About

The page provides information about the NYU 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. These services include 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 center offers a comprehensive suite of software services designed to facilitate advanced computational research and data analysis, along with access to datasets from diverse disciplines through collaborations with data repositories, platforms, and academic institutions. Additionally, the center supports faculty and researchers' projects with a wide range of computing services and resources, including high-speed, robust, and scalable storage systems to meet diverse computational and storage needs.

Research topics

• Computer Science
• Business
• Marketing
• Political Science
• Multimedia
• Human–computer interaction
• Internet privacy
• Economics

Selected publications

• Using Generative AI and Agentic AI to Unlock Potential of Data-Driven Leadership and Democratize Traditional AI Leaders have to Overcome the Three Is of Inertia, Ignorance and Imagination

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach

  arXiv (Cornell University) · 2025-12-10

  preprintOpen accessSenior author

  Artificial Intelligence (AI) models increasingly drive high-stakes consumer interactions, yet their decision logic often remains opaque. Prevailing explainable AI techniques rely on post hoc numerical feature attributions, which fail to provide coherent narratives behind model decisions. Large language models (LLMs) present an opportunity to generate natural-language explanations, but three design challenges remain unresolved: explanations must be both decision-correct and faithful to the factors that drive the prediction; they should be able to serve multiple audiences without shifting the underlying decision rule; and they should be trained in a label-efficient way that does not depend on large corpora of human-scored explanations. To address these challenges, we introduce LEXMA (LLM-based EXplanations for Multi-Audience decisions), a reinforcement-learning-based fine-tuning framework that produces narrative-driven, audience-appropriate explanations. LEXMA combines reflection-augmented supervised fine-tuning with two stages of Group Relative Policy Optimization (GRPO). Specifically, it fine-tunes two separate parameter sets to improve decision correctness and satisfy stylistic requirements for different audiences, using reward signals that do not rely on human-annotated explanations. We instantiate LEXMA in the context of mortgage approval decisions. Results demonstrate that LEXMA yields significant improvements in predictive performance compared with other LLM baselines. Moreover, human evaluations show that expert-facing explanations generated by our approach are more risk-focused, and consumer-facing explanations are clearer, more actionable, and more polite. Our study contributes a cost-efficient, systematic LLM fine-tuning approach to enhance explanation quality for business decisions, offering strong potential for scalable deployment of transparent AI systems.

  PublisherDOI

• Privacy Choice During Crisis

  Management Science · 2025-09-25 · 1 citations

  article1st authorCorresponding

  Consumers’ privacy choice between withholding and sharing personal data may change during crises. Crises not only alter personal considerations of benefits and costs of this choice, but also trigger societal considerations, such as use of shared data in crisis management. Although the literature on privacy choice has focused on personal considerations, research on how this choice is influenced by broader circumstances remains sparse. We address this topic by leveraging newly available location big data and a global public health crisis as a natural shock. Analyzing 22 billion raw records of intertemporal individual-level mobile location data across a wide spectrum of cities in the United States, we present the first large-scale evidence that opt-out reduces during a crisis, and societal, beyond personal, considerations might have influenced consumers’ privacy choices. This paper was accepted by David Simchi-Levi, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00017 .

  PublisherDOI

• Algorithmic Pricing Network and Market Outcomes: Evidence from High-Frequency Pricing Data on Amazon

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• The Effect of Voice AI on Digital Commerce

  Information Systems Research · 2025-05-06 · 14 citations

  articleSenior author

  This research examines the impact of voice-activated shopping assistants, such as Alibaba’s Tmall Genie, on consumer behavior in online shopping. Analyzing real-world purchase data using econometric models, this work finds that the average consumer’s weekly spending on Alibaba increased by 16.6% within the first four months after adopting voice AI. Additionally, we explore specific product features that moderate the effect of Genie adoption by examining the repeat purchase and product substitutability and familiarity, supporting a mechanism that involves reducing information acquisition costs. The positive effects of Genie adoption remain significant on repeat purchases in the long term, although they attenuate over time. Furthermore, our analyses reveal that on average, the voice channel has a positive spillover effect on spending on the PC channel but no significant effect on the mobile channel. The channel dynamics are contingent on specific shopping contexts. These results demonstrate that voice AI devices with shopping capabilities can enhance the growth of the affiliated e-commerce platform. As the first study to empirically examine the impact of voice AI adoption on e-commerce consumption, our paper provides valuable implications for e-commerce platforms and retailers leveraging voice-activated shopping.

  PublisherDOI

• From Aversion to Adoption: The Role of Promotion Design in Mitigating Algorithm Aversion

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Exploring Generative AI’s Impact on Research: Perspectives from Senior Scholars in Management Information Systems

  ACM Transactions on Management Information Systems · 2025-03-26 · 5 citations

  articleOpen access

  This commentary reflects on insights from a panel discussion at the 2024 Annual MIS Academic Leadership Conference, where six senior MIS scholars discussed the impact of Generative AI on scholarly research and peer review. The discussion underscored the importance of responsible use, transparency, and ethical standards, as well as the irreplaceable role of human judgment in maintaining research integrity. This commentary explores the potential of Generative AI as a collaborative tool across various stages of the research lifecycle, highlighting the "human-in-the-loop" approach to harness AI's capabilities while preserving essential human insight. This commentary synthesizes the senior scholars’ perspectives on the responsible integration of Generative AI, emphasizing opportunities to enhance research efficiency and foster interdisciplinary collaboration, while advocating for policies that ensure AI supports—rather than substitutes—human intellectual contributions in academic research.

  PublisherOA PDFDOI

• The Impact of Visual Generative AI on Advertising Effectiveness

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach

  ArXiv.org · 2025-12-10

  articleOpen accessSenior author

  Artificial Intelligence (AI) models increasingly drive high-stakes consumer interactions, yet their decision logic often remains opaque. Prevailing explainable AI techniques rely on post hoc numerical feature attributions, which fail to provide coherent narratives behind model decisions. Large language models (LLMs) present an opportunity to generate natural-language explanations, but three design challenges remain unresolved: explanations must be both decision-correct and faithful to the factors that drive the prediction; they should be able to serve multiple audiences without shifting the underlying decision rule; and they should be trained in a label-efficient way that does not depend on large corpora of human-scored explanations. To address these challenges, we introduce LEXMA (LLM-based EXplanations for Multi-Audience decisions), a reinforcement-learning-based fine-tuning framework that produces narrative-driven, audience-appropriate explanations. LEXMA combines reflection-augmented supervised fine-tuning with two stages of Group Relative Policy Optimization (GRPO). Specifically, it fine-tunes two separate parameter sets to improve decision correctness and satisfy stylistic requirements for different audiences, using reward signals that do not rely on human-annotated explanations. We instantiate LEXMA in the context of mortgage approval decisions. Results demonstrate that LEXMA yields significant improvements in predictive performance compared with other LLM baselines. Moreover, human evaluations show that expert-facing explanations generated by our approach are more risk-focused, and consumer-facing explanations are clearer, more actionable, and more polite. Our study contributes a cost-efficient, systematic LLM fine-tuning approach to enhance explanation quality for business decisions, offering strong potential for scalable deployment of transparent AI systems.

  PublisherOA PDF

• Mobile Push vs. Pull Targeting and Geo-Conquesting

  Information Systems Research · 2024-03-13 · 9 citations

  articleOpen access

  Firms have two distinct options when delivering content to consumers’ mobile devices: mobile push and mobile pull. Mobile push delivers firm-initiated (ad) content directly to consumers, while mobile pull requires consumers to initiate requests for (ad) content. This study tests the impact of mobile push and mobile pull on consumers’ coupon redemption behavior in a large-scale randomized field experiment in a geo-conquesting setting, targeting customers located around competitor retail stores with mobile coupons to drive them to stores of the focal retailer. The results show that mobile push increases coupon redemption rates by 6.0%, with substantial heterogeneity based on app-specific use experience and store density: App-specific use experience negatively moderates the effect of mobile push delivery on redemptions, likely because both usage experience and push notifications reduce app-specific search costs, thereby acting as substitutes for one another. In areas with higher store density, the positive effect of mobile push delivery on the redemption likelihood is greater, suggesting that push notifications can highlight the focal coupon among alternative store choices, thereby reducing consumer switching costs. These findings have important implications for retailers and brands in creating competitive mobile targeting campaigns that effectively leverage both mobile push and pull delivery mechanisms.

  PublisherOA PDFDOI

Recent grants

• CAREER: Identifying and Measuring the Economic Value of Information on the Internet

  NSF · $498k · 2007–2013

Frequent coauthors

• Beibei Li

  91 shared

• Yuanyuan Dang

  South China University of Technology

  66 shared

• Xitong Guo

  66 shared

• Raghuram Iyengar

  University of Pennsylvania

  40 shared

• S. Sriram

  Ross School

  38 shared

• Catherine E. Tucker

  37 shared

• Sriraman Venkataraman

  Quantitative BioSciences

  37 shared

• Hanna Hałaburda

  37 shared

Awards & honors

• AIS Fellow Award, Association of Information Systems, (2022)
• Distinguished Alumni Award, IIM Calcutta, (2022)
• ICIS Best Impact Paper Award, (2022)
• ISR Best Paper Runner Up Award, (2021)
• Inaugural Practical Impact Award, INFORMS ISS, (2020)