Moraes, Carla Delgado, Claudio de Farias, João Pedro Freire (2023). Dealing with a large number of students and inequality when teaching programming in higher education. Anais do XXIX Workshop de Informática na Escola (WIE 2023), 07 e 08 de Novembro, 2023, Passo Fundo/RS (Prêmio de melhor artigo da trilha).
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Laura O. Moraes, Carlos Eduardo Pedreira, Carla Delgado, João Pedro Freire (2022). Machine Teaching: uma ferramenta didática e de análise de dados para suporte a cursos introdutórios de programação. Anais do II Simpósio Brasileiro de Educação em Computação, pp. 213-223, 24 a 29 de Abril, 2022, Virtual.
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Laura O. Moraes, Carlos Eduardo Pedreira, Carla Delgado, João Pedro Freire (2021). Supporting Decisions Using Educational Data Analysis. Anais Estendidos do XXVII Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia 2021), pp. 99-102, Novembro 2021, Virtual.
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Chunpai Wang, Shaghayegh Sahebi, Siqian Zhao, Peter Brusilovsky, Laura O. Moraes (2021). Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '21), pp. 179-188, June 21–25, 2021, Utrecht, Netherlands. ACM, New York, NY, USA.
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Laura O. Moraes, Carlos Eduardo Pedreira (2021). Unsupervised Concept Extraction in an Introduction to Programming Course. PhD Thesis in Systems Engineering and Computer Science - COPPE, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro.
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Laura O. Moraes, Carlos Eduardo Pedreira (2021). Clustering Introductory Computer Science Exercises Using Topic Modeling Methods. IEEE Transactions on Learning Technologies, vol. 14, no. 1, pp. 42-54, Feb. 2021.
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Laura O. Moraes, Carlos Eduardo Pedreira (2020). Designing an Intelligent Tutoring System Across Multiple Classes. 4th Educational Data Mining in Computer Science Education (CSEDM), 2020, Virtual Workshop.
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Laura O. Moraes, Carlos Eduardo Pedreira (2019). A Decision Tree Approach for the Differential Diagnosis of Chronic Lymphoid Leukemias and Peripheral B Cell Lymphomas. Computer Methods and Programs in Biomedicine, v. 178, pp. 85-90.
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Íñigo Martínez de Rituerto de Troya, Ruqian Chen, Laura O. Moraes, Pranjal Bajaj, Jordan Kupersmith, Rayid Ghani, Nuno B. Brás, Leid Zejnilovic (2018). Predicting, Explaining, and Understanding Risk of Long Term Unemployment. 32nd Annual Conference on Neural Information Processing Systems (NeurIPS) AI for Social Good Workshop, Canada.
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Laura O. Moraes, Carlos Eduardo Pedreira (2016). Classificação de Linfomas Utilizando uma Abordagem Baseada em Árvores De Decisão. Dissertação (Mestrado em Engenharia de Sistemas e Computação) - COPPE, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro.
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Louis-Martin Losier, Felipe Fink Grael, Etienne Desgagné, Fábio Hochleitner, Laura O. Moraes (2015). Cloud Based Alert System for Aggressive Natural Disasters. 15th Pan-American Conference on Soil Mechanics and Geotechnical Engineering, Argentina.
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Felipe Fink Grael, Carmen Maidantchik, Luiz Henrique Ramos de Azevedo Évora, Kaio Karam, Laura O. Moraes, Manuela Cirilli, Marzio Nessi, Kathy Pommès (2011). Glance Information System for Atlas Management. Journal of Physics: Conference Series, v. 331, n. 8, p. 082004.
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Students

current STUDENTS

Gabriel Monteiro De Castro Xara Wanderley (2023--ongoing). Master student.
Henrique Soares Rodrigues (2023--ongoing). Master student.
Matheus Avellar de Barros (2023--ongoing). Comparação sistemática de algoritmos supervisionados de classífica binária. Master student. Co-advised with Carlos Eduardo Pedreira (PESC/COPPE/UFRJ).
Juliana Barros de Sousa (2023--ongoing). Construção de feedback automatizado para alunos em curso introdutório de programação. Undergraduate Final Course Project (CNPq). Co-advised with Fernanda Duarte (DEL/UFRJ).
Renan Costa Alves (2023--ongoing). Machine Teaching: um ambiente virtual de aprendizagem personalizado. Undergraduate student (PIBIC-Af).
Beatriz Queiroz Reis (2023--ongoing). Infraestrutura flexível para um ambiente virtual de aprendizagem. Undergraduate student.
Hugo Folloni Guarilha (2022--ongoing). Machine Teaching. Undergraduate student (Reditus e PIBIC). Co-advised with Carla Delgado (IC/UFRJ).

PAST STUDENTS

Arthur Mendonça Sasse (2023). Explorando o agrupamento de códigos para feedbacks mais eficientes no ensino de programação. Undergraduate Final Course Project (CNPq). Co-advised with Carlos Eduardo Pedreira (PESC/COPPE/UFRJ) and Carla Delgado (IC/UFRJ).
João Pedro Matos Freire (2023). Modelos para previsão de desempenho de alunos em curso introdutório de programação. Undergraduate Final Course Project. Co-advised with Flávia Landim (IM/UFRJ) and Carla Delgado (IC/UFRJ).
João Pedro Matos Freire (2023). Previsão de desempenho em cursos de programação. Undergraduate student (PIBIC). Co-advised with Carlos Eduardo Pedreira (PESC/COPPE/UFRJ) and Carla Delgado (IC/UFRJ).
Rodrigo Silva Peres (2023). Grandes Modelos de Linguagem na resolução de questões de vestibular: o caso dos Institutos Militares brasileiros. Master's Thesis. Co-advised with Carlos Eduardo Mello.
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Alex Santos de Oliveira and Rafael da Silva Fernandes (2023). Exploring the impact of intermediate languages on machine translation. Undergraduate Final Course Project. Co-advised with João Antonio Recio da Paixão.
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Gabriel Monteiro de Castro Xara Wanderley (2023). Arquitetura resiliente e escalável para um sistema de execução de código remoto. Undergraduate Final Course Project. Co-advised with Claudio Miceli.
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Fernando Silva França and Bruno Hryniewicz Santos Cruz (2023). Biblioteca para exemplificação de álgebra linear EasyLinalg. Undergraduate Final Course Project. Co-advised with João Antonio Recio da Paixão.
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Gabriel Matos Cardoso Leite (2017). Técnica de Redução de Comparações em Classificadores Multiclasse. Undergraduate Final Course Project. Co-advised with Carlos Eduardo Pedreira.

Projects

CURRENT PROJECTS

PAST PROJECTS

Machine Teaching

Ir para o projeto

At this project we aim at creating a personalized learning system in Introduction to Programming classes as a way to uncover students’ difficulties, to understand their knowledge and to provide timely feedback to keep students engaged.

Keywords:

Learning Analytics
Educational Data Mining
Computer Science Education
CSEDM

Predicting long-term unemployment in Portugal

Ir para o projeto

At this project we aim at better identifying individuals at high risk of long-term unemployment (LTU) and supporting more efficient allocation of IEFP’s resources to respond to the needs of unemployed individuals.

Keywords:

Data Science for Social Good
Long-term Unemployment
Regression

Lasso Tree

Ir para o projeto

At this project we propose a decision-tree approach for the differential diagnosis of B-cell chronic lymphoproliferative disorders using flow cytometry data.

Keywords: