Author: Hamid Arzani

We are excited to host our Research Talk Series: CORS Practice Presentations.

📆 Wednesday, May 21
🕛 12:00 PM – 1:30 PM
📍 MC331 (Mechanical Engineering Building), 5 King’s College Rd, Toronto.

This session features presentations from UTORG student members preparing for CORS Annual Meeting 2025:

🎤 Ali Daei Naby (PhD Candidate, Rotman)
Ttile: Inspect or Guess? Mechanism Design with Unobservable Inspection
Abstract: We study the problem of selling k units of an item to n unit-demand buyers to maximize revenue, where buyers’ values are independently (and not necessarily identically) distributed. The buyers’ values are initially unknown but can be learned at a cost through inspection sources. Motivated by applications in e-commerce, where the inspection is unobservable by the seller (i.e., buyers can externally inspect their values without informing the seller), we introduce a framework to find the optimal selling strategy when the inspection is unobservable with and without the seller’s ability to control inspection. For each setting, we fully characterize the optimal mechanism for selling to a single buyer, subject to an upper bound on the allocation probability. Building on this characterization and leveraging connections to the \emph{Prophet Inequality}, we design an approximation mechanism for selling k items to $n$ buyers that achieves $1-1/\sqrt{k+3}$ of the optimal revenue. Our mechanism is simple and sequential and achieves the same approximation bound in an online setting, remaining robust to the order of buyer arrivals. Additionally, in a setting with observable inspection, we leverage connections to cutoff-based search algorithms in \emph{Weitzman’s Pandora’s problem with non-obligatory inspection} and propose a new sequential mechanism for selling an item to n buyers that achieves (the improved bound of) $1-1/e \approx .63$ of the optimal revenue.

🎤 Forrest Mayer (Master’s Student, MIE)
Title: Assessing the Trade-offs Between Disease Mitigation and Health Equity Focused Vaccine Prioritization Strategies
Abstract: The COVID-19 pandemic disproportionately impacted socially disadvantaged populations’ health outcomes and worsened existing disparities. During the pandemic, governments had to allocate limited vaccines to the population. Limited budget vaccine allocation can be formulated as the critical node detection problem (CNDP), which identifies a set of critical nodes (people) in a graph (social contact network) that when removed (vaccinated), minimize a network connectivity metric. Previous implementations of CNDP for vaccination have not considered equitable distribution. We extend an optimal approximation algorithm for the distance-based critical node detection problem to prioritize social disadvantage while minimizing network connectivity. Our experiments demonstrate a majority focus on equity-based prioritization has a large impact on disadvantaged groups’ vaccination rates with a low negative impact on network connectivity and advantaged groups’ vaccination rates. These results demonstrate that policymakers can prioritize socially disadvantaged groups in vaccine allocation to improve health outcomes with little negative impact on disease mitigation.

🎤 Zhenghang Xu (PhD Candidate, Rotman)
Title: Bayesian Pricing for Impatient Customers with Unknown Valuation
Abstract: We study a Bayesian dynamic pricing problem for a revenue-maximizing, capacity-constrained service provider that learns unknown customer valuations through sequential pricing. Customers arrive sequentially, accept or reject the prevailing price, and those who accept occupy the server; arrivals are lost if the server is busy. Our contributions include: (1) A novel recursive algorithm to compute optimal prices that balance demand learning and revenue maximization; (2) theoretical identification of an adaptive explore-then-commit structure: when uncertainty resolves sufficiently, the firm prices at the valuation’s lower bound to guarantee sales; (3) a practical heuristic for delay-free settings, inspired by the Bayesian optimal policy, that numerically outperforms existing benchmarks and achieves the optimal regret order. By unifying Bayesian learning with pricing dynamics, we show how firms adaptively transition from exploration to exploitation. Results demonstrate how customer impatience and server constraints shape pricing strategies, offering actionable methods with provable guarantees for revenue management under uncertainty.

🎓 Whether you’re attending CORS or just curious about ongoing research in OR, we invite you to come, listen, and enjoy having lunch!

📝Please register using this link so we can prepare the right amount of food and seating. Don’t worry if you forget to register—you’re still welcome to join us!

Join us for insightful presentations by two of our fellow students, and enjoy a delicious lunch served during the talks.

Event Details
Date: Wednesday, March 26
Time: 12:00 PM – 1:30 PM
Location: RTL (Rotman Lower Level) 1030, Rotman School of Management, University of Toronto

Please Register Here so we can prepare the right amount of food and seating. Don’t worry if you forget to register—you’re still welcome to join us!

First Talk : Patient Abandonment Behavior in the Emergency Department 

Abstract
Study objective: In this study, we developed and validated machine learning models to predict emergency department (ED) patients’ tendency to leave without  being seen (LWBS). We used patient as well as system characteristics in our models which dynamically updated these risk levels as patients spent more time waiting. We applied these models to an experimental cohort, and show that it misses fewer LWBS cases than previous models that do not dynamically update their predictions. Methods: A dataset of 150,959 patient arrivals at an ED of a large academic institution was collected over a period of 24 months from 2017 to 2019. We used the first 18 months of data to train and validate the machine learning models, with the final six months serving as an experimental period. The models were applied to the experimental period and their ability to identify real LWBS cases was measured. Results: The models achieved an area under the receiver operating characteristic curve (AUC) of 0.86 when dynamically updating LWBS risk levels over time. This was in contrast to an AUC of 0.80 for the static model from past literature that does not update the score after the moment of arrival. Among the experimental cohort, the dynamic model also showed the ability to reduce the number of missed LWBS cases by approximately 50% as compared to the static model. Conclusion: Prediction models were created to accurately identify patients who will LWBS, as well as when during their wait they are most likely to LWBS. When tested on a cohort that was not used in the training stages, the models illustrated the importance of updating patients’ risk of LWBS as their wait continued.

Bio
Yaniv Ravid is a 4th year PhD student at the Operations Management and Statistics department at Rotman School of Management. His research utilizes data analytics to better address operational risks and business systems disruptions. Before his doctoral studies, Yaniv worked in the tech industry as a software consultant and data security specialist. He obtained his Master of Engineering in Operations Research and Information Engineering from Cornell University.

Second Talk : Conformal Inverse Optimization for Adherence-aware Prescriptive Analytics 

Abstract
Inverse optimization is increasingly used to estimate unknown parameters in an optimization model based on decision data. We show that such a point estimate alone is insufficient in a prescriptive setting where the estimated parameters are used to prescribe new decisions. The resulting decisions may be low-quality and misaligned with human intuition and thus are unlikely to be adopted. To tackle this challenge, we propose a novel decision recommendation pipeline, which seeks to learn an uncertainty set for the unknown parameters and then solve a robust optimization model to prescribe new decisions. We show that the suggested decisions can achieve bounded optimality gaps, as evaluated using both the ground-truth parameters and human perceptions. Our method demonstrates strong empirical performance compared to the standard inverse optimization pipeline. Finally, we perform a case study where we apply this new pipeline to provide delivery route recommendations in Toronto, Canada. Our approach achieves a significantly higher delivery path adherence rate than current industry practices without compromising service quality. Moreover, our method provides a better trade-off between absolute and perceived decision quality than baselines under various realistic scenarios, including cases with model mis-specification and data scarcity.

Bio
Bo Lin is a PhD candidate at the University of Toronto. His research centres around AI for sustainable cities, with a focus on urban mobility to date. His research has been widely referenced in policy documents and mainstream media, including CBC News, Toronto Star, U of T News, and Uber Engineering Blog. His work has been recognized with several awards, including the INFORMS TSL Best Student Paper Award, runner-up for the CORS Best Student Paper Award, and finalist nominations for the INFORMS Service Science Best Cluster Paper Award and INFORMS Data Mining Best Theoretical Paper Award. In 2023, he interned as an Applied Scientist on Uber’s Core Analytics and Science team.

We look forward to seeing you!