Srikanth Jagabathula

About

Srikanth Jagabathula is the Robert Stansky Research Faculty Fellow and a Professor of Technology, Operations, and Statistics at the NYU Stern School of Business. He serves as the Academic Director of the Anand Khubani BS in Business, Technology, and Entrepreneurship program. His academic journey includes a year as Visiting Associate Professor of Technology and Operations Management at Harvard Business School. He teaches courses on operations management and AI/ML across undergraduate, graduate, PhD, and executive levels, and has been recognized as one of Poets & Quants' “Top 40 Under 40 Outstanding Business School Professors” and with the NYU Stern Distinguished Teaching Award. His research focuses on using AI and ML algorithms to optimize supply chain decisions for various businesses, including traditional retailers, brands, and digital content platforms. He pioneered the use of large-scale choice models to combine operations data with customer data for solving operations problems, leveraging his deep technical expertise in mathematical optimization, machine learning, and statistics to develop scalable solutions for large operations and marketing challenges. Beyond academia, Srikanth has created practical impact through entrepreneurial ventures and advisory roles, notably with Celect Inc., acquired by Nike, which translated his doctoral research into innovative retail solutions. His contributions have earned him numerous accolades, including the NSF CAREER Award and multiple…

Research topics

• Computer Science
• Economics
• Finance
• Econometrics
• Statistics
• Business
• Industrial organization
• Microeconomics
• Mathematical optimization
• Marketing

Selected publications

• Accounting for Discrepancies Between Online and Offline Product Evaluations

  Marketing Science · 2019-01-01 · 55 citations

  article

  Consumers use different attribute partworths online and offline; these differences are consistent across consumers, and we propose a method for adjusting them.

  PublisherDOI

• A Conditional Gradient Approach for Nonparametric Estimation of Mixing Distributions

  Management Science · 2020 · 41 citations

  1st authorCorresponding

  Mixture models are versatile tools that are used extensively in many fields, including operations, marketing, and econometrics. The main challenge in estimating mixture models is that the mixing distribution is often unknown, and imposing a priori parametric assumptions can lead to model misspecification issues. In this paper, we propose a new methodology for nonparametric estimation of the mixing distribution of a mixture of logit models. We formulate the likelihood-based estimation problem as…

  PublisherDOI

• Demand Estimation Under Uncertain Consideration Sets

  Operations Research · 2023-09-04 · 30 citations

  articleOpen access1st authorCorresponding

  In “Demand Estimation Under Uncertain Consideration Sets,” Jagabathula, Mitrofanov, and Vulcano investigate statistical properties of the consider-then-choose (CTC) models, which gained recent attention in the operations literature as an alternative to the classical random utility (RUM) models. The general class of CTC models is defined by a general joint distribution over ranking lists and consideration sets. Starting from the important result that the CTC and RUM classes are equivalent in term…

  PublisherOA PDFDOI

• A Model-Based Embedding Technique for Segmenting Customers

  Operations Research · 2018-08-28 · 30 citations

  article1st authorCorresponding

  We consider the problem of segmenting a large population of customers into nonoverlapping groups with similar preferences, using diverse preference observations such as purchases, ratings, clicks, and so forth, over subsets of items. We focus on the setting where the universe of items is large (ranging from thousands to millions) and unstructured (lacking well-defined attributes) and each customer provides observations for only a few items. These data characteristics limit the applicability of e…

  PublisherDOI

• Personalized Retail Promotions Through a Directed Acyclic Graph–Based Representation of Customer Preferences

  Operations Research · 2022-01-26 · 25 citations

  article1st authorCorresponding

  A Framework to Run Personalized Promotions The availability of individual-level transaction data allows retailers to implement personalized operational decisions. Although such decisions have been around for several years now in online platforms, recent technological developments open new opportunities to extend similar practices to bricks-and-mortar settings (e.g., by using electronic price tags to show different prices to different customers or by using beacon-based technology to send promotio…

  PublisherDOI

Recent grants

• CAREER: Data to Operational Decisions: A Predictive Analytics Approach

  NSF · $317k · 2018–2019

• CAREER: Data to Operational Decisions: A Predictive Analytics Approach

  NSF · $184k · 2019–2021

• CAREER: Data to Operational Decisions: A Predictive Analytics Approach

  NSF · $500k · 2015–2018

Frequent coauthors

• Ashwin Venkataraman

  University of Dallas

  29 shared

• Devavrat Shah

  25 shared

• Lakshminarayanan Subramanian

  19 shared

• Vivek F. Farias

  Massachusetts Institute of Technology

  14 shared

• Gustavo Vulcano

  Consejo Nacional de Investigaciones Científicas y Técnicas

  10 shared

• Daria Dzyabura

  Skolkovo Moscow School of Management

  7 shared

• Eitan Muller

  6 shared

• Paat Rusmevichientong

  University of Southern California

  6 shared

Awards & honors

• NSF CAREER Award
• Wickham Skinner Early-Career Research Accomplishments Award…
• over ten best paper awards in Operations and ML
• President of India Gold Medal
• best Master’s thesis award

Similar researchers at New York University

• Michael Feldmanh-73
• Anindya Ghoseh-60
• Alexander Tuzhilinh-49