• Ph.D., Carleton University, Canada, 2018
  • M.S., University of Castilla-La Mancha, Spain, 2011
  • B.S., University of Bari, Italy, 2009

Research Interests

  • Software Engineering
  • Artificial Intelligence (Machine Learning, Deep Learning, Natural Language Processing)
  • Model-Driven Engineering
  • Empirical Studies
  • Cyber security

Professional Memberships

  • IEEE Member since 2016


  • Programming for Engineers
  • Programming for Engineers Lab
  • Artificial Intelligence and Machine Learning
  • Object-Oriented Programming and Design using C++


Damiano Torre, Ph.D., is an Assistant Professor of Software Engineering at St. Mary’s University, where he started his tenure in August 2023. With a passion for advancing the field of software engineering, Torre’s research interests primarily revolves around artificial intelligence, model-driven engineering, cyber security and empirical software engineering.

Before joining St. Mary’s University, Torre made significant contributions to academia and research. From April 2021 to July 2023, he served as an associate research scientist in the Department of Computer Information Systems as member of the Center for Cybersecurity Innovation at Texas A&M University-Central Texas. During his tenure, Torre achieved numerous remarkable milestones, including being Co-Principal Investigator of an NSF-awarded research proposal, and also a Co-Principal Investigator for another research proposal which was awarded a TARC seed funding award from the Texas A&M Engineering Experiment Station (TEES). Additionally, his research proposal on analyzing the vulnerabilities of AI-based techniques was selected as a DARPA Riser at the DARPA FORWARD Conference 2022. At Texas A&M University-Central Texas, Torre actively contributed to the academic community and demonstrated his expertise through multiple publications. He was the lead author of a manuscript published in Empirical Software Engineering, which presented the results of a systematic study on deep learning techniques for detecting cyber security attacks. His work also encompassed co-authoring papers on topics such as IoT networks, lightweight security, IoT forensic analysis, and privacy-preservation techniques for IoT devices, all published in reputable journals.

Prior to coming to the United States, Torre was affiliated with the University of Luxembourg, where he engaged in groundbreaking research projects in collaboration with industry partners from the legal and finance domains. His work on GDPR compliance and AI-assisted approaches for assessing privacy policies received recognition in renowned conferences and journals, including the ACM/IEEE International Conference on Model-Driven Engineering Languages and Systems (MODELS), IEEE Requirements Engineering (RE) Conference, and the IEEE Transactions on Software Engineering journal.

Torre’s academic journey includes a B.S. from the University of Bari (Italy), an M.S. from the University of Castilla-La Mancha (Spain) and a Ph.D. from Carleton University (Canada) in 2009, 2011 and 2018, respectively. These degrees exemplify his commitment to lifelong learning and academic excellence.

View the full list of publications on Google Scholar.

Lorenz, S., Stinehour, S., Chennamaneni, A., Subhani, A.B. and Torre, D., 2023. IoT forensic analysis: A family of experiments with Amazon Echo devices. Forensic Science International: Digital Investigation, 45, p.301541.

Torre, D., Mesadieu, F. and Chennamaneni, A., 2023. Deep learning techniques to detect cybersecurity attacks: a systematic mapping study. Empirical Software Engineering, 28(3), pp.1-71.

Torre, D., Chennamaneni, A. and Rodriguez, A., 2023. Privacy-Preservation Techniques for IoT Devices: A Systematic Mapping Study. IEEE Access, 11, pp. 16323-16345.

Amaral, O., Abualhaija, S., Torre, D., Sabetzadeh, M. and Briand, L.C., 2022. AI-enabled automation for completeness checking of privacy policies. IEEE Transactions on Software Engineering, 48(11), pp.4647-4674.

Goulart, A., Chennamaneni, A., Torre, D., Hur, B. and Al-Aboosi, F.Y., 2022. On wide-area IoT networks, lightweight security and their applications—a practical review. Electronics, 11(11), p.1762.

Torre, D., Genero, M., Labiche, Y. and Elaasar, M., 2022. How consistency is handled in model-driven software engineering and UML: an expert opinion survey. Software Quality Journal, 31(1), pp.1-54.

Torre, D., Alferez, M., Soltana, G., Sabetzadeh, M. and Briand, L., 2021. Modeling data protection and privacy: application and experience with GDPR. Software and Systems Modeling, 20, pp.2071-2087.

Veizaga, A., Alferez, M., Torre, D., Sabetzadeh, M. and Briand, L., 2021. On systematically building a controlled natural language for functional requirements. Empirical Software Engineering, 26(4), p.79.

Veizaga, A., Alferez, M., Torre, D., Sabetzadeh, M., Briand, L. and Pitskhelauri, E., 2020. Leveraging natural-language requirements for deriving better acceptance criteria from models. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) (pp. 218-228).

Torre, D., Abualhaija, S., Sabetzadeh, M., Briand, L., Baetens, K., Goes, P. and Forastier, S., 2020, August. An ai-assisted approach for checking the completeness of privacy policies against gdpr. In 2020 IEEE 28th International Requirements Engineering Conference (RE) (pp. 136-146).

Torre, D., Labiche, Y., Genero, M., Elaasar, M. and Menghi, C., 2020. UML consistency rules: a case study with open-source UML models. In Proceedings of the 8th International Conference on Formal Methods in Software Engineering (pp. 130-140).

Torre, D., Soltana, G., Sabetzadeh, M., Briand, L.C., Auffinger, Y. and Goes, P., 2019. Using models to enable compliance checking against the GDPR: an experience report. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS) (pp. 1-11).

Torre, D., Labiche, Y., Genero, M. and Elaasar, M., 2018. A systematic identification of consistency rules for UML diagrams. Journal of Systems and Software, 144, pp.121-142.

Torre, D., Procaccianti, G., Fucci, D., Lutovac, S. and Scanniello, G., 2017. On the presence of green and sustainable software engineering in higher education curricula. In 2017 IEEE/ACM 1st International Workshop on Software Engineering Curricula for Millennials (SECM) (pp. 54-60).

Torre, D., Labiche, Y., Genero, M., Elaasar, M., Das, T.K., Hoisl, B. and Kowal, M., 2016. 1st International Workshop on UML Consistency Rules (WUCOR 2015) Post workshop report. ACM SIGSOFT Software Engineering Notes, 41(2), pp.34-37.

Torre, D., Labiche, Y. and Genero, M., 2014. UML consistency rules: a systematic mapping study. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering (pp. 1-10).

Selected Publications

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