Learn About Me

I am an Assistant Professor (tenure-track) at the Intelligent Systems Research Group, Department of Electrical and Computer Engineering, McGill University. I am also an Associate Academic Professor of Mila-Quebec AI Institute. Before joining McGill University, I was a postdoctoral fellow at the Intelligent Assistive Technology and Systems Lab, University of Toronto. I obtained my PhD degree from Electrical and Computer Engineering Department, McMaster University .


Prospective Students

  • I am looking for PhD students in the area of machine learning and its applications. If you are interested and have a proven publication record, please ping me.
  • I am looking for M.Sc. students. Contact me if you can demonstrate prior experience and high-level skill in machine learning.
  • I have some projects for undergraduate honours thesis in the area of machine learning and computer vision. Contact me if you are an undergraduate student at McGill and would like to have your honours thesis with me.

  • MSc and PhD applicants who are interested in my research are encouraged to contact me via email. The body of the email MUST contain your name, degree you are applying for (M.Sc./PhD), your degree(s) information (university, GPA), whether you are domestic or international student, IELTS/TOEFL/GRE score (if applicable), your three recent publications (if applicable), whether or not you have applied to the Department of Electrical and Computer Engineering. Attach your complete academic CV and transcript(s). Please keep in mind that, due to the volume of emails that I receive, I may not reply to all.


    The ECE department deadlines for the Fall Semester is December 15th for both international and domestic applicants; and for the Winter Semester is August 1st for international applicants and October 15th for domestic applicants. For more information please read here.


    I strive to embody the values of respect, collaboration and diversity, and have a strong commitment to equity. The diversity of my Lab is at the core of our innovation and creativity and strengthens our research excellence. I seek qualified candidates who share our commitment to equity, diversity and inclusion. While all qualified candidates are invited to apply, I particularly welcome applications from women, persons with disabilities, First Nations, Métis and Inuit peoples, members of visible minorities, and LGBTQ+ persons.


    My Research Interests

    Current research interests include machine learning and related areas in computer vision, reinforcement learning, subspace learning for data clustering and classification, and anomaly detection.

    COURSES I TEACH

  • ECSE 551 Machine Learning for Engineers. Winter 2020, Fall 2021, Winter 2021. For more details, see the course homepage on myCourses.
  • ECSE 206 Introduction to Signals and Systems. Winter 2019, Fall 2019, Fall 2020. For more details, see the course homepage on myCourses.

  • My Students

    I am fortunate to supervise a number of talented students including:
  • Bahareh Nikpour, PhD
  • Mohammadreza Sadeghi, PhD
  • Thi Kieu Khanh Ho, PhD
  • Saqib Ali Khan, PhD
  • Shuhong Shen, MSc-thesis (Co-supervised)
  • Denzel Guye, MSc-thesis (Co-supervised)
  • Hadi Hojjati, MSc-thesis
  • Dimitrios Sinodinos, MSc-thesis
  • Tri-tin Truong, MSc-thesis
  • William Zhang, BSc
  • Michael Li, BSc

  • SELECTED PUBLICATIONS (Peer-Reviewed)

  • B. Nikpour, N. Armanfard, (2021),``Spatial Hard Attention Modeling via Reinforcement Learning for Skeleton-based Activity Recognition'', Under Review.
  • M. Sadeghi, N. Armanfard, (2021),``Deep Successive Subspace Learning for Data Clustering'', Under Review.
  • M. Sadeghi, N. Armanfard, (2021),``IDECF: Improved Deep Embedding Clustering with Deep Fuzzy Supervision'', Under Review.
  • B. Nikpour, N. Armanfard, (2021),``Joint Selection for Skeleton-based Activity Recognition'', Under Review.
  • M. Komeili, N. Armanfard, D. Hatzinakos, (2020), ``Multiview Feature Selection for Single-view Classification'', IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2020.2987013.
  • S. Chang, S. Mak, N. Armanfard, J. Boger, A. Mihailidis, (2020), ``Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure'', IEEE Journal of Translational Engineering in Health and Medicine DOI: 10.1109/JTEHM.2020.3029690.
  • N. Armanfard, M. Komeili, J. P. Reilly, J. F. Connolly, (2019), ``A Machine Learning Framework for Automatic and Continuous MMN Detection with Preliminary Results for Coma Outcome Prediction'', IEEE Journal of Biomedical and Health Informatics, vol.23, no. 4, pp. 1794-1804.
  • S. Chang, N. Armanfard, A. Q. Javaid, J. Boger, A. Mihailidis, (2018), `` Unobtrusive Detection of Orthostatic Hypotension using Ballistocardiogram '', IEEE Journal of Translational Engineering in Health and Medicine, DOI: 10.1109/JTEHM.2018.2864738.
  • N. Armanfard, J. P. Reilly, M. Komeili, (2018), ``Logistic Localized Modeling of the Sample Space for Feature Selection and Classification'', IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2017.2676101.
  • M. Komeili, N. Armanfard, D. Hatzinakos, (2018), ``Liveness Detection and Automatic Template Updating using Fusion of ECG and Fingerprint'', IEEE Transactions on Information Forensics & Security, DOI: 10.1109/TIFS.2018.2804890.
  • M. Komeili, W. Louis, N. Armanfard, D. Hatzinakos, (2018), ``Feature Selection for Non-Stationary Data: Application to Human Recognition using Medical Biometrics'', IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2017.2702059.
  • N. Armanfard, M. Komeili, A. Mihailidis, (2018), ``Development of a Smart Home-Based Package for Unobtrusive Physiological Monitoring'', Engineering in Medicine and Biology Society (EMBC), 40th Annual International Conference of the IEEE, Honolulu, HI, USA.
  • N. Armanfard, J. P. Reilly, M. Komeili, (2016), ``Local Feature Selection for Data Classification'', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 6, pp. 1217-1227. You can find its Codes here.
  • N. Armanfard, M. Komeili, E. Kabir, (2012), ``TED: A Texture-Edge Descriptor for Pedestrian Detection in Video Sequences'', Pattern Recognition, vol. 45, no.3, pp. 983-992.

  • PLACE TO TALK WITH ME

    • E-Mail: narges.armanfard[AT]mcgill.ca
    • Address: Room 623, Department of Electrical and Computer Engineering, McConnell Engineering Building, McGill University, 3480 Rue University, Montreal, QC., Canada H3A 0E9
    • Phone: 514-398-2939