Nathalia Moraes do Nascimento

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Assistant Professor of Software Engineering, Engineering Division
[email protected]
610-648-3277

Dr. Nathalia Nascimento earned her Ph.D. and M.Sc. in Software Engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio, Brazil) and her B.Sc. in Computer Engineering from the State University of Feira de Santana (UEFS, Brazil).

She worked as a Postdoctoral Fellow at the University of Waterloo in Canada. Her principal research interests and key publications lie in the integration of Software Engineering and Artificial Intelligence, focusing on empirical studies, LLM-based agents, Generative AI applications in Software Engineering, Software Engineering for AI, Internet of Things (IoT), Software Frameworks, Software Product Lines, Multiagent systems, Evolutionary Applications (Neuroevolution), Natural Language Processing (NLP), Self-adaptive and Self-organizing systems, and Machine Learning applications for sensor data analysis.

She has showcased her work at multiple international conferences, such as those organized by NASA and IBM, contributing significantly to the fields of AI and Software Engineering.

  • Integration of Software Engineering and Artificial Intelligence
  • Empirical Studies in Software Engineering and AI
  • Generative AI for Software Engineering;
  • Large Language Model (LLM)-Based Agents
  • Multiagent Systems 
  • Software Frameworks and Product Lines
  • Self-Adaptive and Self-Organizing Systems
  • Internet of Things (IoT) and Embedded Systems
  • Machine Learning Applications for Sensor Data Analysis

To follow my most recent published papers and patents, please visit my Google Scholar profile.

  1. Nathalia, N., Paulo, A., and Donald, C. (2023). "Artificial Intelligence vs. Software Engineers: An Empirical Study on Performance and Efficiency Using ChatGPT." In Proceedings of the 33rd Annual International Conference on Computer Science and Software Engineering, pp. 24-33.
  2. Nascimento, N., Alencar, P., and Cowan, D. (2020). "A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems." IEEE Access, Vol. 8, pp. 204256-204267.
  3. Nascimento, N., Alencar, P., and Lucena, C. (2017). FIoT: An Agent-Based Framework for Self-Adaptive and Self-Organizing Applications Based on the Internet of Things. Information Sciences, Vol. 378, pp. 161-176.
  4. Melo, G., Nascimento, N., Alencar, P., and Cowan, D. (2023). "Identifying Factors that Impact Levels of Automation in Autonomous Systems." IEEE Access.
  5. Nascimento, N., Alencar, P., and Cowan, D. (2023). "Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems." In 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), pp. 104-109. IEEE.
  6. Nascimento, N., Tavares, C., Alencar, P., and Cowan, D. (2023). "GPT in Data Science: A Practical Exploration of Model Selection." In 2023 IEEE International Conference on Big Data (BigData), pp. 4325-4334. IEEE.
  7. Nascimento, N., Alencar, P., and Cowan, D. (2023). "GPT-in-the-Loop: Supporting Adaptation in Multiagent Systems." In 2023 IEEE International Conference on Big Data (BigData), pp. 4674-4683. IEEE.
  8. Melo, G., Nascimento, N., Alencar, P., and Cowan, D. (2023). "Variability-Aware Architecture for Human-Chatbot Interactions: Taming Levels of Automation." In 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), pp. 122-127. IEEE.
  9. Nascimento, N. M. do and de Lucena, C. J. P. (2017). "Engineering Cooperative Smart Things Based on Embodied Cognition." In Adaptive Hardware and Systems (AHS), 2017 NASA/ESA Conference on, pp. 109-116. IEEE.
  10. Nascimento, N., Alencar, P., Lucena, C., and Cowan, D. (2018). "A Context-Aware Machine Learning-Based Approach." In Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering, pp. 40-47.
  11. Nascimento, N., Alencar, P., Lucena, C., and Cowan, D. (2018). "Toward Human-in-the-Loop Collaboration Between Software Engineers and Machine Learning Algorithms." In 2018 IEEE International Conference on Big Data (Big Data), pp. 3534-3540. IEEE.

 

  • B.Sc., Computer Engineering, State University of Feira de Santana (UEFS), Brazil
  • M.Sc., Software Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
  • Ph.D., Software Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil