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Rafid Mahmood

I am an Assistant Professor at the Telfer School of Management in the University of Ottawa. I am also a part-time Sr. Research Scientist at the NVIDIA Toronto AI Lab.

I am interested in the operational challenges behind the deployment and use of AI systems. I use deep learning and data-driven optimization for problems where the typical forms of large-scale data collection and model tuning are prohibitive. My work addresses ML technology (e.g., computer vision systems), healthcare (e.g., personalized medicine), and finance (e.g., portfolio optimization). I am also interested in general data science problems (e.g., sports analytics).

Students interested in (Undergraduate/Masters/PhD) supervision at Telfer are welcome to contact me here with a CV and transcript.

Graduate students interested in internships at NVIDIA are welcome to contact me here with a CV and summary of research interests.

News

Sep 28, 2024 Our paper, Pricing and Competition with Generative AI, was accepted at NeurIPS 2024. See you in Vancouver!
Sep 20, 2024 Our paper, Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models was accepted at EMNLP 2024.
Jul 29, 2024 Our pre-print, AutoScale: Automatic Prediction of Compute-optimal Data Composition for Training LLMs, introduces a method for determining the optimal mixture of data to train LLMs.
Apr 9, 2024 Check out our pre-print, Can Feedback Enhance Semantic Grounding in Large-Scale Vision Language Models, which uses multiple VLMs that iterately improve semantic prediction tasks!
Jan 15, 2024 Our paper Translating Labels to Solve Annotation Mismatches Across Object Detection Datasets was accepted at ICLR 2024.
Oct 20, 2023 Our review paper Inverse Optimization: Theory and Applications was accepted at Operations Research.
Oct 16, 2023 Our paper Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization won First Place for the Pierskalla Best Paper Award in Healthcare!
Oct 3, 2023 I will be speaking about our recent work on optimizing data collection at the ICCV 2023 Tutorial on Learning with Noisy and Unlabeled Data for Large Models beyond Categorization