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Operations Seminar Series

The Yale Operations Seminar Series presents recent research papers in operations. The goal is to bring researchers from other universities to the Yale campus to stimulate exchange of ideas and deepen understanding of operations trends. These seminars are geared towards faculty and PhD students interested in operations research and management. Leading operations scholars, both internal and external will present their latest research. Doctoral students will meet with faculty prior to these seminars to review the papers and related literature. Participation in this seminar is required throughout the program.

Faculty Coordinator: Lesley Meng, Assistant Professor of Operations Management, (203)436-9869
Faculty Support Coordinator: Nicole Morales, Faculty Support Team Coordinator, (203) 436-3841
Seminar Series is held on Tuesdays from 11:45 a.m.-12:45 p.m., in 4230 at Edward P. Evans Hall, 165 Whitney Avenue, New Haven, CT, and via Zoom.
An email notice with abstract and paper will be sent in advance of each talk in the series.

Spring 2023

February 7: Andrew SchaeferNoah Harding Chair and Professor of Computational and Applied Mathematics, Rice University

Title: Outcome-Based Regulation and Adverse Selection in Lung Transplantation

Abstract: Organ transplantation programs in the United States have seen increased scrutiny of outcomes in the past twenty years. Under regulations by the Organ Procurement Transplantation Network (OPTN) and Centers for Medicare and Medicaid (CMS), the United States has seen a rise in risk-averse patient selection among transplant programs, resulting in decreased transplantation volume for some programs. However, there is debate in the clinical literature over whether this observed response is rational. In this work, we develop a chance-constrained mixed-integer program to model the perspective of a transplant program that seeks to simultaneously maximize transplant volume and control the risk of OPTN/CMS penalization. Using our model, we demonstrate that under realistic conditions, it may be rational for a transplant program to curtail its transplant volume in order to avoid penalization. Moreover, we demonstrate that this incentive does not disappear even if regulators use accurate risk adjustment for high-risk patients. Our findings provide the first rigorous theoretical evidence that OPTN/CMS regulations create incentives for programs to reject certain medically-suitable patients.


January 31: Yale Herer, Professor of Industrial Engineering and Management, Technion, Israel Institute of Technology

TitleAn asymptotic perspective on risk pooling: Limitations and relationship to transshipments

Abstract: In this talk we provide a novel perspective on risk pooling approaches by characterizing and comparing their asymptotic performance, highlighting the conditions under which one approach dominates the other. More specifically, we determine the inventory policy and the expected total costs of systems under physical and information pooling as the number of locations grows. We show that physical pooling dominates information pooling in settings with no additional per-item and per-location costs for operating the centralized system. In the presence of such costs, however, information pooling becomes a viable alternative to physical pooling. Through asymptotic analysis, we also address the grouping problem, the division of a given set of non-identical locations into an ordered collection of mutually exclusive and collectively exhaustive subsets of predetermined sizes and demonstrate that homogeneous groups, comprising locations with similar demand volatility, achieve a lower expected total cost. Finally, the convergence of the expected total costs and the base stock levels under the two pooling approaches is demonstrated through a simple numerical illustration. Our analysis supports the assertion that it is important to consider not only the individual characteristics of each location in isolation, but also the interactions among them, when designing pooling systems.

This talk is based on the publication: Yale T. Herer & Enver Yücesan (2022) An asymptotic perspective on risk pooling: Limitations and relationship to transshipments, IISE Transactions, DOI: 10.1080/24725854.2022.2086719

Fall 2022

December 6: Robert ShumskyProfessor of Operations Management, Tuck School of Business at Dartmouth

TitleWait Time Information Design

Abstract: When customers arrive, service providers often collect information to generate delay forecasts. We study how delay data-collection and forecasting systems can be designed to improve customer satisfaction. We assume that customers may be loss-averse in the sense that an increase in the expected wait causes more distress than the positive response caused by an equivalent decrease and that they may be risk conscious in that an increase in the variance of expected delay reduces utility. Our goal is to find the structure of delay information that optimizes the customers’ experience while waiting.  Delay forecasts follow Bayes' rule, given a prior distribution, the additional information collected for a particular customer, and the passage of time. We find that when loss aversion dominates, the optimal delay information focuses on the tails of the delay distribution. When risk consciousness is dominant more traditional information about the duration of delay--along a continuum from `short' to `long'--is optimal, and this information should be most precise about the longest delays. The optimal information design also affects the timing of delay revelation. When customers are loss averse, it is optimal to avoid changes in expected delay over time, so that waiting times are revealed as customers go into service. When customers are risk conscious, it is optimal to provide information so that they learn the good (or bad) news immediately, when they arrive.

November 8: Brian DentonStephen M. Pollock Professor and Chair of Industrial and Operations Engineering, University of Michigan

Title: Predictive Models for Optimizing Imaging Decisions for Detection of Metastatic Prostate Cancer

Abstract: In this talk I will discuss data-analytics approaches to develop, calibrate, and validate predictive models, to help urologists in a large state-wide collaborative make prostate cancer staging decisions based on individual patient risk factors. The models we developed predict the probability a patient who receives radiographic imaging will have metastatic cancer. The models were developed using observational data for patients diagnosed with prostate cancer. We used several machine learning methods and compared their performance at predicting outcomes of imaging. The models were validated using statistical methods based on bootstrapping and subsequent evaluation on out-of-sample data. These models were used to design guidelines that seek to optimally weigh the benefits and harms of radiological imaging for detection of metastatic prostate cancer. The Michigan Urological Surgery Improvement Collaborative, a state-wide medical collaborative, implemented these guidelines, which were predicted to reduce unnecessary imaging by more than 40% and limit the percentage of patients with missed metastatic disease to be less than 1%. The effects of the guidelines were measured post-implementation to confirm their impact on reducing unnecessary imaging across the state of Michigan. Time permitting, I will finish the talk by summarizing additional work on models for optimizing other types of decisions relevant to early detection of prostate cancer. OR Practice–Data Analytics for Optimal Detection of Metastatic Prostate Cancer (

October 25: Chaithanya BandiAssociate Professor of Analytics and Operations, NUS Business School

Title: Online Scheduling in data-rich but uncertain environments: A case study at PGIMER hospital

Abstract: In this talk, I will begin by giving an overview of three different problems motivated from our collaboration with PGIMER hospital in India. PGIMER is one of the largest public hospitals in India and was among the first hospitals to be part of the Digital India campaign. The resulting digitization enabled our data-driven study of operations in this hospital. We considered three different but related problems in this hospital: (1) Modeling and calibrating the complex dynamics of patient flows in this hospital; (2) Optimal design of operations and (3) Optimal staffing and scheduling. In this talk, I will focus on the intraday scheduling problem in a group of orthopaedic clinics where the planner schedules appointment times, given a sequence of appointments. We consider patient re-entry, where patients may be required to go for an x-ray examination, returning to the same doctor they have seen and variability in patient behaviours such as walk-ins, earliness, and no-shows, which leads to inefficiency such as long patient waiting time and physician overtime.  In our data set, we find significant variability in patient behaviours. We formulate the problem as a two-stage optimization problem, where scheduling decisions are made in the first stage. Queue dynamics in the second stage are modeled under a P-Queue paradigm, which minimizes a risk index representing the chance of violating performance targets, such as patient waiting times. The model reduces to a sequence of mixed-integer linear-optimization problems. Our model achieves significant reductions, in comparative studies against a sample average approximation (SAA) model, on patient waiting times, while keeping server overtime constant. Our simulations further characterize the types of uncertainties under which SAA performs poorly. Managerial insights: We present an optimization model that is easy to implement in practice and tractable to compute. Our simulations indicate that not accounting for patient re-entry or variability in patient behaviours will lead to suboptimal policies, especially when they have specific structure that should be considered.

October 11: Adam ElmachtoubAssociate Professor of Industrial Engineering and Operations Research at Columbia University

Title: Simple and Fair Pricing Strategies

Summary: In this talk, we survey several recent results on using simple and fair pricing strategies as alternatives to dynamic and personalized pricing strategies.

We show that our policies can be near-optimal, consumer-friendly, and easily implementable.

September 13: Omar BesbesVikram S. Pandit Professor of Business, Columbia Business School

Title: Data-driven decisions: how big should your data really be?

Abstract: We consider two fundamental questions in data-driven decision making: 1) how should a decision-maker construct a mapping from historical data to decisions? 2) how much data is needed to operate ``effectively”? We discuss various central applications in pricing and capacity decisions, together with different associated data structures. We present recent results that enable to quantify (robustly) achievable performance across data sizes, small and big. These results yield fundamental practical insights on the economics of data sizes: in many applications, a little data can go a long way in optimizing decisions.

The talk will draw on results from a series of papers:

Allouah, Bahamou, and Besbes, Pricing with Samples. Available at SSRN: 

Allouah, Bahamou, and Besbes, Optimal Pricing with a Single Point. Available at SSRN: 

Besbes and Mouchtaki, How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time. Available at SSRN: 

Spring 2022 - Virtual & In Person

Carri Chan (Professor of Business, Decision, Risk, and Operations, Columbia Business School), May 10

Please join us on Tuesday, May 10th, 11:45 am-12:45 pm EDT in 4200 or via Zoom link: 

Prediction-driven Surge Planning With Applications in the Emergency Department

Optimizing emergency department (ED) nurse staffing decisions to balance the quality of service and staffing cost can be extremely challenging, especially when there is a high level of uncertainty in patient-demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand-rate uncertainty by utilizing demand forecasts. In this work, we study a two-stage prediction framework that is synchronized with the base (made months in advance) and surge (made nearly real-time) staffing decisions in the ED. We quantify the benefit of the more expensive surge staffing. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Lastly, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and staffing in the ED. High fidelity ED simulation experiments demonstrate that the proposed framework can reduce staffing costs by 8% – 17% while guaranteeing timely access to care.

Ville Satopää (Assistant Professor, Technology and Operations Management, INSEAD), May 5

Please join us on Thursday, May 5th, 11:45 am-12:45 pm EDT in 4200 or via Zoom link: 

Herding in Probabilistic Forecasts

Decision makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, disclosure of public information may lower forecasting accuracy. This result, however, has been derived only for point forecasts. In this paper, we consider experts' probabilistic forecasts under herding, find a closed-form expression for the first two moments of a unique equilibrium forecast, and show that the experts report too similar locations and inflate the variance of their forecasts due to herding. Furthermore, we show that the negative externality of public information no longer holds. In addition to reacting to new information as expected, probabilistic forecasts contain more information about the experts' full beliefs and interpersonal structure. This facilitates model estimation. To this end, we consider a one-shot setting with one forecast per expert and show that our model is identifiable up to an infinite number of solutions based on point forecasts, but up to two solutions based on probabilistic forecasts. We then provide a Bayesian estimation procedure for these two solutions and apply it to economic forecasting data collected by the European Central Bank and the Federal Reserve Bank of Philadelphia. We find that, on average, the experts invest around 19% of their efforts into making similar forecasts. The level of herding shows an increasing trend from 1999 to 2007 but drops sharply during the financial crisis of 2007-2009, and then rises again until 2019.

Francis de Véricourt (Chaired Professor of Management Science, ESMT Berlin), May 3

Please join us on Tuesday, May 3rd, 11:45 am-12:45 pm EDT in 4200 or via Zoom link: 

Is your machine better than you? You may never know.

AI systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent empirical studies suggest that professionals sometimes doubt the quality of these systems, and as a result overrule machine-based prescriptions. This paper explores the extent to which a decision maker (DM) can properly assess whether a machine produces better recommendations. To that end, we analyze an elementary dynamic Bayesian framework, in which a machine performs repeated decision tasks under a DM’s supervision. The task consists in deciding whether to take an action or not. Crucially, the DM observes the accuracy of the machine’s prediction on the task only if she ultimately takes the action. As she observes the machine’s accuracy, the DM updates her belief about whether the machine’s predictions outperform her own. Depending on this belief, however, the DM sometimes overrides the machine, which affect her ability to assess it.

In this set-up, we characterize the evolution of the DM's belief and overruling decisions over time. We identify situations under which the DM’s belief oscillates forever, i.e., the DM always hesitates whether the machine is better. In this case, the DM never fully ignores the machine but regularly overrules it. We further find that the DM’s belief sometimes converges to a Bernoulli random variable, i.e., the DM ends up wrongly believing that the machine is better (or worse) with positive probability. We fully characterize the conditions under which these failures to learn occur. These results highlight some fundamental limitations in our ability to determine whether machines make better decision than experts. They further provide a novel explanation for why humans may collaborate with machines – even when one may actually outperform the other.

Huifeng Su (Doctoral Student, Yale School of Management), April 26

Please join us on Tuesday, April 26th, 11:45 am-12:45 pm EDT in 4200 or via Zoom link: 

The Impact of Hallway Placement on Emergency Department Quality of Care

Emergency Department (ED) crowding is a constant and relentless operational challenge that hospitals face across the country. With the intention to make timely care accessible to more patients, the emergency department often takes advantage of an additional source of capacity within the ED — the hallways. In this study, we investigate and quantify the impact of patient placement in the hallway on ED patient flow and quality measures through a quasi-experimental research design. In addition, we conduct heterogeneous analysis across ED operational settings to understand whether hallway placement uniformly backfires across utilization levels.

Maxime Cohen (Scale AI Chair Professor of Retail and Operations Management, McGill University), April 19

Please join us on Tuesday, April 19th, 11:45 am-12:45 pm EDT via Zoom link: 

Frustration-Based Promotions: Field Experiments in Ride-Sharing

The service industry has become increasingly competitive. One of the main drivers for increasing profits and market share is service quality. When consumers encounter a bad experience, or a frustration, they may be tempted to stop using the service. In collaboration with the ride-sharing platform Via, our goal is to understand the benefits of proactively compensating customers who have experienced a frustration. Motivated by historical data, we consider two types of frustrations: long waiting times and long travel times. We design and run three field experiments to investigate how different types of compensation affect the engagement of riders who experienced a frustration. We find that sending proactive compensation to frustrated riders (i) is profitable and boosts their engagement behavior, (ii) works well for long waiting times but not for long travel times, (iii) seems more effective than sending the same offer to nonfrustrated riders, and (iv) has an impact moderated by past usage frequency. We also observe that the best strategy is to send credit for future usage (as opposed to waiving the charge or sending an apologetic message).

Jonas Oddur Jonasson (Assistant Professor, Operations Management, MIT Sloan), April 12

Please join us on Tuesday, April 12th, 11:45 am-12:45 pm EDT in 4200 or via Zoom link: 

Redesigning Sample Transportation in Malawi Through Improved Data Sharing and Daily Route Optimization

Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g. blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Due to lack of updated information, existing sample transportation (ST) systems typically operate fixed schedules which do not account for demand variability. We present an innovative approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones and integrate it with a novel Multi-Stage version of the Dynamic Multi-Period Vehicle Routing Problem to generate daily routes in response to this updated information. We design the Optimized Sample Transportation (OST) system which comprises two components: a novel data sharing platform to monitor incoming sample volumes at healthcare facilities, and an optimization based solution approach to the problem of routing and scheduling courier trips in a multi-stage transportation system. We implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Our solution approach performs well in a range of numerical experiments. Based on analysis of implementation data describing over 20,000 samples and results transported during July-October 2019, we show that the implementation of OST routes reduced average ST delays in three districts of Malawi by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Results from our implementation demonstrate the practical feasibility of our novel approach for improving centralized ST operations in Malawi and its broader applicability to other resource-limited settings, particularly in sub-Saharan Africa.

Michael Blair (Doctoral Student, Yale School of Management), April 5

Please join us on Tuesday, April 5th, 11:45 am-12:45 pm EDT in 4200 or via Zoom link: 

Estimating the Impact of Climate Change: An Empirical Analysis of Smart Thermostat Data

Using a rich micro-level dataset, we empirically analyze smart thermostat data to understand the relationship between households' thermostat settings and their ambient environment. Using a unique methodology combining Dynamic Linear Models, random effects, and Bayesian Statistics, we develop models for short and long-term behavior. Using weather simulations we estimate the impact of climate change, and identify key patterns in household actions that drive large differences in consumption.

Yen-Shao Chen (Doctoral Student, Yale School of Management), March 29

Please join us on Tuesday, March 29th, 11:45 am-12:45 pm EDT in 4200 or via Zoom link:

Control of Nonlinear Opinion Dynamics in Social Networks

Our goal is to persuade large-scale social networks. Persuasion here means using agents to optimize a function of the opinions in the network. Many of the opinion dynamics models which capture realistic behaviors are nonlinear. This nonlinearity makes it difficult to learn a policy that optimizes the opinion function. In this study, we provide a mathematical model that describes nonlinear opinion dynamics and learn a policy to maximize the mean opinion using optimal control theory. Our control policy not only achieves the optimal objective in certain networks, but it is interpretable, scalable, and efficient for human-like agents in large-scale social networks.

Saravanan Kesavan (Professor of Operations and Sarah Graham Kenan Scholar, UNC Kenan Flagler Business School), March 8

Please join us on Tuesday, March 8th, 11:45 am-12:45 pm EDT in 4200 or via Zoom link:

Doing Well by Doing Good: Improving Store Performance with Responsible Scheduling Practices at the Gap, Inc.

We estimate the causal effects of responsible scheduling practices on store financial performance at the US retailer Gap, Inc. The randomized field experiment evaluated a multi-component intervention designed to improve dimensions of work schedules – consistency, predictability, adequacy, and employee control – shown to foster employee well-being. The experiment was conducted in 28 stores in the San Francisco and Chicago metropolitan areas for nine months between November 2015 and August 2016. Intent-to-treat (ITT) analyses indicate that implementing responsible scheduling practices increased store productivity by 5.1%, a result of increasing sales (by 3.3%) while also decreasing labor (by 1.8%). Drawing on qualitative interviews with managers and quantitative analyses of employee shift-level data, we offer evidence that the intervention improved financial performance through improved store execution. Our experiment provides evidence that responsible scheduling practices that take worker well-being into account can enhance store productivity by motivating additional employee effort and reducing barriers to employees adhering to the scheduled labor plan.

Daniela Saban (Associate Professor of Operations, Information & Technology, Stanford Graduate School of Business), March 1

Please join us on Tuesday, March 1st, 11:45 am-12:45 pm EDT in 4200 or via Zoom link:

Improving Match Rates in Dating Markets Through Assortment Optimization

Motivated by our collaboration with an online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period in order to maximize the expected number of matches in a time horizon, where a match is formed only after two users like each other, possibly in different periods. Our work combines several methodologies. We model the platform's problem as a dynamic optimization problem. We use econometric tools and exploit a change in the company's algorithm in order to estimate the users' preferences as well as other parameters of interest. We find that the number of matches obtained in the recent past has a negative effect on the like behavior of users. We propose a family of heuristics to solve the platform's problem that leverage these data findings, and use simulations to assess their benefits. Two field experiments show that our algorithm yields at least 27% more matches relative to our industry partner's algorithm. Overall, our results highlight the importance of correctly accounting for the preferences, behavior, and activity metrics of users on both ends of a transaction to improve the operational efficiency of matching platforms.

Kejia Hu (Assistant Professor of Operations Management, Vanderbilt University, Owen Graduate School of Business), February 1

Please join us on Tuesday, February 1st, 11:45 am-12:45 pm EDT via Zoom link:

To What Extent Do Workers’ Preferences Matter?

Our research investigates how preference satisfaction, particularly intrinsic values such as psychological comfort, can improve a worker’s service efficiency and quality. Examining a comprehensive dataset linking surgeons’ performances to their preferences for operating rooms, we not only confirm the significant role of intrinsic values in driving workers’ service efficiency and quality but also quantify the preference satisfaction effect as large enough to serve as a new managerial lever for firms. However, we also find that compared to workers without preferences, workers with preferences perform better if satisfied, but worse if unsatisfied. This suggests that firms should consider the cultivation of workers’ preferences only if their systems can satisfy their workers. Furthermore, our second-order analysis suggests that in a restricted system, managers should prioritize satisfying workers with heavy workloads or complex tasks to achieve the greatest improvement. Finally, we update the surgery scheduling framework by incorporating surgeons’ preferences. Our counterfactual analysis demonstrates that preference satisfaction can achieve huge benefits in operation cost saving and patient welfare improvement at little expense. For the operations in our sample, we find satisfying surgeons’ preferences can reduce healthcare costs by over 4 million dollars, not to mention the potential for significant improvement in patients’ and surgeons’ welfare.


Ozan Candogan (Associate Professor of Operations Management, University of Chicago Booth School of Business)

Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures

Andrew (Di) Wu (Assistant Professor of Technology and Operations,  Stephen M. Ross School of Business, University of Michigan)

Text-Based Measure of Supply Chain Risk Exposure

Peter Frazier (Professor of Operations Research and Information Engineering, Cornell University)

Modeling for COVID-19 College Reopening Decisions: Cornell, A Case Study

Nitish Jain (Assistant Professor of Management Science and Operations, London Business School)

The Impact of Trade Credit Provision on Retail Inventory: An Empirical Investigation Using Synthetic Controls

Ioannis Stamatopoulos (Assistant Professor of Operations Management, McCombs School of Business, The University of Texas at Austin)

Inventory Record Inaccuracy Explains Price Rigidity in Perishable Groceries

Samantha Keppler (Assistant Professor of Technology and Operations, University of Michigan, Stephen M. Ross School of Business)

Crowdfunding the Front Lines: An Empirical Study of Teacher-Driven School Improvement

Vijay Kamble (Assistant Professor of Information and Decision Sciences, University of Illinois, Chicago)

Individual Fairness in Hindsight (joint with Swati Gupta)

Forrest Crawford (Associate  Professor of Biostatistics, Statistics and Data Science, Ecology and Evolutionary Biology, and Management, Yale University)

Covid-19 Transmission Models in the Real World: Models, Data and Policy

David Alderson (Professor of Operations Research, Naval Post Graduate School)

Rethinking Resilience

Basak Kalkanci  (Assoicate Professor of Operations Management, Georgia Tech)

How Transparency into Internal and External Responsibility Initiatives Influences Consumer Choice

Nikhil Garg (Postdoctoral Associate, UC Berkeley) 

Dropping Standardized Testing for Admissions: Differential Variance and Access

Antoine Desir (Assistant Professor, INSEAD)

Incentive Compatible Assortment Optimization


Sharad Goel (Assistant Professor of  Management Science and Engineering, and by Courtesy, of Computer Science, Sociology and Law, Stanford University)
Designing Equitable Algorithms for Lending and Beyond

Elisa Long (Assistant Professor of  Decisoins, Operations, and Technology Management, UCLA, Anderson School of Management)
Nursing Home Staff Newtorks and Covid-19

Yao Cui (Assistant Professor of  Operations,  Technology and Management, Cornell University)
Tax Induced Inequalities in the Sharing Economy

Simone Marinesi (Assistant Professor of  Operations, Information and Decisions,  University of Pennsylvania, Wharton)
Rethinking Crowdfunding Platform Design: Mechanisms to Deter Misconduct and Improve Efficiency

Yaron Shaposhnik (Assistant Professor, Simon Business School, University of Rochester)
Globally-Consistent Rule-based Summary Explanation for Machine Learning Models: Application to Credit-Risk Evaluation

Shouqiang Wang (Assistant Professor, Operations Management, University of Texas at Dallas, Naveen School of Management)
Engineering Social  Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms

David Shmoys (Laibe/Acheson Profesor of Business Management and Leadership, Department of Computer Science, Cornell)
Models and Algorithms in Support of Modern Urban Mobility  -joint with Daniel Freund, Shane Henderson and Eoion O’Mahony

Jeho Lee (Professor of International Business, Graduate School of Business, Seoul National University)
In Search of Robust Design Principles: Anomalies in the Hub-and-Spoke Networks of Major US Carriers

 Kimon Drakopoulos (Assistant Professor of Data Sciences and Operations, University of Southern California)
Why Perfect Tests May Not be Worth Waiting For: Information as a Commodity   -joint with R.S. Randhawa 

 Hamsa Sridhar Bastani (Assistant Professor of Operations, Information and Decisions, Wharton School of Business)
Predicting with Proxies: Transfer Learning in High Dimension


Oguzhan Alagoz (Visiting Professor of Operations at Kellogg School of Management and Proctor and Gamble Bascom Professor of Industrial Systems Engineering, University of Wisconsin-Madison)
A Mathematical Modeling Framework to Personalize Mammography Screening Decisions

Florin Ciocan (Assistant Professor of Technology and Operations Management, INSEAD)
Interpretable Optimal Stopping

John Birge (Jerry W. and Carol Lee Levin Distinguished Service Professor of Operations Management, University of Chicago, Booth)
Dynamic Learning in Strategic Pricing Games

Foad Iravani (Assistant Professor of Operations Management, University of Washington, Foster)
A Reduce-To-Threshold Approach to Direct Load Control Contracts with Monthly Constraints

Karl Sigman (Professor of Industrial Engineering and Operations Research, Columbia University)
Exact/Perfect Simulation with Applications in Operations Research

Karen Smilowitz (Industrial Engineering and Management Sciences, Northwestern University)
Leveraging network structure in covering path problems: An application to school bus routing

Roman Kapuscinski (John Psarouthakis Research Professor of Manufacturing Management, University of Michigan)
Strategic Behavior of Suppliers in the Face of Production Disruptions

Martin Lariviere (John L. and Helen Kellogg Professor of Operations, Kellogg School of Business, Northwestern University)
Priority Queues and Consumer Surplus

Jose Guajardo (Haas School of Business, University of California, Berkeley)
How Do Usage and Payment Behavior Interact in Rent-to-Own Business Models? Evidence from Developing Economies

Mohamed Mostagir (Assistant Professor of Technology and Operations, Ross School of Business, University of Michigan)
Dynamic Contest Design: Theory, Experiments, and Applications

Junior Operations Conference

February 8 and 9, 2018 

  • Maria Ibanez (Harvard)- “Discretionary Task Ordering: Queue Management in Radiological Services”
  • Ali Makhdoumi (MIT)- “Fast and Slow Learning from Reviews”
  • Irene Lo (Columbia)- “Dynamic Matching in School Choice: Efficient Seat Reassignment after Late Cancellations”
  • Daniel Freund (Cornell)- “Models and Algorithms for Transportation in the Sharing Economy”
  • Chloe Glaeser (Wharton)- “Optimal Retail Location: Empirical Methodology and Application to Practice”
  • Joann De Zheger (Stanford)- “Pay It Forward: Sustainability in Smallholder Commodity Supply Chains”


Marcelo Olivares (Industrial Engineering, Universidad de Chile)
Managing Worker Utilization in Service Platforms: An Empirical Study of an Outbound Call-Center  -joint with Andres Musalem and Daniel Yung

Peter Kolesar (Professor Emeritus, Columbia University, Member, Columbia Water Center)
Breaking the Deadlock: Improving Water-Release Policies on the Delaware River through Operations Research

Yash Kanoria (Associate Professor, Decision, Risk and Operations Division, Columbia Business School, Columbia University)
State Dependent Control of Ride Hailing Systems   -joint with Sid Banerjee and Pengyu Qian

Philipp Afèche (Associate Professor of Operations Management, Rotman School of Management, University of Toronto)
Ride-Hailing Networks with Strategic Drivers: The Impact of Platform Control Capabilities on Performance  -joint with Costis Maglaras and Zhe Liu

Andre Calmon (Assistant Professor of Technology and Operations Management and the Patrick and Valentine Firmenich Fellow for Business and Society at INSEAD)
Operations Strategy at the Base of the Pyramid: Consumer Education and Reverse Logistics in a Durable Goods Supply Chain

Shane G. Henderson (Professor and Director, School of Operations Research and Information Engineering)
Citi Bike Planning  -joint with Daniel Freund, Nanjing Jian, Eoin O’Mahony and David Shmoys

Rene Caldentey (Professor of Operations Management, The University of Chicago , Booth)
On the Optimal Design of a Bipartite Matching System    -joint with Philipp Afeche (U. Toronto) and Varun Gupta (U Chicago)

Johannes Ledolter (Professor of Management Sciences and Statistics & Actuarial Scince, Robert Thomas Holmes Professor, The University of Iowa, Tippie College of Business)
Data Science Projects: Treatment of Visual Loss and Effects of Traumatic Brain Injury

Paat Rusmevichientong (Professor, Data Sciences and Operations, University of Southern California, Marshall School of Business)
A New Approach in Approximate Dynamic Programming for Revenue Management of Reusable Products)   -joint with Huseyin Topaloglu and Mika Sumida (Cornell Tech)

Hamed Mamani (Associate Professor of Operations Management, Premera Endowed Professor, University of Washington, Foster School of Business)
Payment Models in Healthcare

Pinar Keskinocak (Willam W. George Chair and ADVANCE Professor and Interim Associate Dean for Faculty Development & Scholarship, College of Engineering and Director of the Center for Health and Humanitarian Systems, Georgia Tech Steward School of Industrial and System Engineering)
Quantitative Models Embedded in Decision-Support Tools for Healthcare Applications


Nicholas Arnosti (Assistant Professor, Decision, Risk and Operations, Columbia Business School)

Georgia Perakis  (Professor of Operations Research, Statistics and Operations Management, MIT Sloan School of Management)
Peak-End Demand Models and their Impact on Hard Promotion Planning Problems

Gad Allon (Professor of Operations, Information and Decisions, Wharton, UPenn)
Managing Service Systems in the Presence of Social Networks

Mohsen Bayati (Associate Professor of Operations, Information and Technology, Graduate School of Business, Stanford University)
Avoiding the Exploration-Exploitation Tradeoff in Personalized Decision-Making

Arash Asadpour (Assistant Professor of Information, Operations and Management Sciences, NYU, Stern School of Business)
Concise Bidding and Multidimensional Budget Constraints

Gad Allon (Professor of Operations, Information and Decisions, U Penn, The Wharton School)
Two Operations Management Problems in Criminology 

Mor Armony (Professor of Information, Operations, and Management Sciences, NYU, Stern School of Business)
Pooling Queues with Work Averse Servers

Michael Steele (Professor of Statistics, Professor of Operations, Information and Decisions, UPenn, The Wharton School)
Expectations: What Makes Them Great?

Ilan Lobel (Professor of Operations Management in the Department of Information, Operations and Management Sciences, NYU, Stern School of Business)
Feature-based Dynamic Pricing

Junior Operations Conference

February 2 and 3, 2017

  • John Silberholz (MIT)- “An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer”
  • Hamsa Bastani (Stanford)- “Online Decision-Making Algorithms for Data-Driven Operations”
  • Swati Gupta (MIT)- “Learning Combinatorial Structures”
  • Ali Aouad (MIT)- “Revenue Management in Face of Choice Heterogeneity”
  • Negin Golrezaei (USC)- “Real-Time Optimization of Personalized Assortments”


Noah Gans (Professor of Operations, Information and Decisions, UPenn, The Wharton School)
Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

Brian Tomlin
(Professor of Business Administration, Dartmouth University,Tuck School of Business)
Dual Co-Product Technologies: Implications for Process Development and Adoption

Assaf Zeevi (Professor of Decision, Risk and Operations, Graduate School of Business, Columbia University)
Chasing Bandits: (a bit of) Theory and (some) Applications

Dimitris Bertsimas (Massachusetts Institute of Technology)
Personalized Diabetes Management Using Electronic Medical Records

Pen Sun (Professor in Decision Sciences, Fuqua School of Business, Duke University)
Optimal Contract to induce Continued Effort

Jan Van Mieghem (Professor of Operations, Kellogg School of Management, Northwestern)
Collaboration and Multitasking in Processing Networks: Humans versus Machines

Mustafa Akan (Assistant Professor of Operations Management, The Tepper School of Business, Carnegie Mellon University)
Towards an Equitable Alloction of Organs Among End-Stage Liver Disease Patients

Moshe Kress (Professor of Operations Research, The Naval Postgraduate School)
When is Information Sufficient for Action? Search with Unreliable Yet Informative Intelligence

L. Beril Toktay (Brady Family Chairholder and the Scheller College of Business ADVANCE Professor, Georgia Institute of Technology)
Drawn from Efficient Implementation of Collective Extended Producer Responsibility Legislation and Design Incentives under Collective Extended Producer Responsibility: A Network Perspective

Vivek Farias (Associate Professor of Operations Management, The Sloan School of Management, MIT)
Optimal A-B Testing

Gerard Cachon (Fred R. Sullivan Professor of Operations, Information, and Decisions The Wharton School, University of Pennsylvania)
The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Joseph Milner (Professor of Operations Management, The Rotman School of Management, University of Toronto)
Dynamic Patient Scheduling for Multi-Appointment Health Care Programs

Forrest Crawford (Department of Biostatistics, Yale School of Public Health)
Link-tracing studies of hidden networks in epidemiology  and public health

Junior Operations Conference

January 14 and 15, 2016

  • Ken Moon (Stanford GSB) – “Randomized Markdowns and Online Monitoring”​
  • Ying Liu (NYU) – "Intertemporal Pricing under Minimax Regret"
  • Kimon Drakopoulos (MIT) – “Control of Contagion Processes on Networks”
  •  Daniel Guetta (Columbia) – “Multi-Item Two Echelon Distribution Systems with Random Demands: Bounds and Effective Strategies”
  • Dennis Zhang (Northwesten) – “Does Social Interaction Improve Service Quality? Field Evidence from Massive Open Online Education”
  • Yaron Shaposhnik (MIT) – “Exploration vs. Exploitation: Reducing Uncertainty in Operational Problems”
  • Mohammed Fazel-Zarandi (Toronto) – “Can Supply Chain Flexibility Facilitate Information Sharing?”



Margaret Brandeau (Professor of Medicine and Engineering, Stanford University)
Public Health Preparedness: Our Multi-Billion Dollar Problem/Opportunity

Abraham Seidmann (Xerox Professor of Computers and Information Systems, Electronic Commerce, and Operations Management WE Simon Graduate School of Business Administration, University of Rochester)
The Process Implications of using Telemedicine for Chronically Ill Patients: Analyzing Key Consequences for Patients and the Medical Specialists Practices

Sergei Savin (Associate Professor, University of Pennsylvania, The Wharton School)
Managing Office Revisit Intervals and Patient Panel Sizes in Primary Care

Christopher S. Tang (Carter Professor of Business Administration, UCLA Anderson School)
Economic Value of Market Information for Farmers in Developing Countries

Stephen Chick (Novartis Chaired Professor of Healthcare Management Technology and Operations Management Area, INSEAD)
A Bayesian Decision - Theoretical Model of sequential Clinical Trials with Delayed Responses

Francis de Vericourt (Associate Professor of Technology and Operations Management, INSEAD/ESMT)

Costis Maglaras (Columbia Business School)
Observational Learning and Abandonment in Congested Systems

Omar Besbes (Columbia University)
Should information collection affect operational decisions? The case of inventory management

Itai Ashlaghi (Assistant Professor of Operations Management, MIT Sloan School of Business)
Incentive Constraints in Random Matching Markets

Junior Operations Conference

February 6, 2015

  • Maxime Cohen (MIT) – “When Data Analytics Meets Promotion Pricing”
  • Daniela Saban (Columbia) – “Procurement Mechanisms for Differentiated Products”
  • Angelo Mancini (Chicago) – “Dynamic Release Management: A Quasi-Open-Loop Approach”
  • Yiangos Papanastasiou  (LBS) – “Crowdsourcing Exploration”


Retsef Levi (J. Spencer Standish (1945) Professor of Management, Professor Operations Management, Sloan School of Management, MIT)
Systematic Approach to Manage Risks of Economically Motivated Food and Drug Adulteration in Supply Chains in China

Felipe Caro (Associate Professor in Decisions, Operations, and Technology Management, UCLA Anderson)
Adoption of a markdown decision support system at Zara

Terry Taylor (Milton W. Terrill Associate Professor of Business Administration, UC Berkeley)
Supplier Evasion of a Buyer's Audit: Implications for Motivating Supplier Social and Environmental Responsibility

Hyun-soo Ahn (Associate Professor of Operations and Management Science, Ross School of Business/University ofMichigan)
The role of cost modeling in competitive bid procurement

Carri W. Chan (Associate Professor, Decision, Risk and Operations, Columbia University)
Critical Care in hospitals: When to introduce a step down unit?

Laurens Debo (Associate Professor of Operations, University of Chicago, Booth School of Business)
Inferring Quality from Wait Time

Sameer Hasija (Assistant Professor of Technology and Operations, INSEAD)
Fleet Management Coordination in Decentralized Humanitarian Operations