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
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
Deep learning-assisted appointment scheduling under uncertainty
Amirhossein Moosavi,
Onur Ozturk,
Rafid Mahmood,
and Jonathan Patrick
In preparation
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
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
Inverse optimization: Theory and applications
Timothy CY Chan,
Rafid Mahmood,
and Ian Y Zhu
Operations Research
2023
arXiv
DOI
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.
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
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
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
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
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
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.
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
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.
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.
Low delay network streaming under burst losses
Rafid Mahmood,
Ahmed Badr,
and Ashish Khisti
IEEE International Symposium on Information Theory (ISIT)
2016
Embedded MDS codes for multicast streaming
Ahmed Badr,
Rafid Mahmood,
and Ashish Khisti
IEEE International Symposium on Information Theory (ISIT)
2015
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
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
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
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
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.
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
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
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
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
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