About

Dan A. Iancu is an associate professor of operations, information and technology at Stanford Graduate School of Business. He holds a BS degree in electrical engineering and computer science from Yale University, an SM in engineering sciences from Harvard University, and a PhD in operations research from MIT. His research interests lie at the interface of operations, finance, and risk management, focusing on developing new tools for dynamic optimization under uncertainty and prescriptive analytics, and applying them to operational and contracting problems in complex value chains. A particular area of recent focus has been designing better procurement, payment, and financing solutions in global supply chains, where material and financial flows impact millions of lives and the environment. His work has been published in leading journals, and he serves on the editorial boards of several of them. He has received multiple awards for his research and teaching, including best paper awards from INFORMS and teaching prizes at Harvard and MIT Sloan.

Research topics

• Computer science
• Business
• Mathematical optimization
• Mathematics
• Microeconomics

Selected publications

• Boosting Sales and Customer Welfare from Premade Foods (Let the Freshest Chicken Fly off the Shelf First)

  Management Science · 2025-08-11

  article1st authorCorresponding

  This paper examines a grocery retailer’s management of a premade food product. The retailer’s goal is to maximize a weighted sum of direct profit and customer welfare. Multiple items of the product are produced in batches and immediately displayed for sale. Considering that each item’s quality decreases while it sits on the shelf, the retailer chooses the shelf life, whether to issue items in first-in-first-out (FIFO) or last-in-first-out (LIFO) order, whether to timestamp items, and how to price items. In a base model, we find that the retailer should use LIFO issuance and not timestamp items. The intuition is that this increases customer welfare and allows for a longer shelf life, increasing sales and thus reducing waste. By extending the model, we identify features that can make FIFO optimal (such as a holding cost, upper bound on the shelf life, age-dependent disposal cost, or customer risk or loss aversion), and we show how customer heterogeneity can favor timestamps. Lastly, we show how a mandate to donate unsold food items (as implemented in France and California) can motivate a retailer to increase the shelf life, thereby reducing the quality and quantity of donated items. This paper was accepted by David Simchi-Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01246 .

  PublisherDOI

• Optimality of Linear Policies in Distributionally Robust Linear Quadratic Control

  ArXiv.org · 2025-08-16

  preprintOpen access

  We study a generalization of the classical discrete-time, Linear-Quadratic-Gaussian (LQG) control problem where the noise distributions affecting the states and observations are unknown and chosen adversarially from divergence-based ambiguity sets centered around a known nominal distribution. For a finite horizon model with Gaussian nominal noise and a structural assumption on the divergence that is satisfied by many examples -- including 2-Wasserstein distance, Kullback-Leibler divergence, moment-based divergences, entropy-regularized optimal transport, or Fisher (score-matching) divergence -- we prove that a control policy that is affine in the observations is optimal and the adversary's corresponding worst-case optimal distribution is Gaussian. When the nominal means are zero (as in the classical LQG model), we show that the adversary should optimally set the distribution's mean to zero and the optimal control policy becomes linear. Moreover, the adversary should optimally ``inflate" the noise by choosing covariance matrices that dominate the nominal covariance in Loewner order. Exploiting these structural properties, we develop a Frank-Wolfe algorithm whose inner step solves standard LQG subproblems via Kalman filtering and dynamic programming and show that the implementation consistently outperforms semidefinite-programming reformulations of the problem. All structural and algorithmic results extend to an infinite-horizon, average-cost formulation, yielding stationary linear policies and a time-invariant Gaussian distribution for the adversary. Lastly, we show that when the divergence is 2-Wasserstein, the entire framework remains valid when the nominal distributions are elliptical rather than Gaussian.

  PublisherOA PDFDOI

• Financial Access or Price Premiums? A Nuanced View into Improving Farmer Welfare and Reducing Child Labor in Commodity Supply Chains

  Springer series in supply chain management · 2024-01-01 · 1 citations

  book-chapter
  PublisherDOI

• Climate impacts of digital use supply chains

  Environmental Research Climate · 2024-01-26

  articleOpen access

  Abstract Information and communications technology (ICT) has become an indispensable part of our lives. Prior research on climate impact of ICT devices and services mostly makes use of life cycle assessment and energy modeling frameworks focused on embodied greenhouse gas (GHG) emissions. Because these perspectives emphasize the GHGs emissions associated with the construction and distribution of digital devices along production supply chains, not much is known about the GHGs emissions monitored or facilitated by digital device use. In this study, we propose the concept of digital use supply chains (DUSCs) as an orthogonal dimension of digital devices’ life cycle. DUSC refers to the production activities and resource consumption recorded by digital devices. We propose a framework to conceptualize and quantify digital behavior-related GHGs emissions through use of the Screenomics paradigm, where users’ digital screen data are unobtrusively collected moment-by-moment. Through Screenomics’ granular recording of users’ digital behavior, we evaluate behavior-based GHGs emissions traced by the digital devices. DUSC connects individual’s digital behaviors to their global climate change impact, contributing to a more nuanced and complete evaluation of the climate impacts of the digital economy. Our single-case study indicates the estimated scale of the GHGs emissions linked to a user’s one-day digital activities could be three orders of magnitude (1000 times) higher than the emissions associated with the device life cycle alone. DUSC could enable climate change mitigation at a meaningful, actionable level through personalized educational or behavior change programs, and also facilitate novel data-driven feedback loops that may provide digital device users with insights into their personal climate impacts. Recognition and future study of DUSC could accelerate the quantification and standardization of a ‘carbon handprint’ of digital devices and create positive climate impacts from digital products and services.

  PublisherOA PDFDOI

• Distributionally Robust Linear Quadratic Control

  2023-01-01

  article1st authorCorresponding
  PublisherDOI

• Area Conditions and Positive Incentives: Engaging Local Communities to Protect Forests

  SSRN Electronic Journal · 2023-01-01 · 2 citations

  articleOpen access
  PublisherDOI

• Distributionally Robust Linear Quadratic Control

  arXiv (Cornell University) · 2023-05-26 · 4 citations

  preprintOpen access

  Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations, subject to additive noise, with the goal of minimizing a quadratic cost function for the state and control variables. In this work, we consider a generalization of the discrete-time, finite-horizon LQG problem, where the noise distributions are unknown and belong to Wasserstein ambiguity sets centered at nominal (Gaussian) distributions. The objective is to minimize a worst-case cost across all distributions in the ambiguity set, including non-Gaussian distributions. Despite the added complexity, we prove that a control policy that is linear in the observations is optimal for this problem, as in the classic LQG problem. We propose a numerical solution method that efficiently characterizes this optimal control policy. Our method uses the Frank-Wolfe algorithm to identify the least-favorable distributions within the Wasserstein ambiguity sets and computes the controller's optimal policy using Kalman filter estimation under these distributions.

  PublisherOA PDFDOI

• On the Management of Premade Foods

  SSRN Electronic Journal · 2022-01-01 · 2 citations

  articleOpen access
  PublisherDOI

• Quantifying and Realizing the Benefits of Targeting for Pandemic Response

  SSRN Electronic Journal · 2021-01-01 · 3 citations

  articleOpen access
  PublisherDOI

• Quantifying and Realizing the Benefits of Targeting for Pandemic Response

  medRxiv · 2021-03-26 · 3 citations

  preprintOpen accessCorresponding

  To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific population groups or activities. Because targeting is operationally challenging and contentious, rigorously quantifying its benefits and designing practically implementable policies that achieve some of these benefits is critical for effective and equitable pandemic control. We propose a flexible framework that leverages publicly available data and a novel optimization algorithm based on model predictive control and trust region methods to compute optimized interventions that can target two dimensions of heterogeneity: age groups and the specific activities that individuals normally engage in. We showcase a complete implementation focused on the Île-de-France region of France and use this case study to quantify the benefits of dual targeting and to propose practically implementable policies. We find that dual targeting can lead to Pareto improvements, reducing the number of deaths and the economic losses. Additionally, dual targeting allows maintaining higher activity levels for most age groups and, importantly, for those groups that are most confined, thus leading to confinements that are arguably more equitable. We then fit decision trees to explain the decisions and gains of dual-targeted policies and find that they prioritize confinements intuitively, by allowing increased activity levels for group-activity pairs with high marginal economic value prorated by social contacts, which generates important complementarities. Because dual targeting can face significant implementation challenges, we introduce two practical proposals inspired by real-world interventions — based on curfews and recommendations — that achieve a significant portion of the benefits without explicitly discriminating based on age.

  PublisherOA PDFDOI

Frequent coauthors

• Nikolaos Trichakis

  Massachusetts Institute of Technology

  14 shared

• Xavier Warnes

  8 shared

• Dimitris Bertsimas

  5 shared

• Marek Petrik

  5 shared

• Dharmashankar Subramanian

  5 shared

• Pablo A. Parrilo

  5 shared

• Yonatan Gur

  Netflix (United States)

  5 shared

• Gerry Tsoukalas

  5 shared

Awards & honors

• Srivani Faculty Scholar, 2023–24
• MBA Class of 1969 Faculty Scholar, 2022–23
• Fletcher Jones Faculty Scholar for 2015–16
• Finalist for the INFORMS Pierskalla Best Paper Award, 2014
• Louise & Claude N. Rosenberg Jr. Faculty Scholar, Stanford G…