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 best paper awards, and he has delivered over 100 talks worldwide. He serves on the editorial boards of leading journals such as Management Science, Operations Research, and Manufacturing and Service Operations Management, and has authored over 30 peer-reviewed papers and holds three patents.

Research topics

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

Selected publications

• Integrating Choice Overload into Assortment Planning

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III

  ArXiv.org · 2025-06-29

  preprintOpen access

  As financial institutions increasingly adopt Large Language Models (LLMs), rigorous domain-specific evaluation becomes critical for responsible deployment. This paper presents a comprehensive benchmark evaluating 23 state-of-the-art LLMs on the Chartered Financial Analyst (CFA) Level III exam - the gold standard for advanced financial reasoning. We assess both multiple-choice questions (MCQs) and essay-style responses using multiple prompting strategies including Chain-of-Thought and Self-Discover. Our evaluation reveals that leading models demonstrate strong capabilities, with composite scores such as 79.1% (o4-mini) and 77.3% (Gemini 2.5 Flash) on CFA Level III. These results, achieved under a revised, stricter essay grading methodology, indicate significant progress in LLM capabilities for high-stakes financial applications. Our findings provide crucial guidance for practitioners on model selection and highlight remaining challenges in cost-effective deployment and the need for nuanced interpretation of performance against professional benchmarks.

  PublisherOA PDFDOI

• News Event-driven Forecasting of Commodity Prices

  SSRN Electronic Journal · 2024-01-01

  articleOpen access
  PublisherDOI

• Personalized Substitution Suggestions in Online Grocery Retailing

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Frontiers in Operations: News Event-Driven Forecasting of Commodity Prices

  Manufacturing & Service Operations Management · 2024-03-20 · 10 citations

  articleOpen access

  Problem definition: Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results: Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural,” “hike,” “policy,” and “elections,” all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0641 .

  PublisherOA PDFDOI

• Efficient Local-Search Heuristics for Online and Offline Assortment Optimization

  SSRN Electronic Journal · 2024-01-01

  articleOpen access
  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 terms of explanatory power, the authors characterize conditions under which CTC models become identified. Then, they propose expectation-maximization (EM) methods to solve the related estimation problem for different subclasses of CTC models, building from the provably convergent outer-approximation algorithm. Finally, subclasses of CTC models are tested on a synthetic data set and on two real data sets: one from a grocery chain and one from a peer-to-peer (P2P) car sharing platform. The results are consistent in assessing that CTC models tend to dominate RUM models with respect to prediction accuracy when the training data are noisy (i.e., transaction records do not necessarily reflect the physical inventory records) and when there is significant asymmetry between the training data set and the testing data set. These conditions are naturally verified in P2P sharing platforms and in retailers working on long-term forecasts (e.g., semester long) or geographical aggregate forecasts (e.g., forecasts at the distribution center level).

  PublisherOA PDFDOI

• The Limits of Centralized Pricing in Online Marketplaces and the Value of User Control

  Management Science · 2023 · 19 citations

  • Computer Science
  • Business
  • Industrial organization

  We report experimental and quasi-experimental evidence from a “sharing economy” marketplace that transitioned from decentralized to centralized pricing. Centralized pricing increased the utilization of providers’ assets, resulting in higher revenues but also higher transaction costs. Providers who were barred from accessing the price system made nonprice adjustments, including reducing the availability of their assets, canceling booked transactions, and exiting the market. Providers who retained partial pricing control reacted substantially less but experienced similar revenue increases. We highlight the challenges of implementing centralized pricing and assessing its welfare effects. We show that partial control can mitigate these challenges, allowing providers to express their private and heterogeneous preferences while maintaining the benefits of centralization. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: S. Jagabathula was funded in part by the Division of Civil, Mechanical, and Manufacturing Innovation [Grant 1454310]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4789 .

  PublisherDOI

• Near-Optimal Non-Convex Stochastic Optimization under Generalized Smoothness

  arXiv (Cornell University) · 2023-02-13

  preprintOpen access

  The generalized smooth condition, $(L_{0},L_{1})$-smoothness, has triggered people's interest since it is more realistic in many optimization problems shown by both empirical and theoretical evidence. Two recent works established the $O(ε^{-3})$ sample complexity to obtain an $O(ε)$-stationary point. However, both require a large batch size on the order of $\mathrm{ploy}(ε^{-1})$, which is not only computationally burdensome but also unsuitable for streaming applications. Additionally, these existing convergence bounds are established only for the expected rate, which is inadequate as they do not supply a useful performance guarantee on a single run. In this work, we solve the prior two problems simultaneously by revisiting a simple variant of the STORM algorithm. Specifically, under the $(L_{0},L_{1})$-smoothness and affine-type noises, we establish the first near-optimal $O(\log(1/(δε))ε^{-3})$ high-probability sample complexity where $δ\in(0,1)$ is the failure probability. Besides, for the same algorithm, we also recover the optimal $O(ε^{-3})$ sample complexity for the expected convergence with improved dependence on the problem-dependent parameter. More importantly, our convergence results only require a constant batch size in contrast to the previous works.

  PublisherOA PDFDOI

• Customers’ Multihoming Behavior in Ride-Hailing: Empirical Evidence from Uber and Lyft

  SSRN Electronic Journal · 2023-01-01 · 3 citations

  articleOpen access
  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