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Parvaneh Saeedi, Ph.D., P.Eng.

Email: psaeedi@sfu.ca

Phone: +1 (778) 782-4746

School of Engineering Science

Simon Fraser University

8888 University Dr., Burnaby

BC, V5A 1S6, Canada

Office: ASB10837

Biography

Dr. Parvaneh Saeedi is a Professor of Engineering Science with expertise in artificial intelligence, computer vision, and medical image analysis. Her research focuses on developing robust and interpretable machine learning methods for healthcare, biomedical imaging, and geospatial applications. She has authored many peer reviewed publications in leading journals and conferences, including various IEEE Transactions, Medical Image Analysis, MICCAI, CVPR, and ICIP. Her work has contributed to advances in embryo and fetal health assessment, privacy preserving learning, and remote sensing image understanding.

She served as Associate Dean of Research and Graduate Studies for the Faculty of Applied Sciences at Simon Fraser University from 2019 to 2024, where she provided faculty wide leadership in research planning and graduate education. In that role, she oversaw graduate program development, coordinated closely with university level offices, and addressed complex academic and administrative issues affecting students and supervisors. During her term, Professor Saeedi contributed directly to the design and implementation of new graduate programs and led strategic initiatives that strengthened research capacity and fostered interdisciplinary collaboration across the faculty. Her tenure coincided with the COVID 19 pandemic, during which she served the faculty and school with exceptional dedication and collaborative leadership, navigating rapidly evolving governance and safety requirements while mitigating disruptions to research productivity and graduate training.

Dr. Saeedi has received competitive fundings and awards from national and international agencies and actively collaborates with clinicians, hospitals, and industry partners. She was the recepient of NSERC's University Faculty Award from 2007 to 2012. She is committed to mentoring highly qualified personnel and translating research outcomes into real world impact.

Professor Saeedi is a long-standing advocate for equity and inclusion, having served as Women in Engineering faculty mentor since 2012 and as Chair of Equity & Diversity Committee in the School of Engineering Science, where she actively supports women and underrepresented groups in engineering. She is an avid supporter for animal protection and welfare. Since 2000, she has been actively involved as a Vision Mate with the Canadian National Institute for the Blind (CNIB), contributing to community support and accessibility for individuals living with vision loss in Canada.

Research

Her research focuses on the development of scalable, privacy aware, and robust artificial intelligence systems that operate effectively in real world, data distributed environments. Spanning collaborative intelligence, federated and split learning, medical image and video analysis, and unsupervised representation learning, the work addresses fundamental challenges in deploying AI beyond centralized settings, including communication constraints, system heterogeneity, limited supervision, and privacy risks. By combining principled algorithm design with system level considerations, the research develops methods that enable reliable collaboration across institutions while preserving data confidentiality and model performance. Collectively, these contributions support the deployment of trustworthy and efficient AI technologies in healthcare and other high impact domains, aligned with national priorities in responsible artificial intelligence, health innovation, and equitable access to advanced computational tools.

Overview of privacy preserving collaborative intelligence for medical imaging

Collaborative Intelligence for Medical Imaging

Collaborative intelligence enables the distribution of artificial intelligence computation across multiple devices and institutions, offering a scalable and privacy aware pathway for deploying medical AI beyond centralized data centers and closer to the point of care. This research focuses on the design of communication efficient, privacy preserving, and robust collaborative intelligence frameworks for medical image analysis. The work addresses key limitations of centralized and conventional federated learning, including data sharing constraints, unreliable communication, client heterogeneity, and deployment in real clinical environments. The main objective is to integrate split learning, federated optimization, feature compression, and error resilience mechanisms to enable scalable and trustworthy AI across distributed healthcare institutions while maintaining patient data privacy.

Overview of privacy preserving collaborative intelligence for medical imaging

Unsupervised Video Summarization

Video summarization aims to automatically distill long videos into concise representations that preserve the most informative and relevant content, enabling efficient understanding, retrieval, and analysis of large scale video data. In this research work, we are aiming to generate concise and informative summaries of medical data without relying on human annotations. The approach leverages reinforcement learning and transformer based models, using video reconstruction quality as a learned reward signal to identify the most salient frames. By learning directly from raw data, the framework avoids hand crafted heuristics and unstable adversarial training while preserving essential video content.

Overview of privacy preserving collaborative intelligence for medical imaging

Robust Learning for Biomedical Image Segmentation

This research advances principled approaches to biomedical image segmentation by focusing on how models learn reliable and clinically meaningful representations from complex, imperfect, and heterogeneous medical imaging data. By treating segmentation as an adaptive learning process rather than a purely label driven task, the work emphasizes robustness, generalization, and the structured use of feedback to improve boundary awareness and reduce sensitivity to annotation noise and domain variability. The overarching goal is to develop segmentation frameworks that remain reliable across diverse imaging settings while supporting scalable, privacy preserving, and clinically relevant deployment.

Current Students and HQP

Postdoctoral Fellows

PhD Students

Undergraduate Students

Teaching

Courses taught at Simon Fraser University.

Publications

Papers under Review

  1. C. Shiranthika, H. Hadizadeh, and P. Saeedi, “DiffusedSplitFed: Latent Diffusion and global feature fusion meet Split Federated medical image segmentation,” submitted to IEEE Transactions on Artificial Intelligence, Nov. 2025.
  2. Z. H. Kafshgari, H. Hadizadeh, and P. Saeedi, “ResFormer: A CNN-Transformer Model Fusion with Self-distilled Spatial Attention for Enhanced Image Segmentation,” submitted to IEEE Journal of Biomedical and Health Informatics (JBHI), Dec. 2025.
  3. Z. H. Kafshgari, H. Hadizadeh, and P. Saeedi, "SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels," submitted to 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), Nov. 2025.

Journal Papers

  1. M. Abbasi, H. Hadizadeh, and P. Saeedi, “Reinforcement Learning for Unsupervised Video Summarization with Reward Generator Training,” IEEE Transactions on Circuits and Systems for Video Technology, pp. 1–14, Oct. 2025 (Impact Factor: 11.1).
  2. C. Shiranthika, Z. H. Kafshgari, H. Hadizadeh, and P. Saeedi, “MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation,” arXiv:2503.20830, accepted for Special Issue: Medical Imaging Analysis: Current and Future Trends in Biosignal Processing, 2025 (Impact Factor: 3.7).
  3. C. Shiranthika, H. Hadizadeh, P. Saeedi, and I. V. Bajić, “Adaptive Asynchronous Split Federated Learning for Medical Image Segmentation,” IEEE Access, Nov. 2024 (Impact Factor: 3.9).
  4. M. Abbasi and P. Saeedi, “Enhancing Multivariate Time Series Classifiers through Self Attention and Relative Positioning Infusion,” IEEE Access, vol. 12, pp. 67273–67290, 2024 (Impact Factor: 3.9).
  5. C. Shiranthika, P. Saeedi, and I. V. Bajić, “Decentralized Learning in Healthcare: A Review of Emerging Techniques,” IEEE Access, vol. 11, pp. 54188–54209, 2023 (Impact Factor: 3.9).
  6. L. Seeholzer, T. Krammer, P. Saeedi, and K. Wegener, “Analytical Model for Predicting Tool Wear in Orthogonal Machining of Unidirectional Carbon Fiber Reinforced Polymer (CFRP),” The International Journal of Advanced Manufacturing Technology, vol. 119, pp. 7259–7289, 2022 (Impact Factor: 3.4).
  7. S. Mohajerani and P. Saeedi, “Pragmatic Augmentation Algorithms for Deep Learning Based Cloud and Cloud Shadow Detection in Remote Sensing Imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2021 (Impact Factor: 4.8).
  8. S. Mohajerani and P. Saeedi, “Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1–12, 2021 (Impact Factor: 3.827).
  9. S. Mohajerani and P. Saeedi, “Pragmatic Augmentation Algorithms for Deep Learning Based Cloud and Cloud Shadow Detection in Remote Sensing Imagery,” IEEE Geoscience and Remote Sensing Letters, pp. 1–5, 2021 (Impact Factor: 3.833).
  10. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Trophectoderm Segmentation in Human Embryo Images via Inceptioned U Net and Multi Scaled Ensembling,” Medical Image Analysis, pp. 1–14, 2020 (Impact Factor: 5.356).
  11. J. K. M. Au, M. Tian, R. M. Rad, P. Saeedi, and J. C. Havelock, “Automatic Image Segmentation and Quantitative Component Measurements on Human Blastocyst Images Using Artificial Intelligence in Assessing Morphology Grading,” Fertility and Sterility, vol. 114, no. 3, pp. e145–e146, 2020.
  12. S. Mohajerani and P. Saeedi, “Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples,” IEEE Transactions on Image Processing, pp. 1–12, 2019 (Impact Factor: 5.071).
  13. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Cell Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution,” IEEE Access, pp. 1–11, 2019 (Impact Factor: 3.557).
  14. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “A Hybrid Approach for Multiple Blastomeres Identification in Early Human Embryo Images,” Computers in Biology and Medicine, vol. 101, pp. 100–111, 2018 (Impact Factor: 3.434).
  15. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Human Blastocyst Zona Pellucida Segmentation via Boosting Ensemble of Complementary Learning,” Informatics in Medicine Unlocked, vol. 13, pp. 112–121, 2018 (Impact Factor: 3.038).
  16. P. Saeedi, D. Yee, J. Au, and J. Havelock, “Automatic Identification of Human Blastocyst Components via Texture,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 12, pp. 2968–2978, Dec. 2017 (Impact Factor: 4.288).
  17. A. Singh, J. Au, P. Saeedi, and J. Havelock, “Automatic Segmentation of Trophectoderm in Microscopic Images of Human Blastocysts,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 1, pp. 382–392, Jan. 2015 (Impact Factor: 3.84).
  18. M. Cote and P. Saeedi, “Hierarchical Image Segmentation Using a Combined Geometrical and Feature Based Approach,” Journal of Data Analysis and Information Processing, vol. 2, no. 4, pp. 117–136, Nov. 2014 (Impact Factor: 1.24).
  19. P. Saeedi and M. Mao, “Two Edge Corner Image Features for Registration of Geospatial Images with Large View Variations,” International Journal of Geosciences, vol. 5, pp. 1324–1344, Oct. 2014 (Impact Factor: 1.03).
  20. M. Cote and P. Saeedi, “Automatic Rooftop Extraction in Nadir Aerial Imagery of Suburban Regions Using Corners and Variational Level Set Evolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 313–328, Jan. 2013 (Impact Factor: 4.39).
  21. M. Izadi and P. Saeedi, “Robust Weighted Graph Transformation Matching for Rigid and Non Rigid Image Registration,” IEEE Transactions on Image Processing, vol. 21, no. 10, pp. 4369–4382, Oct. 2012 (Impact Factor: 4.288).
  22. M. Izadi and P. Saeedi, “3D Polygonal Building Detection in Monocular Satellite Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 6, pp. 2254–2272, June 2012 (Impact Factor: 5.83).
  23. H. Badakhshannoory and P. Saeedi, “A Model Based Validation Scheme for Organ Segmentation in CT Scan Volumes,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, pp. 2681–2693, Sept. 2011 (Impact Factor: 2.278).
  24. Y. M. Chen, I. V. Bajić, and P. Saeedi, “Moving Region Segmentation from Compressed Video Using Global Motion Estimation and Markov Random Fields,” IEEE Transactions on Multimedia, June 2011 (Impact Factor: 4.32).
  25. R. Wong, S. Flibotte, R. Corbett, P. Saeedi, S. Jones, M. Marra, J. Schein, and I. Birol, “LaneRuler: Automated Lane Tracking for DNA Electrophoresis Gel Images,” IEEE Transactions on Automation Science and Engineering, Nov. 2009 (Impact Factor: 2.08).
  26. P. Saeedi, P. D. Lawrence, and D. G. Lowe, “Vision Based 3D Trajectory Tracking for Unknown Environments,” IEEE Transactions on Robotics, vol. 22, no. 1, pp. 119–136, Feb. 2006 (Impact Factor: 3.81).
  27. P. Saeedi, P. D. Lawrence, D. G. Lowe, P. Jacobsen, D. Kusalovic, and K. Ardron, “An Autonomous Excavator with Vision Based Track Slippage Control,” IEEE Transactions on Control Systems Technology, vol. 13, no. 1, pp. 67–84, Jan. 2006 (Impact Factor: 1.021).
  28. D. R. Fuhrmann, M. Kryzywinski, R. Chiu, P. Saeedi, J. Schein, I. Bosdet, A. Chinwalla, L. Hillier, R. H. Waterston, S. Jones, and M. Marra, “Software for Automated Analysis of DNA Fingerprinting Gels,” Genome Research, vol. 13, no. 5, pp. 940–953, May 2003 (Impact Factor: 9.97).
  29. S. G. Gregory, M. Sekhon, J. Schein, S. Zhao, K. Osoegawa, C. E. Scott, R. S. Evans, P. W. Burridge, T. V. Cox, C. A. Fox, R. D. Hutton, I. R. Mullenger, K. J. Phillips, J. Smith, J. Stalker, G. J. Threadgold, E. Birney, K. Wylie, A. Chinwalla, J. Wallis, L. Hillier, J. Carter, T. Gaige, S. Jaeger, C. Kremitzki, D. Layman, J. Maas, R. Mcgrane, K. Mead, R. Walker, S. Jones, M. Smith, J. Asano, I. Bosdet, S. Chan, s chittaranjan, R. Chiu, C. Fjell, D. Fuhrmann, N. Girn, C. Gray, R. Guin, L. Hsiao, M. Krzywinski, R. Kutsche, S. Lee, C. Mathewson, C. Mcleavy, S. Messervier, S. Ness, P. Pandoh, A. Prabhu, P. Saeedi, D. Smailus, J. Stott, W. Terpstra, M. Tsai, J.vardy, N. Wye, G. Yang, S. Shatsman, B. Ayodeji, K. Geer, G. Tsegaye, A. Shvartsbeyn, E. Gebregeorgis, M. Krol, D. Russell, L. Overton, J. Malek, M. Holmes, M. Heaney, J. Shetty, T. Feldblyum, W. C. Nierman, J. J. Catanese, T. Hubbard, R. H. Waterston, J. Rogers, P. J. De jong, C. M. Fraser, M. Marra, J. D. Mcpherson & D. R. Bentley, "A physical map of the mouse genome," Nature Genetics, 418 ,(743-750), Aug 15, 2002 (Impact Factor: 14.67).

Conference Papers

  1. C. Shiranthika, H. Hadizadeh, P. Saeedi, and I. V. Bajić, “SplitFedZip: Learned Compression for Data Transfer Reduction in Split Federated Learning,” The FLUID Workshop at AAAI, 2025 (Qualis: A1).
  2. C. Shiranthika, H. Hadizadeh, I. V. Bajić, and P. Saeedi, “Adaptive Asynchronous Split Federated Learning for Medical Image Segmentation” WiML Symposium, co located with ICML 2025, Vancouver, BC, Canada, July. 2025 (Qualis: A1).
  3. Z. H. Kafshgari, H. Hadizadeh, and P. Saeedi, “Robust Split Federated Learning Model Using Co Learning Mechanism for Medical Image Segmentation,” 19th Workshop for Women in Machine Learning (WiML), co located with NeurIPS, Vancouver, BC, Canada, Dec. 2024 (Qualis: A1).
  4. C. Shiranthika, H. Hadizadeh, I. V. Bajić, and P. Saeedi, “Enhancing Communication Efficiency and Robustness in Split Federated Learning with Rate Distortion,” 19th Workshop for Women in Machine Learning (WiML), co located with NeurIPS, Vancouver, BC, Canada, Dec. 2024 (Qualis: A1).
  5. C. Shiranthika, P. Saeedi, and I. V. Bajić, “Optimizing Split Points for Error Resilient SplitFed Learning,” Women in Computer Vision (WiCV) Workshop at IEEE CVPR, Seattle, USA, June 2024 (Qualis: A1).
  6. C. Shiranthika, Z. H. Kafshgari, P. Saeedi, and I. V. Bajić, “SplitFed Resilience to Packet Loss: Where to Split, That Is the Question,” International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 367–377, 2023 (Qualis: A1).
  7. C. Shiranthika, Z. H. Kafshgari, P. Saeedi, and I. V. Bajić, “SplitFed Resilience to Packet Loss: Where to Split, That Is the Question,” International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Dec. 2023.
  8. M. Abbasi and P. Saeedi, “Adopting Self Supervised Learning into Unsupervised Video Summarization through Restorative Score,” IEEE International Conference on Image Processing (ICIP), pp. 425–429, 2023 (Qualis: A1).
  9. M. Abbasi and P. Saeedi, “Time Series Classification for Modality Converted Videos: A Case Study on Predicting Human Embryo Implantation from Time Lapse Images,” International Workshop on Multimedia Signal Processing (MSSP), pp. 1–5, 2023 (Qualis: B2).
  10. Z. H. Kafshgari, I. V. Bajić, and P. Saeedi, “Smart Split Federated Learning over Noisy Channels for Embryo Image Segmentation,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5, 2023 (Qualis: A1).
  11. Z. H. Kafshgari, C. Shiranthika, P. Saeedi, and I. V. Bajić, “Quality Adaptive Split Federated Learning for Segmenting Medical Images with Inaccurate Annotations,” IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1–5, 2023 (Qualis: B1).
  12. N. Qaderi and P. Saeedi, “A Gated Deep Model for Single Image Super Resolution Reconstruction,” IEEE International Workshop on Multimedia Signal Processing (MSSP), pp. 1–5, 2022 (Qualis: B1).
  13. M. Abbasi Boroujeni, P. Saeedi, J. Au, and J. Havelock, “Timed Data Incrementation: A Data Regularization Method for IVF Implantation Outcome Prediction from Length Variant Time Lapse Image Sequences,” IEEE International Workshop on Multimedia Signal Processing (MSSP), pp. 1–5, 2021 (Qualis: B2).
  14. M. Abbasi, P. Saeedi, J. Au, and J. Havelock, “A Deep Learning Approach for Prediction of IVF Implantation Outcome from Day 3 and Day 5 Time Lapse Human Embryo Image Sequences,” IEEE International Conference on Image Processing (ICIP), pp. 289–293, 2021 (Qualis: A1).
  15. L. Lockhart, P. Saeedi, J. Au, and J. Havelock, “Automating Embryo Development Stage Detection in Time Lapse Imaging with Synergic Loss and Temporal Learning,” International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 541–549, 2021 (Qualis: A1).
  16. L. Lockhart, P. Saeedi, J. Au, and J. Havelock, “Human Embryo Cell Centroid Localization and Counting in Time Lapse Sequences,” IEEE International Conference on Pattern Recognition (ICPR), pp. 1–5, 2020 (Qualis: A2).
  17. S. Mohajerani, M. S. Drew, and P. Saeedi, “Illumination Invariant Image from Four Channel Images: The Effect of Near Infrared Data in Shadow Removal,” London Imaging Meeting (LIM), pp. 1–5, 2020.
  18. J. Au, M. Tian, R. Moradi, P. Saeedi, and J. Havelock, “Automatic Image Segmentation and Quantitative Component Measurements on Human Blastocyst Images Using Artificial Intelligence in Assessing Morphology Grading and Predicting Implantation and Live Birth Outcomes,” Fertility and Sterility, 2020.
  19. S. Arasteh, M. Tayebi, Z. Zohrevand, U. Glässer, P. Saeedi, and H. Wehn, “Fishing Vessels Activity Detection from Longitudinal AIS Data,” International Conference on Advances in Geographic Information Systems, pp. 347–356, 2020 (Qualis: A1).
  20. L. Lockhart, P. Saeedi, J. Au, and J. Havelock, “Multi Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data,” IEEE International Workshop on Multimedia Signal Processing, pp. 1–5, 2019 (Qualis: B2).
  21. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Blast Net: Semantic Segmentation of Human Blastocyst Components via Cascaded Atrous Pyramid and Dense Progressive Upsampling,” IEEE International Conference on Image Processing (ICIP), pp. 1–5, 2019 (Qualis: A1). Best Student Work Award Nominee.
  22. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Predicting Human Embryos’ Implantation Outcome from a Single Blastocyst Image,” IEEE Engineering in Medicine and Biology Society Conference (EMBS), pp. 1–5, 2019 (Qualis: B1).
  23. S. Mohajerani, R. Asad, K. Abhisek, N. Sharma, A. Duynhoven, and P. Saeedi, “CLOUDMASKGAN: A Content Aware Unpaired Image to Image Translation Algorithm for Remote Sensing Imagery,” IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, pp. 1–5, 2019 (Qualis: A1).
  24. S. Mohajerani and P. Saeedi, “Cloud Net: An End to End Cloud Detection Algorithm for Landsat 8 Imagery,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, pp. 1–5, 2019 (Qualis: B2).
  25. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Blastomere Cell Counting and Centroid Localization in Microscopic Images of Human Embryo,” IEEE International Workshop on Multimedia Signal Processing, Vancouver, Canada, pp. 1–6, 2018 (Qualis: B2).
  26. R. M. Rad, P. Saeedi, J. Au, and J. Havelock, “Multi Resolutional Ensemble of Stacked Dilated U Net for Inner Cell Mass Segmentation in Human Embryonic Images,” IEEE International Conference on Image Processing (ICIP), pp. 3518–3522, 2018 (Qualis: A1).
  27. S. Mohajerani, T. A. Krammer, and P. Saeedi, “A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks,” IEEE International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, pp. 1–5, 2018 (Qualis: B2).
  28. S. Mohajerani and P. Saeedi, “CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images,” IEEE International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, pp. 1–5, 2018 (Qualis: B2).
  29. F. S. Mirshahi and P. Saeedi, “A Dual Path Deep Network for Single Image Super Resolution Reconstruction,” IEEE International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, pp. 1–5, 2018 (Qualis: B2).
  30. T. A. Krammer and P. Saeedi, “Improving Landsat 8 Cloud Detection Algorithms via a New Snow Identification and Separation Algorithm,” World Congress in Computer Science, Computer Engineering, and Applied Computing, July 2018 (Qualis: B5).
  31. R. Moradi Rad, P. Saeedi, J. Au, and J. Havelock, “Coarse to Fine Texture Analysis for Inner Cell Mass Identification in Human Blastocyst Microscopic Images,” IEEE International Conference on Image Processing Theory, Tools and Applications, Montreal, Canada, pp. 1–5, Nov. 2017 (Qualis: B2).
  32. Sh. Kheradmand, A. Singh, P. Saeedi, J. Au, and J. Havelock, “Inner Cell Mass Segmentation in Human HMC Embryo Images Using Fully Convolutional Network,” IEEE International Conference on Image Processing, China, pp. 1–5, Sept. 2017 (Qualis: A1).
  33. T. Horita and P. Saeedi, “Automatic Cloud Detection and Verification in Satellite Images,” International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), Las Vegas, USA, pp. 1–4, July 2017 (Qualis: B5).
  34. R. Moradi Rad, P. Saeedi, and I. V. Bajić, “Automatic Cleavage Detection in H.264 Sequence of Human Embryo Development,” IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, Canada, pp. 1–4, May 2016 (Qualis: C).
  35. Sh. Kheradmand, P. Saeedi, and I. V. Bajić, “Human Blastocyst Segmentation Using Neural Network,” IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, Canada, pp. 1–4, May 2016 (Qualis: C).
  36. A. Singh, J. Buonassisi, P. Saeedi, and J. Havelock, “Automatic Blastomere Detection in Day 1 to Day 2 Human Embryo Images Using Partitioned Graphs and Ellipsoids,” IEEE International Conference on Image Processing (ICIP), pp. 917–921, 2014 (Qualis: A1).
  37. D. Yee, P. Saeedi, and J. Havelock, “An Automatic Model Based Approach for Measuring the Zona Pellucida Thickness in Day Five Human Blastocysts,” International Conference on Image Processing, Computer Vision and Pattern Recognition (IPCV), pp. 877–880, 2013 (Qualis: B5).
  38. P. Saeedi and M. Cote, “A Star Corner Algorithm for Building Extraction in Satellite and Aerial Images,” International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), Las Vegas, Nevada, USA, 2012 (Qualis: B5).
  39. Z. Blair and P. Saeedi, “3D Reconstruction of Pitched Roofs in Monocular Satellite and Aerial Images,” International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), Las Vegas, Nevada, USA, 2012 (Qualis: B5).
  40. H. Hadizadeh, I. V. Bajić, P. Saeedi, and S. Daly, “Good Looking Green Images,” IEEE International Conference on Image Processing (ICIP), 2011 (Qualis: A1).
  41. J. Westell and P. Saeedi, “3D Object Recognition via Multi View Inspection in Unknown Environments,” International Conference on Control, Automation, Robotics and Vision (ICARCV), 2010 (Qualis: B1).
  42. H. Badakhshannoory and P. Saeedi, “Automatic Liver Segmentation from CT Scans Using Multi Layer Segmentation and Principal Component Analysis,” International Symposium on Visual Computing (ISVC), 2010 (Qualis: B1).
  43. M. Izadi and P. Saeedi, “Automatic Building Detection in Aerial Images Using a Hierarchical Feature Based Image Segmentation,” IEEE International Conference on Pattern Recognition (ICPR), 2010 (Qualis: A2).
  44. H. Badakhshannoory, P. Saeedi, and K. A. Qayumi, “Liver Segmentation Based on Deformable Registration and Multi Layer Segmentation,” IEEE International Conference on Image Processing (ICIP), 2010 (Qualis: A1).
  45. M. Izadi and P. Saeedi, “Height Estimation for Buildings with Complex Contours in Monocular Satellite and Airborne Images Based on Fuzzy Reasoning,” IEEE International Conference on Image Processing (ICIP), 2010 (Qualis: A1).
  46. M. S. Nosrati and P. Saeedi, “Rooftop Detection Using a Corner Leaping Based Contour Propagation Model,” International Conference on Image Processing Theory, Tools and Applications (IPTA), 2010 (Qualis: B2).
  47. Y. M. Chen, I. V. Bajić, and P. Saeedi, “Motion Segmentation in Compressed Video Using Markov Random Field Classification,” IEEE International Conference on Multimedia and Expo (ICME), 2010 (Qualis: A2).
  48. M. S. Nosrati and P. Saeedi, “A Novel Approach for Polygonal Rooftop Detection in Satellite and Aerial Imageries,” IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, 2009 (Qualis: A1).
  49. M. S. Nosrati and P. Saeedi, “A Combined Approach for Building Detection in Satellite Imageries Using Active Contours,” International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), Las Vegas, Nevada, USA, July 2009 (Qualis: B5).
  50. M. Izadi and P. Saeedi, “Height Estimation for Buildings in Monocular Satellite and Airborne Images Based on Fuzzy Reasoning and Genetic Algorithm,” International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), London, UK, May 2009.
  51. Y. M. Chen, I. V. Bajić, and P. Saeedi, “Coarse To Fine Moving Region Segmentation in Compressed Video,” International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), London, UK, May 2009.
  52. M. Kalantari, P. Saeedi, Y. P. Fallah, and M. K. Khandani, “A Novel Data Clustering Algorithm Based on Electrostatic Field Concepts,” IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Nashville, TN, USA, 2009 (Qualis: B1).
  53. P. Saeedi and H. Zwick, “Automatic Building Detection in Aerial and Satellite Images,” International Conference on Control, Automation, Robotics and Vision (ICARCV), Vietnam, 2008 (Qualis: B1).
  54. M. Izadi and P. Saeedi, “Robust Region Based Background Subtraction and Shadow Removing Using Colour and Gradient Information,” IEEE International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, Dec. 2008 (Qualis: A2).
  55. D. A. Lavigne, P. Saeedi, A. Dlugan, N. Goldstein, and H. Zwick, “Automatic Building Detection and 3D Shape Recovery from Single Monocular Electro Optical Imagery,” SPIE Defense and Security Symposium, Orlando, Florida, USA, Apr. 2007.
  56. P. Saeedi, D. Kusalovic, P. Jacobsen, K. Ardron, P. D. Lawrence, and D. G. Lowe, “Path Tracking Control for Tracked Based Machines Using Sensor Fusion,” ASCE Aerospace Division International Conference on Engineering, Construction, and Operations in Challenging Environments (Earth & Space), League City, TX, USA, Mar. 7–10, 2004.
  57. P. Saeedi, D. G. Lowe, and P. D. Lawrence, “3D Localization and Tracking in Unknown Environments,” IEEE International Conference on Robotics and Automation (ICRA), Taipei, Taiwan, Sept. 2003 (Qualis: A1).
  58. P. Saeedi, P. D. Lawrence, D. G. Lowe, and P. Jacobsen, “Multi Sensor Track Controls for Excavator Based Machines,” ISSS CCCT International Conference on Computer, Communication and Control Technologies, Orlando, Florida, USA, July 2003.
  59. P. Saeedi, D. G. Lowe, and P. D. Lawrence, “An Efficient Binary Corner Detector,” IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, Dec. 2002 (Qualis: B1).
  60. P. Saeedi, P. D. Lawrence, and D. G. Lowe, “3D Motion Tracking of a Mobile Robot in a Natural Environment,” IEEE International Conference on Robotics and Automation (ICRA), San Francisco, USA, May 2000 (Qualis: A1).

Alumni and Highly Qualified Personnel (HQP)

Postdoctoral Fellows

PhD Graduates

MASc Graduates

MEng Graduates

Undergraduate Alumni

Open Positions

I am currently recruiting one PhD student. Applicants must hold a Master’s degree in Electrical and Computer Engineering with a strong focus on Artificial Intelligence, have a minimum GPA of 80 percent, and demonstrate a strong research record, including peer reviewed publications.

Due to the high volume of inquiries I receive, I am only able to respond to candidates whose background and qualifications closely align with these requirements.