About

The page provides information about the New York University Stern Center for Research Computing (SCRC), which is dedicated to providing world-class computational facilities and services to researchers at the Stern School of Business. The center offers a variety of services including a moderately sized Slurm HPC cluster, Cloud Computing (Virtual Machines), data acquisition and storage, research software, and access to WRDS (Wharton Research Data System). The research software suite is designed to facilitate advanced computational research and data analysis, while the datasets are sourced from diverse disciplines through collaborations with data repositories, platforms, and academic institutions. The compute services and storage systems support faculty and researchers' projects by providing a wide range of computing resources and high-speed, scalable storage solutions. The page emphasizes the center's role in supporting research activities through these technological and data resources.

Research topics

• Computer Science
• Mathematics
• Economics
• Machine Learning
• Business
• Artificial Intelligence
• Mathematical optimization
• Operations research
• Microeconomics
• Marketing
• Operations management
• Computer network
• Engineering

Selected publications

• Pricing AI as a Service

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Auction Design using Value Prediction with Hallucinations

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• On the Supply of Autonomous Vehicles in Platforms

  Manufacturing & Service Operations Management · 2025-10-21 · 2 citations

  article

  Problem definition: The ongoing large-scale deployment of autonomous vehicle (AV) technology has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. Our work studies the incentives of stakeholders in such deployments, possible misalignments, and contracting solutions to overcome them. Methodology/results: We consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human individual contractors (ICs), including (1) an open platform in which the AV supplier and ICs bring their own vehicles to the system, (2) an AV-supplier operated platform without ICs, (3) a platform that sources AVs through leasing contracts, and (4) an integrated supply chain. We find that except for the AV-only platform, all deployment models are subject to a risk of AV underutilization due to the need to maintain the ICs’ utilization sufficiently high to ensure ICs remain engaged. As AV underutilization propagates in a nonintegrated supply chain, an open platform can become arbitrarily worse than supply chain integration, and careful usage commitments are needed to overcome the efficiency loss. Managerial implications: This paper identifies a new kind of supply chain misalignment that is likely to emerge as AVs become deployed technology, which both platform operators and AV suppliers should be mindful of. Moreover, we demonstrate the value of usage commitment in the deployment of AVs through an open platform, which makes it more efficient than AV-only/AV leasing platforms. History: This paper was selected as part of the 1RR initiative between the M&SOM Journal and the MSOM Society. This particular paper was part of the 2024 MSOM Service Operations SIG Conference. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1175 .

  PublisherDOI

• Asymptotically Efficient Distributed Experimentation

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Randomly Wrong Signals: Bayesian Auction Design with ML Predictions

  ArXiv.org · 2025-02-12

  preprintOpen access1st authorCorresponding

  We study auction design when a seller relies on machine-learning predictions of bidders' valuations that may be unreliable. Motivated by modern ML systems that are often accurate but occasionally fail in a way that is essentially uninformative, we model predictions as randomly wrong: with high probability the signal equals the bidder's true value, and otherwise it is a hallucination independent of the value. We analyze revenue-maximizing auctions when the seller publicly reveals these signals. A central difficulty is that the resulting posterior belief combines a continuous distribution with a point mass at the signal, so standard Myerson techniques do not directly apply. We provide a tractable characterization of the optimal signal-revealing auction by providing a closed-form characterization of the appropriate ironed virtual values. This characterization yields simple and intuitive implications. With a single bidder, the optimal mechanism reduces to a posted-price policy with a small number of regimes: the seller ignores low signals, follows intermediate signals, caps moderately high signals, and may again follow very high signals. With multiple bidders, we show that a simple eager second-price auction with signal-dependent reserve prices performs nearly optimally in numerical experiments and substantially outperforms natural benchmarks that either ignore the signal or treat it as fully reliable.

  PublisherOA PDFDOI

• Asymptotically Efficient Distributed Experimentation

  2025-07-02

  article1st authorCorresponding
  PublisherDOI

• Reducing Marketplace Interference Bias via Shadow Prices

  Management Science · 2024-11-29 · 2 citations

  articleSenior author

  Marketplace companies rely heavily on experimentation when making changes to the design or operation of their platforms. The workhorse of experimentation is the randomized controlled trial (RCT), or A/B test, in which users are randomly assigned to treatment or control groups. However, marketplace interference causes the stable unit treatment value assumption to be violated, leading to bias in the standard RCT metric. In this work, we propose techniques for platforms to run standard RCTs and still obtain meaningful estimates despite the presence of marketplace interference. We specifically consider a generalized matching setting, in which the platform explicitly matches supply with demand via a linear programming algorithm. Our first proposal is for the platform to estimate the value of global treatment and global control via optimization. We prove that this approach is unbiased in the fluid limit. Our second proposal is to compare the average shadow price of the treatment and control groups rather than the total value accrued by each group. We prove that this technique corresponds to the correct first order approximation (in a Taylor series sense) of the value function of interest even in a finite-size system. We then use this result to prove that, under reasonable assumptions, our estimator is less biased than the RCT estimator. At the heart of our result is the idea that it is relatively easy to model interference in matching-driven marketplaces because, in such markets, the platform mediates the spillover. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01881 .

  PublisherDOI

• Frontiers in Operations: Employees vs. Contractors: An Operational Perspective

  Manufacturing & Service Operations Management · 2024-05-17 · 20 citations

  article1st authorCorresponding

  Problem definition: We consider a platform’s problem of how to staff its operations given the possibilities of hiring employees and setting up a contractor marketplace. We aim to understand the operational difference between these two work arrangement models. Methodology/results: We consider a model where demand is not only stochastic but also evolving over time, which we capture via a state of the world that determines the demand distribution. In the case of employees, the platform controls the number of employee hours it uses for serving demand, whereas in the case of contractors, it sets the wage paid to them per utilized hour. We show that although the employee problem is equivalent to a standard newsvendor, the contractor one corresponds to an unusual version of the newsvendor model where utilization is the control variable. Managerial implications: This distinction makes the contractor model more flexible, allowing us to prove that it performs significantly better, especially if the order of magnitude of demand is unknown. Meanwhile, hybrid solutions that combine both employees and contractors have complex optimal solutions and offer relatively limited benefits relative to a contractor marketplace. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0029 .

  PublisherDOI

• On the Supply of Autonomous Vehicles in Platforms

  2024-07-08 · 2 citations

  article

  The likely large-scale deployment of autonomous vehicle (AV) technology in the near future has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. In this paper, we consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human drivers that join as individual contractors (ICs). Our operational models include (1) an open platform that outsources the high capital burden of AVs by allowing the AV supplier and human drivers to bring their own vehicles into the system, (2) an AV-only platform that is operated independently by the AV supplier, (3) a platform that sources AVs from the supplier through leasing contracts, and (4) an integrated supply chain in which the same entity operates the platform and supplies the AVs. We use (4) as a benchmark to measure the performances of the other models.

  PublisherDOI

• Detours in Shared Rides

  Management Science · 2024-05-17 · 12 citations

  article1st authorCorresponding

  Detours are considered key for the efficient operation of a shared rides service, but they are also a major pain point for consumers of such services. This paper studies the relationship between the value generated by shared rides and the detours they create for riders. We establish a limit on the sum of value and detour, and we prove that this leads to a tight bound on the Pareto frontier of values and detours in a general setting with an arbitrary number of requests. We explicitly compute the Pareto frontier for one family of city topologies and construct it via simulation for several more networks, including one based on ride-sharing data from commute hours in Manhattan. We find that average detours are usually small, even in low-demand-density settings. We also find that by carefully choosing the match objective, detours can be reduced with a relatively small impact on values and that the density of ride requests is far more important than detours for the effective operations of a shared rides service. In response, we propose that platforms implement a two-product version of shared rides and limit the worst-case detours of its users. This paper was accepted by Hamid Nazerzadeh, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03125 .

  PublisherDOI

Recent grants

• ICES: Small: Collaborative Research: Selling to Networked Markets

  NSF · $59k · 2012–2013

Frequent coauthors

• Asuman Ozdaglar

  13 shared

• Omar Besbes

  Columbia University

  13 shared

• Renato Paes Leme

  12 shared

• Evan Sadler

  11 shared

• Munther A. Dahleh

  8 shared

• Daron Acemoğlu

  Massachusetts Institute of Technology

  8 shared

• Christian Borgs

  7 shared

• Francisco Castro

  Anderson University - South Carolina

  7 shared

Awards & honors

• 2018 MSOM Young Scholar Prize
• 2021 INFORMS TIMES Award
• 2014 INFORMS Revenue Management and Pricing Section Prize
• 2024 INFORMS Revenue Management and Pricing Section Prize
• 2017 Poets & Quants Top 40 MBA Professors Under 40 list