About

Sridhar Tayur is the Ford Distinguished Research Chair and University Professor of Operations Management at the Tepper School of Business. His role involves leading research and academic initiatives in operations management, with a focus on integrating business, technology, and analytics. As a prominent faculty member, he contributes to the school's strategic vision and thought leadership in these areas, supporting the development of innovative business education and research.

Research topics

• Computer Science
• Artificial Intelligence
• Business
• Computer Security
• Medicine
• Internal medicine
• Economics
• Management
• Marketing
• Manufacturing engineering
• Engineering
• Industrial organization
• Economic growth
• Demography
• Operations management
• Knowledge management

Selected publications

• How INFORMS Can Contribute to the Second Quantum Revolution

  INFORMS Journal on Data Science · 2026-04-09

  article1st authorCorresponding

  The Second Quantum Revolution—comprising Quantum Computing, Quantum Communications, and Quantum Sensing—holds the promise of further abilities to improve the human condition, security, and sustainability, and hence, mass prosperity, across the world, in a variety of ways, including improving health (through better diagnosis as well as new drug development, via exciting applications of Quantum Machine Learning and Quantum Sensing) as well as by enhancing communication and cybersecurity. For certain computational workloads and sensing tasks, quantum systems have demonstrated or projected energy advantages over purely digital methods at equivalent precision, although system-level comparisons remain an active and unsettled research area. Seen through a data-science lens, quantum technologies are not only physical systems but also data-generating systems whose calibration, validation, and control depend on statistical inference, hypothesis testing, and learning under uncertainty. Integer programming, queuing, Markov decision processes, and semidefinite programs are some of the fundamental methodologies in operations research (OR) and management science (MS) that are used to tackle practical applications from business (supply chain, finance), engineering (communication networks), and medicine (cancer genomics, image recognition). At the Tepper Quantum Technologies Group, we are exploring the twin questions: (a) What can quantum do for OR/MS; and (b) what can OR/MS do for quantum. This article—a companion to my 2025 INFORMS Keynote and not intended as a comprehensive survey of the field but rather as a selective perspective organized around illustrative examples from our group—provides a brief summary of our research spanning algorithms, hardware, and applications (AHA). I hope that it helps illustrate how we can contribute to the Second Quantum Revolution. History: Yu Ding served as the senior editor for this article.

  PublisherDOI

• OM Forum—Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community

  UNC Libraries · 2026-04-09

  articleOpen access

  Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human–machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI’s promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human–AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.

  PublisherDOI

• Can AI Really Transform Real-World Supply Chains Anytime Soon?

  Springer series in supply chain management · 2025-09-25 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Mission Impossible: Maximizing Deceased Liver Allograft Utilization Does Not Solve the Organ Shortage

  American Journal of Transplantation · 2025-08-01 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Capitalism, Supply Chains and Democracy

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Toward a Liquid Biopsy: Greedy Approximation Algorithms for Active Sequential Hypothesis Testing

  Management Science · 2025-08-19

  articleSenior author

  This paper addresses challenges in developing liquid biopsies for early-stage cancer detection through active sequential hypothesis testing (ASHT). In the problem of ASHT, a learner seeks to identify the true hypothesis (true cancer type) from a known set of hypotheses (candidate cancer types). The learner is given a set of actions (sequencing genetic intervals) and knows the distribution of the random outcome (whether a mutation is detected) of any action under any true hypothesis. Given a target error [Formula: see text], the goal is to sequentially select the fewest number of actions to identify the true hypothesis with probability at least [Formula: see text]. Motivated by applications in which the number of hypotheses or actions is massive (e.g., genomics-based cancer detection), we propose efficient greedy algorithms and provide the first approximation guarantees for ASHT, under two types of adaptivity. Our guarantees are independent of the number of actions and logarithmic in the number of hypotheses. Numerical tests on synthetic and real DNA mutation data show that our algorithms significantly outperform previous heuristic policies. This paper was accepted by David Simchi-Levi, healthcare management. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.00829 .

  PublisherDOI

• Multi-Armed Bandits with Endogenous Learning Curves: An Application to Split Liver Transplantation

  Manufacturing & Service Operations Management · 2025-02-06 · 2 citations

  articleSenior author

  Problem Definition: Proficiency in many sophisticated tasks is attained through experience-based learning, in other words, learning by doing. For example, transplant centers’ surgical teams need to practice difficult surgeries to master the skills required. Meanwhile, this experience-based learning may affect other stakeholders, such as patients eligible for transplant surgeries, and require resources, including scarce organs and continual efforts. To ensure that patients have excellent outcomes and equitable access to organs, the organ allocation authority needs to quickly identify and develop medical teams with high aptitudes. This entails striking a balance between exploring surgical combinations with initially unknown full potential and exploiting existing knowledge based on observed outcomes. Methodology/results: We formulate a multi-armed bandit (MAB) model in which parametric learning curves are embedded in the reward functions to capture endogenous experience-based learning. In addition, our model includes provisions ensuring that the choices of arms are subject to fairness constraints to guarantee equity. To solve our MAB problem, we propose the L-UCB and FL-UCB algorithms, variants of the upper confidence bound (UCB) algorithm that attain the optimal [Formula: see text] regret on problems enhanced with experience-based learning and fairness concerns. We demonstrate our model and algorithms on the split liver transplantation (SLT) allocation problem, showing that our algorithms have superior numerical performance compared with standard bandit algorithms in a setting where experience-based learning and fairness concerns exist. Managerial implications: From a methodological point of view, our proposed MAB model and algorithms are generic and have broad application prospects. From an application standpoint, our algorithms could be applied to help evaluate potential strategies to increase the proliferation of SLT and other technically difficult procedures. Funding: The authors acknowledge the support of CMU Tepper’s Health Care Initiative Funding. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/msom.2022.0412 .

  PublisherDOI

• Tailored First-order and Interior-point methods and a new semidefinite programming hierarchy for entanglement detection

  ArXiv.org · 2025-08-07

  preprintOpen accessSenior author

  Quantum entanglement lies at the heart of quantum information science, yet its reliable detection in high-dimensional or noisy systems remains a fundamental computational challenge. Semidefinite programming (SDP) hierarchies, such as the Doherty-Parrilo-Spedalieri (DPS) and Extension (EXT) hierarchies, offer complete methods for entanglement detection, but their practical use is limited by exponential growth in problem size. In this paper, we introduce a new SDP hierarchy, PST, that is sandwiched between EXT and DPS--offering a tighter approximation to the set of separable states than EXT, while incurring lower computational overhead than DPS. We develop compact, polynomially-scalable descriptions of EXT and PST using partition mappings and operators. These descriptions in turn yield formulations that satisfy desirable properties such as the Slater condition and are well-suited to both first-order methods (FOMs) and interior-point methods (IPMs). We design a suite of entanglement detection algorithms: three FOMs (Frank-Wolfe, projected gradient, and fast projected gradient) based on a least-squares formulation, and a custom primal-dual IPM based on a conic programming formulation. These methods are numerically stable and capable of producing entanglement witnesses or proximity measures, even in cases where states lie near the boundary of separability. Numerical experiments on benchmark quantum states demonstrate that our algorithms improve the ability to solve deeper levels of the SDP hierarchy. In particular, the PST hierarchy combined with FOMs enables scalable and effective entanglement detection in relatively easy instances, while our IPM approach offers robustness and early witness recovery for the more difficult ones. Our results highlight the benefits of tailoring algorithmic formulations to hierarchy structure to advance entanglement detection at scale.

  PublisherOA PDFDOI

• Split Liver Transplantation: An Analytical Decision Support Model

  Operations Research · 2025-04-17 · 1 citations

  article

  This research study introduces a decision support model for split liver transplantation (SLT). SLT is a procedure that can save two lives with one donated liver, thereby increasing the total benefit derived from the limited number of donated livers available. SLT may also improve equity by giving transplant candidates who are physically smaller, including children, increased access to liver transplants. However, SLT is rarely used in the United States. To help quantify the benefits of increased SLT utilization and provide decision support tools, this research presents a deceased-donor liver allocation model that incorporates both efficiency and fairness objectives. We formulate the liver waitlists as a multiqueue fluid system, incorporating specifics of donor-recipient size matching and patients’ dynamically changing health conditions. Leveraging a novel decomposition result, the study finds the exact optimal matching procedure, enabling policy makers to benchmark the performance of different allocation policies against the theoretical optimal. Numerical results, utilizing data from the Organ Procurement and Transplantation Network, show that increased utilization of SLT can significantly reduce patient deaths, increase total quality-adjusted life years, and improve fairness among different patient groups.

  PublisherDOI

• Five Starter Problems: Solving Quadratic Unconstrained Binary Optimization Models on Quantum Computers

  2025-10-01 · 4 citations

  book-chapterSenior author
  PublisherDOI

Recent grants

• New Data-Driven Methods for Managing Complex Inventory Systems in Global Supply Chains

  NSF · $265k · 2014–2017

Frequent coauthors

• Tinglong Dai

  Johns Hopkins University

  14 shared

• Alan Scheller‐Wolf

  Carnegie Mellon University

  13 shared

• Bahar Biller

  SAS Institute (United States)

  12 shared

• Raouf Dridi

  Abu Dhabi National Oil (United Arab Emirates)

  9 shared

• Hedayat Alghassi

  IBM (United States)

  9 shared

• Alp Akçay

  Eindhoven University of Technology

  9 shared

• Jayashankar M. Swaminathan

  9 shared

• Pınar Keskinocak

  8 shared

Education

• Ph.D., Operations Research

  Massachusetts Institute of Technology

  1987

• M.S., Operations Research

  Massachusetts Institute of Technology

  1983

• B.S., Mathematics

  University of Madras

  1981

Awards & honors

• Ford Distinguished Research Chair