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! |
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| 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 |