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Publications

Authors for Operations Research-style entries are listed in alphabetical order. The articles below are ordered by pre-prints, methodological papers, and applied papers.

Click here to see papers organized by chronology.

Pre-prints/under review

  1. No need to sacrifice data quality for quantity: Crowd-informed machine annotation for cost-effective understanding of visual data Christopher Klugmann, Rafid Mahmood, Guruprasad Hegde, Amit Kale, and Daniel Kondermann   arXiv  
  2. AutoScale: Automatic prediction of compute-optimal data composition for training LLMs Feiyang Kang, Yifan Sun, Bingbing Wen, Si Chen, Dawn Song, Rafid Mahmood, and Ruoxi Jia Under review   arXiv  
  3. Can feedback enhance semantic grounding in large visual language models? Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler, and David Acuna Under review   arXiv     Project Page  
  4. Deep learning-assisted appointment scheduling under uncertainty Amirhossein Moosavi, Onur Ozturk, Rafid Mahmood, and Jonathan Patrick Under review
  5. Optimizing data collection for machine learning Rafid Mahmood, James Lucas, Jose M Alvarez, Sanja Fidler, and Marc T Law Under review at Journal of Machine Learning Research   arXiv     Project Page  

    • Preliminary version appeared in NeurIPS 2022.

Methods

  1. Reasoning paths with reference objects elicit quantitative spatial reasoning in large vision-language models Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler, and David Acuna Empirical Methods in Natural Language Processing (EMNLP) 2024   arXiv     Project Page  
  2. Pricing and competition for generative AI Rafid Mahmood Advances in Neural Information Processing Systems (NeurIPS) 2024   arXiv  
  3. Translating labels to solve annotation mismatches across object detection datasets Andrew Liao, David Acuna, Rafid Mahmood, James Lucas, Viraj Prabhu, and Sanja Fidler International Conference on Learning Representations 2024   Project Page  
  4. Inverse optimization: Theory and applications Timothy CY Chan, Rafid Mahmood, and Ian Y Zhu Operations Research 2023   arXiv     DOI  
  5. Got (optimal) milk? Pooling donations in human milk banks with machine learning and optimization Timothy CY Chan, Rafid Mahmood, Deborah L. O’Connor, Debbie Stone, Sharon Unger, Rachel K Wong, and Ian Y Zhu Manufacturing & Service Operations Management 2023   DOI  

    • First Place for the INFORMS Pierskalla Best Paper Award 2023.
      Finalist for the MSOM Practice-Based Research Competition 2023.
      Runner Up for the POMS College of Healthcare Operations Management (CHOM) Best Healthcare Paper Award 2023.
      Honorable Mention for the CORS Practice Prize Competition 2023.

    • Preliminary version appeared in The Journal of Nutrition.

  6. Learning to optimize contextually constrained problems for real-time decision generation Aaron Babier, Timothy CY Chan, Adam Diamant, and Rafid Mahmood Management Science 2023   arXiv  
  7. Bridging the Sim2Real gap with CARE: Supervised detection adaptation with conditional alignment and reweighting Viraj Prabhu, David Acuna, Andrew Liao, Rafid Mahmood, Marc T Law, Judy Hoffman, Sanja Fidler, and James Lucas Transactions on Machine Learning Research (TMLR) 2023   arXiv     Project Page  
  8. How much more data do I need? Estimating requirements for downstream tasks Rafid Mahmood, James Lucas, David Acuna, Daiqing Li, Jonah Philion, Jose M Alvarez, Zhiding Yu, Sanja Fidler, and Marc T Law IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022   arXiv     Project Page  
  9. Optimizing data collection for machine learning Rafid Mahmood, James Lucas, Jose M Alvarez, Sanja Fidler, and Marc T Law Advances in Neural Information Processing Systems (NeurIPS) 2022   arXiv     Project Page  
  10. Low budget active learning via Wasserstein distance: An integer programming approach Rafid Mahmood, Sanja Fidler, and Marc T Law International Conference on Learning Representations (ICLR) 2022   arXiv  
  11. An ensemble learning framework for model fitting and evaluation in inverse linear optimization Aaron Babier, Timothy CY Chan, Taewoo Lee, Rafid Mahmood, and Daria Terekhov INFORMS Journal on Optimization 2021   arXiv     Code     DOI  

    • Honorable Mention for the CORS Best Student Paper Competition 2018.

  12. Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation Timothy CY Chan, Adam Diamant, and Rafid Mahmood Operations Research Letters 2020   Code     DOI     Paper  
  13. Streaming codes for multiplicative-matrix channels with burst rank loss Rafid Mahmood, Ahmed Badr, and Ashish Khisti IEEE Transactions on Information Theory 2018   DOI  

    • Preliminary version appeared in ISIT 2016.

  14. Convolutional codes with maximum column sum rank for network streaming Rafid Mahmood, Ahmed Badr, and Ashish Khisti IEEE Transactions on Information Theory 2016   arXiv     DOI  

    • Preliminary version appeared in ISIT 2015.

  15. Low delay network streaming under burst losses Rafid Mahmood, Ahmed Badr, and Ashish Khisti IEEE International Symposium on Information Theory (ISIT) 2016
  16. Embedded MDS codes for multicast streaming Ahmed Badr, Rafid Mahmood, and Ashish Khisti IEEE International Symposium on Information Theory (ISIT) 2015
  17. Convolutional codes with maximum column sum rank for network streaming Rafid Mahmood, Ahmed Badr, and Ashish Khisti IEEE International Symposium on Information Theory (ISIT) 2015

Applied

  1. Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review Amirhossein Moosavi, Steven Huang, Maryam Vahabi, Bahar Motamedivafa, Nelly Tian, Rafid Mahmood, Peter Liu, and Christopher LF Sun Journal of the American College of Cardiology: Advances 2024
  2. OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines Aaron Babier, Rafid Mahmood, Binghao Zhang, Victor GL Alves, Ana Maria Barragán-Montero, Joel Beaudry, Carlos E. Cardenas, Yankui Chang, Zijie Chen, Jaehee Chun, Kelly Diaz, Harold D Eraso, Erik Faustmann, Sibaji Gaj, Skylar Gay, Mary Gronberg, Bingqi Guo, Junjun He, Gerd Heilemann, Sanchit Hira, Yuliang Huang, Fuxin Ji, Dashan Jiang, Jean CJ Giraldo, Hoyeon Lee, Jun Lian, Shuolin Liu, Keng-Chi Liu, José Marrugo, Kentaro Miki, Kunio Nakamura, Tucker Netherton, Dan Nguyen, Hamidreza Nourzadeh, Alexander FI Osman, Zhao Peng, José Darío Quinto Muñoz, Christian Ramsl, Dong J Rhee, Juan D Rodriguez, Hongming Shan, Jeffrey V Siebers, Mumtaz H Soomro, Kay Sun, Andrés Usuga Hoyos, Carlos Valderrama, Rob Verbeek, Enpei Wang, Siri Willems, Qi Wu, Xuanang Xu, Sen Yang, Lulin Yuan, Simeng Zhu, Lukas Zimmermann, Kevin L Moore, Thomas G Purdie, Andrea L McNiven, and Timothy CY Chan Accepted at Physics in Medicine and Biology 2022   arXiv  
  3. OpenKBP: The open access knowledge-based planning grand challenge Aaron Babier, Binghao Zhang, Rafid Mahmood, Kevin Moore, Thomas G Purdie, Andrea McNiven, and Timothy CY Chan Medical Physics 2021   arXiv     Code     DOI  
  4. Prediction of protein and fat content in human donor milk using machine learning Rachel K Wong, Michael A Pitino, Rafid Mahmood, Ian Y Zhu, Deborah Stone, Sharon Unger, Deborah O’Connor, and Timothy CY Chan Journal of Nutrition 2021   DOI  
  5. Predicting post-operative cochlear implant performance using supervised machine learning Matthew G Crowson, Peter Dixon, Rafid Mahmood, Jong W Lee, David Shipp, Trung Le, Joseph Chen, and Timothy CY Chan Otology and Neurotology 2020   DOI  
  6. AutoAudio: Deep learning for automatic audiogram interpretation Matthew G Crowson, Jong W Lee, Amr Hamour, Rafid Mahmood, Aaron Babier, Vincent Lin, Debara L Tucci, and Timothy CY Chan Journal of Medical Systems 2020   DOI  
  7. The importance of evaluating the complete automated knowledge-based planning pipeline Aaron Babier, Rafid Mahmood, Andrea L McNiven, Adam Diamant, and Timothy CY Chan Physica Medica: European Journal of Medical Physics 2020   arXiv     DOI  
  8. Knowledge-based automated planning with three-dimensional generative adversarial networks Aaron Babier, Rafid Mahmood, Andrea L McNiven, Adam Diamant, and Timothy CY Chan Medical Physics 2020   arXiv     DOI  

    • Preliminary version appeared in NeurIPS ML4H Workshop 2018.

  9. Automated treatment planning in radiation therapy using 3-D generative adversarial networks Aaron Babier, Rafid Mahmood, Andrea McNiven, Adam Diamant, and Timothy CY Chan NeurIPS Machine Learning for Health Workshop 2018   Code     Paper  
  10. Automated treatment planning in radiation therapy using generative adversarial networks Rafid Mahmood, Aaron Babier, Andrea McNiven, Adam Diamant, and Timothy CY Chan Machine Learning for Healthcare Conference 2018   Code     Project Page