Portrait of Amine Mhedhbi

Amine Mhedhbi

Associate Academic Member
Assistant Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Research Topics
Computer Systems
Data Science
Information Retrieval
Machine Learning Operations (MLOps)
Machine Learning Systems
Tabular Data
Website
Google Scholar
LinkedIn
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Biography

Amine Mhedhbi is an assistant professor at Polytechnique Montreal's Department of Computer and Software Engineering, where he leads the Data and AI Systems (DAIS) group. He is an Associate Academic Member at Mila – Quebec Artificial Intelligence Institute and holds an FRQ-IVADO chair in multimodal data engineering.

His research interests include all aspects of data and information management with a focus on analytical and AI-driven data system architectures. His work includes tackling performance considerations, debuggability, interface design, and data applications.

Amine received his Ph.D. from the University of Waterloo, where he was awarded the Computer Science distinguished dissertation award and was a Microsoft Ph.D. fellow.

Current Students

Nour Shaheen
Master's Research - Polytechnique Montréal
Principal supervisor :
Sarath Chandar
Github
Google Scholar

Publications

Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Priyan Vaithilingam
Munyeong Kim
Frida-Cecilia Acosta-Parenteau
Daniel Lee
Amine Mhedhbi
Elena L. Glassman
Ian Arawjo
2025-04-12
ArXiv (preprint)
arxiv.org
arxiv.org
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Priyan Vaithilingam
Munyeong Kim
Frida-Cecilia Acosta-Parenteau
Daniel Lee
Amine Mhedhbi
Elena L. Glassman
Ian Arawjo
2025-04-01
arXiv (published)
doi.org
arxiv.org
Towards Optimizing SQL Generation via LLM Routing
Mohammadhossein Malekpour
Nour Shaheen
Foutse Khomh
Amine Mhedhbi
Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capabl… (see more)e large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset.
2024-11-06
ArXiv (preprint)
doi.org
arxiv.org
Towards Optimizing SQL Generation via LLM Routing
Mohammadhossein Malekpour
Nour Shaheen
Foutse Khomh
Amine Mhedhbi
Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capabl… (see more)e large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset.
2024-11-06
ArXiv (preprint)
doi.org
arxiv.org