cv
Curriculum vitae of Shakeeb Murtaza.
Contact Information
| Name | Shakeeb Murtaza |
| Professional Title | Postdoctoral Fellow |
| shakeebmurtaza@outlook.com |
Professional Summary
Postdoctoral researcher working at the intersection of trustworthy AI (explainability, robustness, evaluation) and applied machine learning, with publications in Pattern Recognition, NeurIPS, and WACV.
Experience
-
2025 - present Montréal, Canada
Postdoctoral Fellow (Industry Collaboration with Genetec)
École de Technologie Supérieure (ÉTS) / LIVIA
Research on explainability and responsible AI for transformer-based models deployed in real-world settings.
- Lead research on explainability and responsible AI for transformer-based models, from method design to reproducible experiments.
- Collaborate with academic and industry stakeholders to define research milestones and translate findings into actionable directions.
- Drive rigorous evaluation with baselines, ablations, and metrics; maintain reproducible experiment pipelines.
- Co-author manuscripts, contribute to funding and grant materials, and present results at conferences.
- Mentor students on experimental design, code quality, and reproducible research practices.
- Manage a high-performance research computing environment (20+ GPU servers) supporting large-scale training.
-
2021 - 2023 Montréal, Canada
Deep Learning Intern (Mitacs)
Ericsson
Industrial research internship during the Ph.D. program.
- Conducted deep-learning research and large-scale distributed training on internal and national GPU clusters (Alliance/Compute Canada).
- Automated data and experiment pipelines, improving reproducibility, throughput, and stability for computer-vision workloads.
-
2019 - 2020 Islamabad, Pakistan
MS Research Fellow (Medical Imaging, Funded Thesis)
National Center of Artificial Intelligence (NCAI), COMSATS University Islamabad
Research in the Medical Imaging and Diagnostic Lab.
- Conducted medical-imaging research focused on explainability and counterfactual reasoning with deep generative models.
- Containerized and automated training workflows on research compute clusters for reproducibility and scale.
Education
-
2020 - 2025 Montréal, Canada
Doctorate in Engineering (Ph.D.)
École de Technologie Supérieure (ÉTS)
Deep learning with minimal supervision for visual tasks
- Thesis: Deep Weakly Supervised Learning Networks for Object Localization (https://espace.etsmtl.ca/id/eprint/3975/)
- Honoured on the ÉTS Honour Roll (Summer 2025) for academic excellence.
-
2018 - 2020 Islamabad, Pakistan
Master of Science
COMSATS University Islamabad
Computer Science
- Specialization in generative deep models for visual tasks.
Awards
-
2025 ÉTS Honour Roll (Summer 2025)
École de Technologie Supérieure
Honoured for academic excellence during doctoral studies.
Publications
-
2026 TeD-Loc: Text Distillation for Weakly Supervised Object Localization
Under revision at Pattern Recognition
-
2025 CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videos
Pattern Recognition
-
2025 -
2024 SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution
NeurIPS
-
2023 DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Image and Vision Computing
Skills
Trustworthy AI: Model explainability (attribution, concept/prototype methods), robustness, safety-minded evaluation, reproducibility
Machine Learning & Deep Learning: Transformers (ViT/VLM), self- and weakly-supervised learning, domain and test-time adaptation, experimental design, evaluation and metrics
Biomedical Imaging: Histology and microscopy image analysis, weakly supervised localization, dataset publication
Research Computing: Python, PyTorch, Bash, Linux, multi-GPU training, HPC schedulers (Alliance/Compute Canada), Docker, Git, CI/CD
Interests
Research Interests: Weakly supervised learning, explainable and trustworthy AI, vision-language models, person re-identification, biomedical image analysis