Mahsa Mohaghegh

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Senior Lecturer

Email: mahsa.mohaghegh@aut.ac.nz

ORCID: ORCID logo https://orcid.org/0000-0003-2228-8300

Links to relevant web pages:

Academic appointments:

  • Senior Lecturer, School of Engineering -Computer and Mathematical Sciences, Auckland University of Technology, AUT (2018 - ongoing)
  • Lecturer, AUT (2017 - 2018)
  • Academic Leader, Unitec Institute of Technology (2016 - 2017)
  • Senior Lecturer, Unitec Institute of Technology (2015 - 2017)
  • Outreach Development Manager, Unitec Institute of Technology (2014 - 2016)
  • Program Leader, Unitec Institute of Technology (2013 - 2014)
  • Lecturer, Unitec Institute of Technology (2010 - 2015)

Qualifications:

  • PhD, Massey University
  • MSc, Iran University of Science and Technology

Overview:

Computer Engineer. AI subject matter expert. Advocate for diversity in tech. Founder/director of She#.

Dr Mahsa Mohaghegh is a computer engineer with a broad background in technology, and an active researcher in internet of things, AI, and machine learning technologies. She is an advocate for taking early action to prepare for the digital disruption that is impacting every industry sector. She is also the founder and director of She# (www.shesharp.co.nz), the women’s networking and learning group aimed at supporting women, and closing the gender gap in technology.

Mahsa has significant experience in programme and project management, team leadership, funding acquisition and stakeholder management. She has been widely recognised in her community engagement work with She#, and for the past three years has been a semi-finalist for New Zealander of the Year.

Mahsa is a passionate, results-driven problem-solver, and a respected manager, mentor, and supervisor. She has an energetic and driven approach to management, and a sound ability to maintain a clear view of programme and project objectives.

Research interests:

Natural Language Processing, Machine Translation, Artificial Intelligence, Sensor Networks, Internet of Things

Teaching summary:

Mahsa currently works as a senior lecturer in AUT University’s School of Engineering, Computer and Mathematical Sciences. This role includes course leadership and teaching, course and paper development, research, postgraduate supervision, and community outreach.

Within course leadership and teaching, Mahsa is responsible for:
- Coordinating and teaching various computer science papers
- Leading the development of new courses and papers
- Updating papers with new material to reflect technology advancement
- Liaising with industry partners and ensuring course relevance with industry standard
- Forming new partnerships within the industry

Fields of research:

  • Artificial Intelligence and Image Processing
  • Natural Language Processing
  • Computer System Security
  • Ubiquitous Computing
  • Knowledge Representation and Machine Learning

Professional activities:

Featured professional activities

    Appointment, affiliation, and membership

    • Founder and Director, She Sharp https://www.shesharp.co.nz/ (2014 - ongoing)

Research outputs:

Journal articles

  • Mohaghegh, M., & McCauley, M. (2016). Computational thinking: The skill set of the 21st century. International Journal of Computer Science and Information Technologies, 7(3). Retrieved from http://ijcsit.com/docs/Volume%207/vol7issue3/ijcsit20160703104.pdf

  • Deylami, H. M., Mohaghegh, M., Sarrafzadeh, A., McCauley, M., Ardekani, I. T., & Kingston, T. (2015). Capture the talent: Secondary school education with cyber security competitions. International Journal in Foundations of Computer Science & Technology, 5(6). doi:10.5121/ijfcst.2015.5606

Conference contributions

  • Ho, K., Liesputra, V., Yongchareon, S., & Mohaghegh, M. (2018). Evaluating social spammer detection systems. In Proceedings of the Australasian Computer Science Week Multiconference 2018. Brisbane, Queensland. doi:10.1145/3167918.3167936

  • Ho, K., Liesapurta, V., Yongchareon, S., & Mohaghegh, M. (2017). A framework for evaluating anti spammer systems for twitter. In On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science Vol. 10573 (pp. 648-662). Rhodes. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-69462-7_41

  • Mohaghegh, M., & Sarrafzadeh, A. (2016). Parallel text identification using lexical and corpus features for the English-Maori language pair. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications. Anaheim, CA. doi:10.1109/ICMLA.2016.0163

  • Mohaghegh, M. (2016). Parallel text identification using lexical and corpus features for the English-Maori language pair. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 910-915). Anaheim, CA: IEEE. doi:10.1109/ICMLA.2016.0163

  • Sosamphan, P., Liesaputra, V., Yongchareon, S., & Mohaghegh, M. (2016). Evaluation of statistical text normalisation techniques for Twitter. In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Volume 1 Vol. 1 (pp. 413-418). Porto: SCITEPRESS. doi:10.5220/0006083004130418

  • Mohaghegh, M., Sarrafzadeh, H., & Mohammadi, M. (2014). Ensemble statistical and heuristic models for unsupervised word alignment. In Proceedings: 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 (pp. 61-66). Detroit, MI: IEEE (Institute of Electrical and Electronics Engineers). doi:10.1109/ICMLA.2014.15

  • Mohaghegh, M., Sarrafzadeh, A., & Mohammadi, M. (2013). A three-layer architecture for automatic post-editing system using rule-Based paradigm. In The 4th Workshop on South and Southeast Asian NLP (WSSANLP), International Joint Conference on Natural Language Processing (pp. 17-24). : Association for Computational Linguistics. doi:10.13140/RG.2.1.4547.9521

  • Mohaghegh, M., & Sarrafzadeh, A. (2012). A hierarchical phrase-based model for English-Persian statistical machine translation. In 2012 International Conference on Innovations in Information Technology (IIT) (pp. 205-208). Al Ain, Abu Dhabi: IEEE. doi:10.1109/INNOVATIONS.2012.6207733

  • Mohaghegh, M., Sarrafzadeh, A., & Mohammadi, M. (2012). GRAFIX: Automated rule-based post editing system to improve English-Persian SMT. In Proceedings of COLING 2012: Posters (pp. 873-882). Mumbai: ACL. Retrieved from http://www.aclweb.org/anthology/C12-2085

  • Mohaghegh, M., Sarrafzadeh, A., & Moir, T. (2011). Improving Persian-English Statistical Machine Translation: Experiments in Domain Adaption. In The 5th International Joint Conference on Natural Language Processing. Thailand. Retrieved from http://www.ijcnlp2011.org/proceeding/workshop/WS1_WSSANLP/pdf/WSSANLP02.pdf

  • Mohaghegh, M., Manford, C., & Sarrafzadeh, A. (2011). Cross-layer optimisation for quality of service support in wireless sensor networks. In 2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011 (pp. 528-533). doi:10.1109/ICCSN.2011.6014950

  • Mohaghegh, M., & Sarrafzadeh, A. (2011). An overview of the challenges and progress in PeEn-SMT: First large scale Persian-English SMT system. In 2011 International Conference on Innovations in Information Technology, IIT 2011 (pp. 319-323). doi:10.1109/INNOVATIONS.2011.5893841

  • Mohaghegh, M., Sarrafzadeh, A., & Moir, T. (2010). Improved Language Modeling for English-Persian Statistical Machine Translation. In Proceedings of SSST-4, (pp. 75-82). Coling. Retrieved from http://www.aclweb.org/anthology/W10-3810

  • Mohaghegh, M., & Sarrafzadeh, A. (2010). Performance Evaluation of Statistical English-Persian Machine Translation. In 10th International Conference on Statistical Analysis of Textual Data.. Sapienza. Retrieved from http://www.ledonline.it/

  • Mohaghegh, M., & Sarrafzadeh, A. (2009). An analysis of the effect of training data variation in English-Persian statistical machine translation. In 2009 International Conference on Innovations in Information Technology, IIT '09 (pp. 105-108). doi:10.1109/IIT.2009.5413782

  • Mohaghegh, M., Sarrafzadeh, A., & IEEE. (2009). An analysis of the effect of training data variation in English-Persian Statistical Machine Translation. In 2009 INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION TECHNOLOGY (pp. 51-+). Retrieved from http://gateway.webofknowledge.com/

Reports

  • Mohaghegh, M. (2018). Case Studies of Successful Women Entrepreneurs in the ICT Industry in 21 APEC Economies: Women’s Economic Empowerment and ICT: Capacity Building for APEC Women’s Entrepreneurs in the Age of the 4th Industrial Revolution. Retrieved from https://www.apec.org/

  • Mohaghegh, M. (2018). Economy Report of New Zealand: Profiles of ICT Business and Women Entrepreneurs in APEC Economies. Retrieved from https://www.apec.org/

Theses

  • Mohaghegh, M. (2012). English-Persian phrase-based statistical machine translation: Enhanced models, search and training. (Massey University, Auckland, New Zealand). Retrieved from https://mro.massey.ac.nz/handle/10179/4703