Mahdiyar Molahasani
Research Scientist, Foundation Models & Generalization
About me
I work on making foundation models reliable when data and compute are imperfect: long-tailed and biased data, distribution shift, decentralized data, and tight inference budgets. My work spans theory and practice, from convergence analysis to training-free methods.
I'm a Senior Machine Learning Research Scientist at Captura in Vancouver, where I work on efficient inference and adaptation for vision-language models. I did my PhD at Queen's University with Ali Etemad and Michael Greenspan.
Interactive notes
News
- Our paper bridging long-tailed recognition and continual learning was accepted to NeurIPS 2026.
- PRISM was accepted to ICCV 2025.
- Joined Captura as a Senior Machine Learning Research Scientist.
- Completed my PhD at Queen's University.
Selected publications
Generalization under imbalance
- A Theoretical Bridge Between Long-Tailed Recognition and Continual Learning Mahdiyar Molahasani, Michael Greenspan, Ali Etemad NeurIPS 2026 [arXiv, Interactive note, NeurIPS M3L Workshop’23]
Bias and fairness in vision-language models
- PRISM: Reducing Spurious Implicit Biases in Vision-Language Models with LLM-Guided Embedding Projection Mahdiyar Molahasani*, Azadeh Motamedi*, Michael Greenspan, Il-Min Kim, Ali Etemad ICCV 2025 [Paper, arXiv, Code, Interactive note]
Privacy-preserving generalization
- Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients Farhad Pourpanah*, Mahdiyar Molahasani*, Milad Soltany*, Michael Greenspan, Ali Etemad AAAI 2025 [Paper, arXiv, Code, Interactive note]
- A Theoretical Framework for Federated Domain Generalization with Gradient Alignment Mahdiyar Molahasani*, Milad Soltany*, Farhad Pourpanah*, Michael Greenspan, Ali Etemad NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning [Paper]
* Equal contribution
Experience & Education
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Senior Machine Learning Research Scientist, Captura Efficient inference and adaptation for vision-language models (2025–)
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Graduate Research Assistant, Ingenuity Labs Research Institute Queen's University (2021–2025)
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Ph.D., Electrical and Computer Engineering, Queen's University With Ali Etemad and Michael Greenspan; thesis on generalization in deep representation learning (2021–2025)
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M.Sc., Electrical and Computer Engineering, University of Saskatchewan (2019–2021)
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B.Sc., Electrical Engineering, Iran University of Science and Technology (2013–2018)
Service
- Top Reviewer, NeurIPS 2026 and NeurIPS 2024, Gold Reviewer Award, ICML 2026.
- Reviewer for NeurIPS, ICML, ICLR, AAAI, AISTATS, TMLR, and IEEE transaction journals.
Night sky
The night sky has been my passion for years, and it even found its way into my research.