The Health AI and Analytics Lab is a collaborative research group in the Department of Management Science and Engineering, dedicated to improving healthcare operations and medical decisions through AI and analytics. We develop models and decision tools that help healthcare systems allocate scarce resources, reduce wait times, improve access and equity, and support high-quality clinical and operational decisions. By integrating optimization, stochastic modelling, machine learning, and real world healthcare data, the lab translates analytical insights into practical policies that enhance system performance and patient outcomes, particularly in complex, capacity-constrained environments. Lab members work closely with healthcare providers across multiple sectors, with collaborations spanning acute‑care hospitals, cancer centres, rehabilitation centres, and community‑based providers, ensuring that solutions are designed for implementation and measurable impact.
News
Health AI & Analytics Lab Members Recognized at CORS 2026
Members of the Health AI & Analytics Lab were actively engaged at the 2026 Canadian Operational Research Society (CORS) Conference, with several students presenting their research and earning recognitions in prestigious competitions. The conference highlighted the breadth of the lab’s work in healthcare operations, service systems, and applied analytics, with multiple students receiving finalist and award distinctions across several categories.
Congratulations to Ahmed Fawzy, Adam Drakich, Jessie Fan, Chris Wang, Gisele Zhang, Hamid Arzani, Larissa Troper, and Amirmohammad Marshalpirgheybi for their outstanding recognitions!
Article Featured in ISE Magazine: "The optimization looks great, but can the machine deliver it?"
A paper by Scholar Sun, a research assistant in the Health AI and Analytics Lab, published in the IISE Transactions on Healthcare Engineering, was featured as a selected article in the May 2026 issue of the ISE Magazine. The paper tackles a large-scale complex problem in radiation therapy for cancer patients and devises methodologies for ensuring the complex model solution can be translated into a realistically deliverable treatment plan in clinics. This work was supervised by Prof. Houra Mahmoudzadeh and co-authored by collaborators from the Waterloo Regional Health Network (WRHN) Cancer Centre. Scholar had previously won the CORS Undergraduate Paper Competition and received the Honorable Mention of the INFORMS Undergraduate Operations Research Prize.
Picktacular: Adaptive Decision Support for Operating Room Picklist Management
A student capstone team designed and delivered Picktacular, an adaptive decision‑support dashboard designed to help hospitals better manage operating room (OR) supply pick‑lists. Developed in collaboration with Cambridge Memorial Hospital (CMH) and supervised by Prof. Houra Mahmoudzadeh and Prof. Stan Dimitrov, the project earned the Sustainable Development Capstone Design Award in Management Science and Engineering. Congratulations Agishan Thaya, Callum Gillies, Curtis Tse, Graydon Power, and Liam Mitchell for the amazing work!