Curriculum Vitae

Education (LinkedIn profile)

• Ph.D. (Electrical and Computer Engineering), Carleton University, Ontario, Canada (Advisors: Rafik Goubran, Chang Shu)

  • Thesis: A Deep Representation Learning Framework for Medical Imaging Data Analysis

• M.C.S. (Computer Science), University of Ottawa, Ontario, Canada (Advisors: WonSook Lee, Chang Shu)

  • Thesis: A PCA-Based Approach to the 3D Reconstruction of Human Body from Single Frontal-View Silhouette

Work Experience

• Research Officer, National Research Council of Canada (2007 - present)

• Part-time Professor, University of Ottawa, Canada (2024)

• Assistant Professor (Adjunct), University of Waterloo, Canada (2020 - 2023)

• Research Professor (Adjunct), Carleton University, Canada (2022 - 2025)

Awards

• External award:

  • US CDC Alice Hamilton Award for Occupational Health and Safety (Honorable Mention, 2014)

• Internal awards:

  • NRC-DT Value for Canada (2020)

  • NRC Inventor Fixed Milestone Awards (2020)

  • NRC-DT Research Excellence (2020)

  • Long Service Award for public service (2017)

  • NRC-ICT Certificate of Excellence (2016)

  • NRC-ICT Award for outstanding client services (2015)

  • NRC-ICT Award for achieving top results in an international contest (2014)

  • NRC Innovative IT research, programs and projects (2013)

  • NRC-IIT Outstanding Achievers Award (2008)

• Outstanding contribution in reviewing:

  • IEEE Transactions on Instrumentation and Measurement  (2019, 2020, 2021)

  • Pattern Recognition (Elsevier, October 2018)

  • Computer-Aided Design (Elsevier, August 2018)

• Volunteer awards:

  • Active Volunteer award by Let's Talk Science (2006)

  • Outstanding Volunteer award by Let's Talk Science (2005)

Services and Outreach (recent)

• Reviewer for international parties

  • The Croatian Science Foundation (HRZZ), 2025

• Editorial board

  • Associate Editor for IEEE Canadian Journal of Electrical and Computer Engineering

  • Guest editor for Journal of Sensors, Journal of Imaging

  • Associate Editor, Frontiers in Oncology

• Technical program committee:

  • MVA 2023

  • VISAPP 2022

  • VISAPP 2021, CASA 2021, HeathInfo 2021

  • 3D Vision 2020

  • 3DRW 2019 (ICCV Workshop)

  • 3DRW 2018 (ECCV Workshop)

• Reviewer for conferences:

  • 2025: AI4VA, 3DV, MICCAI, MVA

  • 2024: 3D Vision

  • 2023: MICCAI, MVA

  • 2022: MICCAI, 3D Vision

  • 2021: MICCAI, MVA, 3D Vision

  • 2020: MIDL, MICCAI

  • 2019: MVA, 3D Vision, 3DRW

  • 2018: MICCAI, ICML, 3D Vision, ECCV, 3DRW

  • 2017: Pacific Graphics, 3D Vision, MICCAI

• Reviewer for journals:

  • IEEE:

    • Access

    • Trans. Biomedical Engineering

    • Trans. Instrumentation & Measurement ("Outstanding Reviewers of 2019 ~ 2021")

  • Elsevier:

    • Artificial Intelligence In Medicine

    • Automation in Construction

    • Biomedical Signal Processing and Control

    • Computers in Biology and Medicine

    • Computer Aided Design ("Outstanding Reviewers of 2018")

    • Computer Methods and Programs in Biomedicine

    • Computers & Graphics

    • Expert Systems with Applications

    • Informatics in Medicine Unlocked

    • Measurement

    • Pattern Recognition ("Outstanding Reviewers of 2018")

    • Pattern Recognition Letters

  • Springer Nature:

    • BMC BioInformatics (2020 - )

    • BMC Supplements (2020 - )

    • Journal of Computer Science and Technology

    • Physical and Engineering Sciences in Medicine (2021 - )

  • Oxford University Press:

    • JAMIA: Journal of the American Medical Informatics Association 

  • MDPI:

    • Sensors

    • Electronics

  • Wiley:

    • Aging Medicine

  • IEICE:

    • Trans. Information and Systems

  • Hindawi:

    • Computational and Mathematical Methods in Medicine

  • InderScience:

    • International Journal of Signal and Imaging Systems Engineering (IJSISE) 

• Reviewer for external programs:

  • BDC Capital’s Deep Tech Venture Fund, March 2022 

  • Deep Space Healthcare Challenge, January 2022 

  • Deep Space Food Challenge (organized by Canadian Space Agency) 2021 

  • NSERC Discovery Grants program, November 2021 

• Reviewer for internal programs:

  • NRC Ideation Initiatives 2020 

  • NRC PDF program 2020

• Assessment for Creative Destruction Lab (CDL)  at the University of Toronto:

  • 2021 on AI, computer vision and robotics startups

  • 2020 on AI startups

  • 2019 on AI for medical imaging, audio and video startups

  • 2018 on Computer Vision and AI startups

Student supervising

1. K. Habashy (master, U. Waterloo, winter and summer 2025), topic: motion generation

2. J. Li (undergraduate, U. Waterloo, summer 2025), topic: motion generation with AI

3. B. Chharawala (undergraduate, U. Waterloo, summer 2025), topic: intelligent robotics

4. S. Liu (undergraduate, U. Waterloo, winter 2025), topic: robotics and AI

5. S. Yang (undergraduate, U. Waterloo, winter 2025), topic: robotics and AI

6. N. Madadi (undergraduate, U. Waterloo, fall 2024), topic: robotics

7. J. Kesan (undergraduate, U. Waterloo, summer 2024), topic: multi-modal AI, video retrieval, action recognition

8. L. Murray (undergraduate, U. Waterloo, summer 2024), topic: robotics

9. Z. Cook (undergraduate, U. Waterloo, summer 2024), topic: robotics, EEG analysis, GenerativeAI, XAI

10. E. Gu (undergraduate, Dalhousie U., winter 2024), topic: AI in audio signal processing, computer vision

11. S. Li (undergraduate, U. Waterloo, winter 2024), topic: GenAI in audio, computer vision 

12. A. Deshpande (undergraduate, U. Waterloo, winter 2024), topic: intelligent robotics, multi-modal DL

13. C. Zhao (undergraduate, U. Waterloo, winter 2024), topic: EEG analysis, GenAI, multi-modal DL

14. Z. Cook (undergraduate, U. Waterloo, fall 2023), topic: EEG analysis, GenerativeAI, XAI

15. J. Wang (undergraduate, U. Waterloo, fall 2023), topic: multi-modal deep learning

16. C. Zhao (undergraduate, U. Waterloo, summer 2023), topic: multi-modal deep learning and integration

17. G. Sinha (undergraduate, U. Waterloo, summer 2023), topic: Deep learning for EEG analysis

18. S. Li (Undergraduate, University of Waterloo, summer 2023), topic: Deep learning for IoT applications

19. K. Parmar (Undergraduate, University of Waterloo, winter 2023), topic: Deep learning for food and medical imaging

20. Z. Gu (Master student, McMaster University, winter 2023), topic: Deep learning for EEG analysis

21. Y. Shen (Undergraduate, University of Waterloo, winter 2023), topic: Deep learning for audio data analysis

22. K. Habashy (Undergraduate, University of Waterloo, fall 2022), topic: audio data analysis and deep learning

23. Z. Zhang (Undergraduate, McMaster U., fall 2022), topic: Deep learning for audio data analysis

24. G. Sinha (Undergraduate, U. of Waterloo, fall 2022), topic: Machine learning for nutrition intaking tracking

25. K. Habashy (Undergraduate, University of Waterloo, Summer 2022), topic: audio data analysis and neurological data analysis with deep learning

26. K. Habashy (University of Waterloo, Winter 2022), topic: audio data analysis with deep learning for Aging in Place

27. K. Ma (Undergraduate, University of Waterloo, Winter 2022), topic: deep learning for COVID-Net project, self-supervised learning

28. S. He (Undergraduate, University of Waterloo, Fall 2021), topic: self-supervised deep learning, neural architecture

29. J. Zhang (Undergraduate, University of Waterloo, Summer 2021), topic: deep learning for COVID-Net, few shot learning, self-supervised deep learning

30. A. Shim (Undergraduate, University of Waterloo, Winter 2021), topic: deep learning for COVID-Net and Cough Analysis projects

31. H. Azimi (Volunteer Visitor from Univ. of Ottawa, Fall 2020), topic: deep learning for a USDA project on fungal infection of wheat

32. J. Zhang (Undergraduate, University of Waterloo, Fall 2020), topic: deep learning for COVID-Net and Cough Analysis projects

33. H. As'Ad (Ph.D. candidate, University of Ottawa, Summer 2020), topic: Geometric deep learning and few shot learning

34. H. Azimi (Ph.D. candidate, University of Ottawa, Summer 2020), topic: Deep object detectors and few shot learning

35. W. Zhang (Graduate, McGill University, Winter 2020), topic: deep machine learning for data analytics

36. G. Rouhafzay (Ph.D. candidate, University of Ottawa, Summer and Fall 2019), topic: deep learning for medical data analysis

37. A. Kerkmann (Undergraduate, University of Ottawa, Winter 2019), topic: deep learning for visual understanding

38. D. Salem (Graduate, University Of Ottawa, Fall 2018), topic: deep learning for image segmentation

39. M. Kuang (Undergraduate, Carleton University, Summer 2018), topic: deep learning for image classification

40. W. Xin (Undergraduate, University of Ottawa, Winter 2018), topic: 3D data analytics