Ashkan Sami
ashkan sami

Prof Ashkan Sami

  

Biography

Prof. Ashkan Sami, a faculty member in the Computer Science department at 麻豆社区 with an emphasis on Software Engineering, is dedicated to the use of foundation models for solving research problems in a broad spectrum of disciplines, including Software Engineering, medicine, and engineering.

In his academic journey, Prof. Sami has engaged in wide range of interdisciplinary and transdisciplinary research, striving to contribute meaningfully to these areas. His efforts have led to publications in respected journals and presentations at notable conferences. Prof. Sami has been honored with several national awards in industrial and applied research, acknowledging his contributions to the field.

From 2015 to 2020, Ashkan embraced the entrepreneurial world, leading his startup company. This experience has enriched his perspective in both academia and industry.

His work has gained media attention, including features in the BBC, The Register, and Stack Overflow team blogs, as well as international news outlets in China, Japan, Germany, and Spain.

Prof. Sami's academic achievements are distinguished by a Ph.D. from Tohoku University in Japan, obtained in 2006, which led to a significant research grant from MEXT and an assistant professorship at the university. His educational background also includes an M.Sc. in AI and Robotics from Shiraz University (1996) and a B.Sc. from Virginia Tech (1991).

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19 results

Dementia Friendly Buildings鈥擜pproach on Architectures

Journal Article
Ghamari, M., Suvish, Dehkordi, A. A., See, C. H., Sami, A., Yu, H., & Sundaram, S. (2025)
Dementia Friendly Buildings鈥擜pproach on Architectures. Buildings, 15(3), Article 385. https://doi.org/10.3390/buildings15030385
Dementia鈥檚 escalating incidence, coupled with its economic burden, highlights the need for architectural designs and forms that benefit people living with dementia. This resea...

Investigating Markers and Drivers of Gender Bias in Machine Translations

Presentation / Conference Contribution
Barclay, P., & Sami, A. (2024, March)
Investigating Markers and Drivers of Gender Bias in Machine Translations. Presented at IEEE International Conference on Software Analysis, Evolution and Reengineering, Rovaniemi, Finland
Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world ...

A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing

Conference Proceeding
Benza茂d, C., Taleb, T., Sami, A., & Hireche, O. (2024)
A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference (4747-4753). https://doi.org/10.1109/globecom54140.2023.10436891
Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of E...

A case study of fairness in generated images of Large Language Models for Software Engineering tasks

Conference Proceeding
Sami, M., Sami, A., & Barclay, P. (2023)
A case study of fairness in generated images of Large Language Models for Software Engineering tasks. In 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME). https://doi.org/10.1109/icsme58846.2023.00051
Bias in Large Language Models (LLMs) has significant implications. Since they have revolutionized content creation on the web, they can lead to more unfair outcomes, lack of i...

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

Journal Article
Feizpour, E., Tahayori, H., & Sami, A. (2023)
CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics. Journal of Software: Evolution and Process, 35(12), Article e2569. https://doi.org/10.1002/smr.2569
Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has i...

Application of deep learning in generating structured radiology reports: a transformer-based technique

Presentation / Conference Contribution
Moezzi, A., Rahmanian, M., Ghaedi, A., Mousavi, S. Z., & Sami, A. (2023, March)
Application of deep learning in generating structured radiology reports: a transformer-based technique. Paper presented at European Congress of Radiology 2023, Vienna

Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique

Journal Article
Moezzi, S. A. R., Ghaedi, A., Rahmanian, M., Mousavi, S. Z., & Sami, A. (in press)
Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique. Journal of Digital Imaging, https://doi.org/10.1007/s10278-022-00692-x
Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is di...

Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset

Presentation / Conference Contribution
Khoshnoud, F., Nasab, A. R., Toudeji, Z., & Sami, A. (2022, May)
Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset. Presented at MSR '22: 19th International Conference on Mining Software Repositories, Pittsburgh, US
In pull-based development systems, code reviews and pull request comments play important roles in improving code quality. In such systems, reviewers attempt to carefully check...

Short-term individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative study with deep learning methods

Journal Article
Forootani, A., Rastegar, M., & Sami, A. (2022)
Short-term individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative study with deep learning methods. Electric Power Systems Research, 210, Article 108119. https://doi.org/10.1016/j.epsr.2022.108119
Accurate short-term forecasting of the individual residential load is a challenging task due to the nonlinear behavior of the residential customer. Moreover, there are a large...

EfficientMask-Net for face authentication in the era of COVID-19 pandemic

Journal Article
Azouji, N., Sami, A., & Taheri, M. (2022)
EfficientMask-Net for face authentication in the era of COVID-19 pandemic. Signal, Image and Video Processing, 16(7), 1991-1999. https://doi.org/10.1007/s11760-022-02160-z
Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This researc...

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