Majid Komeili

I am an Assistant Professor at the School of Computer Science and Institute for Data Science at Carleton University. I perform fundamental and applied research in machine learning.

Prospective Students

We have an open position for a postdoc in NLP and AI Interpretibility. More information here.

Biography

Before joining Carleton, I was a postdoctoral fellow at University of Toronto working jointly with Toronto Rehabilitation Institute and Vector Institute. I received my Ph.D. from Department of Electrical and Computer Engineering at University of Toronto.

Email

firstname.lastname@carleton.ca

Address

5436 Herzberg Laboratories, Carleton University
1125 Colonel By Drive, Ottawa
Ontario, Canada, K1S 5B6

Phone

613-520-2600 ext. 6098

Research

Research Interests

My research interest is in machine learning, deep learning and related areas in computer vision and natural language processing. More specifically, I am interested in interpretable machine learning, deep neural networks and transfer learning. The goal is to create ML models with explainability in mind, or to develop methods that can decipher existing black-box ML models. I am also interested in how ML models can learn to perform new tasks with limitted amount of labeled data; a capability that human is very good at.

Current projects

  • Explainable AI and NLP to help better understand issues around misinformation and vaccine hesitancy in social media (collaboration with the Department of law and Legal Studies and the School of Journalism and Communication at Carleton U)
  • Explainable AI and NLP for assessment of functional limitations and disability services for postsecondary education (collaboration with the Read Initiative)
  • Deep learning and computer vision for communicating graphical information to visually impaired or blind individuals
  • Explainable AI for predictive analytics in employee benefits insurance
  • Explainable AI for analyzing EMR data (collaboration with the Institute of Mental Health Research, Ottawa)
  • Digital tools for revitalizing endangered languages (ELK-Tech)

Past projects

  • Biometrics, spoofing attacks and countermeasures
  • ML for analyzing brain signals (EEG)
  • ML for unobtrusive monitoring of vital physiologic parameters
  • ML for analyzing brain MRI images in patients with ASD

Students

Current Students

  • Seyed Omid Davoudi, PhD
  • Mohammad Mahdi Heydari Dastjerdi, MCS
  • Galen O'Shea, MCS
  • Mohammad Reza Zarei, PhD
  • Aatreyi Pranavbhai Mehta, MCS
  • Mitchell Chatterjee, MCS (from Fall 2022)
  • Rakshil Kevadiya, MCS (from Fall 2022)
  • Alireza Choubineh, MCS (from Fall 2022)
  • Hilaire Djani, Honors Thesis
  • Tim Elliott, Honors Project
  • Juntong He, Honors Project
  • Qixiang Luan, Honors Project
  • Past Grad Students

  • Mohammad Nokhbeh Zaeem, Winter 2021, MCS, moved on to SoundHound Inc
  • Siraj Ahmed, Fall 2020, MCS, U Ottawa, Co-supervised with Prof. J. Park, moved on to Braiyt AI Inc
  • Abhijeet Chauhan, 2020, MCS, moved on to IMRSV Data Labs
  • Past Undergrad Students

  • M. Kazman, Fall 2021, Honors Project.
  • A. Ong, Fall 2021, Honors Project.
  • J. Woo, Summer 2021, Honors Project.
  • I. Nicolaev, Summer 2021, Honors Project.
  • M. Kazman, Summer 2021, Honors Project.
  • J. Geng, Winter 2021, Honors Project.
  • Y. Song, Winter 2021, Honors Project.
  • K. Zhen, Winter 2021, Honors Project.
  • H. Le, Fall 2020, Honors Project.
  • Y. Gao, Fall 2020, Honors Project.
  • T. Cao, Fall 2020, Honors Project.
  • Y. Chen, Fall 2020, Honors Project.
  • V. Nguyen, Summer 2020, Honors Project.
  • J. Danovitch, Winter 2020, Honors Thesis.
  • M. Kuzmenko, Winter 2020, Honors Project.
  • L. Wise, Winter 2020, Honors Project.
  • L. Koftinow-Mikan, Fall 2019, Honors Project.
  • X. Liu, Fall 2019, Honors Project.
  • G. O'Shea, Summer 2019, Honors Project.
  • L. Colwell, Summer 2019, DSRI internship.
  • K. Causton, Summer 2019, Honors Project (with Oliver).
  • Y. Yamanaka, Winter 2019, Honors Project.
  • S. Kudolo, Winter 2019, Honors Project.
  • L. Gruska, Winter 2019, Honors Project.
  • L. He, Winter 2019, Honors Project.
  • Joining/Volunteering

    Applying for MSc or PhD:

    MSc and PhD applicants who are interested in my research are encouraged to contact me via email.

    Prerequisites: A good candidate should have background in probability and linear algebra, and have had courses in Machine Learning or related areas including Computer Vision and Natural Language Processing.

    Prospective MSc and PhD students who are applying to the School of Computer Science at Carleton University and are interested in my research are encouraged to indicate my name as their preferred research supervisor.

    Please note that due to the volume of emails I receive, I am not able to respond to all.

    Undergrad students at Carleton University who are interested in doing their Honours project/thesis with me, are encouraged to contact me via email.

    Publications

    Selected Publications

      MR. Zarei, M. Komeili, (2022), “Interpretable Concept-based Prototypical Networks for Few-Shot Learning”, ICIP, pdf

      O. Davoudi, M. Komeili, (2022), “Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space”, ICLR, pdf

      M. Nokhbeh Zaeem, M. Komeili, (2022), “Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks”, IEEE Transactions on Neural Networks and Learning Systems, under review preprint

      O. Davoudi, M. Komeili, (2021), “Feature-Based Interpretable Reinforcement Learning based on State-Transition Models”, IEEE International Conference on Systems, Man, and Cybernetics, pdf

      K. C. Fraser, M. Komeili, (2021), “Measuring Cognitive Status from Speech in a Smart Home Environment”, IEEE Instrumentation and Measurement Magazine, pdf.

      M. Nokhbeh Zaeem, M. Komeili, (2021), “Cause and Effect: Concept-based Explanation of Neural Networks”, IEEE International Conference on Systems, Man, and Cybernetics, pdf, code

      A. Chauhan, O. Davoudi M. Komeili, (2021), “Multi-scale Deep Nearest Neighbors”, International Joint Conference on Neural Networks. pdf

      M. Komeili, N. Armanfard, D. Hatzinakos, (2020), “Multiview Feature Selection for Single-view Classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence. pdf, code

      M. Komeili, C. Pou-Prom, D. Liaqat, K. C. Fraser, M. Yancheva, F. Rudzicz, (2019), “Talk2Me: Automated Linguistic Data Collection for Personal Assessment”, PLoS One, pdf, code.

      N Armanfard, M. Komeili, JP Reilly, J Connoly, (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, issue 4, pp. 1794 -1804.

      S. J. Haghighi, M. Komeili, D. Hatzinakos, H. El-Beheiry, (2018), “40-Hz ASSR for Measuring Depth of Anaesthesia During Induction Phase”, IEEE Journal of Biomedical and Health Informatics, vol. 22, issue 6, pp. 1871 - 1882.

      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, vol. 13, issue 7, pp. 1810 - 1822. pdf.

      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, vol. 29, issue 5, pp. 1396 - 1413.

      N. Armanfard, M. Komeili, A. Mihailidis (2018), “Development of a Smart Home Package for Unobtrusive physiological Monitoring”, IEEE. 40th International Engineering in Medicine and Biology Conference, United States.

      A. Kushki, M. Komeili, S. Panahandeh, E. Anagnostou, J. Lerch. (2018), “Examining Associations Between Brain Morphology and Social Function in ASD, ADHD, OCD, and typical development using Machine Learning: Analysis of POND Network Data”, INSAR 2018, International Society for Autism Research, Netherlands.

      M. Komeili, W. Louis, N. Armanfard, D. Hatzinakos, (2017), “Feature Selection for Non-stationary Data: Application to Human Recognition using Medical Biometrics”, IEEE Transactions on Cybernetics, vol. 48, issue 5, pp. 1446 - 1459. pdf

      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.

      W. Louis, M. Komeili, D. Hatzinakos, (2016), “Continuous Authentication using One Dimensional Multi-Resolution Local Binary Patterns”, IEEE Transactions on Information Forensics & Security, vol. 11, no. 12, pp 2818-2832. pdf

      S. J. Haghighi, M. Komeili, D. Hatzinakos, (2016), “Predicting the Depth of Anaesthesia with 40-Hz ASSR”, IEEE 29th Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, Canada.

      N. Armanfard, M. Komeili, J. P. Reilly, John F. Connolly, (2016), “Automatic and continuous assessment of ERPs for Mismatch Negativity detection”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), , Orlando, FL, USA.

      M. Komeili, W. Louis, N. Armanfard, D. Hatzinakos, (2016), “Human Recognition using Electrocardiogram Signals: From Rest to Exercise”, IEEE 29th Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, Canada.

      N. Armanfard, M. Komeili, J. P. Reilly, L. Pino, (2016), “Vigilance lapse identification using sparse EEG electrode arrays”, IEEE 29th Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, Canada.

      W. Louis, M. Komeili, D. Hatzinakos, (2016), “Real-time Heartbeat Outlier Removal in Electrocardiogram (ECG) Biometric System”, Electrical and Computer Engineering (CCECE), IEEE 29th Canadian Conference on, Vancouver, Canada.

      M. Komeili, N. Armanfard, D. Hatzinakos, A. N. Venetsanopoulos, (2015), “Feature Selection from Multisession Electrocardiogram Signals for Identity Verification”, Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on, Halifax, Canada.

      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.

    Patents

      M. Komeili, N. Armanfard, D. Hatzinakos, “An Expert System for Fingerprint Spoof Detection” International application number CA2019050141, Patent Cooperation Treaty (PCT), Feb. 2019.

      N. Armanfard, M. Komeili, J. P. Reilly, John F. Connolly, “Expert System for Automatic, Continuous Coma Patient Assessment and Outcome Prediction” U.S. Provisional Patent, USPTO serial no. 62/509,986, May 2017.

       

    Contact

    Address:
    5436 Herzberg Laboratories,
    Carleton University
    1125 Colonel By Drive, Ottawa
    Ontario, Canada, K1S 5B6

    Phone: 613-520-2600 ext. 6098

    Email: firstname.lastname@carleton.ca