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%\title{Research Statement}
%\author{Dan Iancu}
%\date{}
%\keywords{robust optimization, multi-stage minimax, optimal policies, convex costs, dynamic, programming}


%\maketitle
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  {\Large Research Statement} \\[20pt]
%  {\Large Dan Iancu}
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%\begin{abstract}
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%\subsection{Motivation}
\par %In today's increasingly globalized world, few would doubt the central role of operational decisions in the smooth functioning of a firm. Equally true is that, while important, these decisions are also becoming increasingly difficult, particularly when considering the complexities of the globalized business environment typical for today’s companies.

%While responsible for many success stories, today's globalized business environment has also reemphasized the central role of operational decisions to the smooth functioning of a firm, and the fact that such decisions are becoming increasingly complex. 

%With the increased availability of data and inexpensive computing, recent years have witnessed a surge in the adoption of quantitative methods and tools aimed at automating as many decisions and processes as possible, across an ever-wider array of individual, business, and societal problems. %In parallel, the information technology sector has been responding to the ``analytics'' trend with numerous acquisitions and consolidations, betting on the fact that the vast amounts of data and increased computational power can generate advanced solutions for key business and societal problems. 
%While useful in simplifying routine processes, such solutions are nonetheless often limited in their scope, flexibility, and robustness. For instance, they often make unrealistic assumptions concerning the decision environment, using simplistic models of uncertainty, treating decisions in segregation, ignoring the existence of agency issues or alternative incentives, etc. Such simplifications are often a byproduct of pragmatism, sacrificing modeling realism for the sake of computational tractability.
%Such tools are doubtlessly useful; but their underlying models and algorithms often entail pragmatic choices that render computational tractability at the cost of limiting the scope, flexibility, and robustness, by ignoring salient features on the information, incentives or broader environment surrounding the various decision makers.

My research thus far has been focused on providing new ways for understanding and computing optimal decisions and strategies in complex operational settings, in ways that not only support decision-making, but also render quantitative models more pertinent to real-world settings. My work has had two main thrusts: a more methodological one, aimed at developing new tools and algorithms for decision making under uncertainty and risk, and a more qualitative one, aimed at enhancing our understanding of the interplay between key operating decisions and important financial and contractual considerations surrounding a firm and its decision makers.

\textbf{\textit{Robust Optimization (RO).}} Some of my early work on decision making under uncertainty has followed the RO paradigm, a methodology developed primarily during the last 20 years to analyze and optimize the performance of complex systems under limited information.\footnote{The framework has a long history in various disciplines (including economics, engineering, and statistics), and is built around the premise that an exact probability distribution is often unavailable in a decision process, due to, e.g., the lack of ample historical data or inherent non-stationarities. The standard approach is to assume that all unknown parameters belong to particular (suitable) uncertainty sets, and to seek decisions optimizing the worst-case performance among all possible outcomes. Through recent developments in convex optimization, RO has emerged as an eminently tractable methodology for addressing real-world problems, with recent years witnessing an explosion of applications in management science.} My work in this area has several components. In [4] and [7], I investigate several classical models for inventory management under limited demand information, and I show that a new class of replenishment policies, which depend affinely on the observed demands, is actually optimal. The result is relevant on a \emph{practical level}: such policies retain a very intuitive structure, with each new order partially satisfying the existing backlog of demands, and an optimal policy is easy to find, by solving a single linear optimization problem of small size. Furthermore, the results hold even when the underlying cost structure becomes complex (e.g., arbitrary convex), %, when traditional methodologies such as Dynamic Programming (DP) become intractable or prescribe overly complex policies. This 
which allows applying the approach to more complex capacity planning problems under uncertainty, such as the design of flexible retailer-supplier contracts with order pre-commitments and penalties. From a \emph{theoretical perspective}, the results are the first of their kind, and the proof techniques are entirely novel, combining continuous and discrete optimization to construct the optimal strategy.\footnote{In follow-up work in [6], I also examine more complex, multi-tier supply chains, where I show how asymptotically optimal replenishment policies can be computed by solving tractable optimization problems, using techniques from polynomial and semidefinite optimization. The approach performs very well in simulation studies, and places a small burden on the decision maker, allowing a direct control of the trade-off between solution quality and computational complexity through one parameter.}

At the heart of these results lies a key observation concerning a critical distinction between decision making under severe uncertainty (i.e., RO) and decision making under ample information (i.e., exact distributions or deterministic setting). Namely, the former setting generally admits \emph{multiple} optimal policies, %\footnote{Intuitively, the Bellman optimality principle may be an unnecessarily stringent criterion for the former class of models, while it is strictly required for the latter. As such, robust decision models may admit structurally ``simpler'' optimal policies, and new methodology can be developed to find such policies, going beyond the direct use of DP.} 

when traditional methodologies such as Dynamic Programming (DP) become intractable or prescribe overly complex policies


with simpler structure than the latter.

While useful for dynamic policies, this degeneracy may also cause serious inefficiencies and suboptimal performance when non-worst-case outcomes materialize. To this end, in [2], I introduce and formalize the concept of Pareto efficiency in RO. %The concept mimics the corresponding one in economics, engineering and multiobjective optimization. 
I show that decisions generated using RO need not have this property, and that particular algorithms/software used in RO problems are guaranteed to produce inefficient outcomes. I also provide simple procedures for testing whether a given solution is Pareto-efficient, and for generating Pareto-efficient outcomes. This methodology is a strict improvement of the RO framework, incurring no extra computational cost, and significantly reducing conservativeness. %The ideas and arguments in the paper are completely novel, and the research has received the first prize in the INFORMS JFIG paper competition. 

\textbf{\textit{Portfolio Management}}. Since the seminal work of Markowitz, multiple facets and extensions of this problem have been studied in the literature, and several software solutions now exist to guide trading decisions. An issue typically overlooked in most prior (implemented) models is the fact that a manager is usually in charge of several client accounts. In [1], I show that, due to market impact costs, this setting differs in several subtle ways from the classical (single account) case, with a key distinction arising from the performance of each account being coupled with the trading strategies of other accounts. I propose a novel approach for jointly determining the trades and splitting the associated market impact costs across all the accounts. The framework is flexible, allowing the manager to balance conflicting objectives, such as maximizing (risk-adjusted) aggregate wealth and distributing the gains equitably; it is also tractable, requiring convex optimization problems to be solved; furthermore, in numerical studies, it significantly outperforms existing methods employed in the industry or discussed in the literature.\footnote{Other related research, on which I do not elaborate for space reasons, includes [9], which develops optimal personalized treatments for chronic illnesses such as multiple sclerosis (through a quantitative model that explicitly uses all the channels available for acquiring information), as well as [3], which seeks to approximate a manager's static risk preferences (that may be simpler to formulate) by dynamic risk measures (that are axiomatically justified and computationally tractable.}

%Currently available medication for such diseases is often effective only for a subgroup of the population, and biomarkers accurately assessing the response of a new patient do not exist. As such, physicians learn about drug effectiveness of primarily through risky experimentation, i.e., by initiating treatment and monitoring the patient's response. Precise guidelines for discontinuing treatment are often lacking or left entirely at the physician's discretion. We introduce a quantitative framework for developing adaptive, personalized treatments for such chronic diseases. Our model acknowledges that drug effectiveness can be assessed by aggregating information from several channels, e.g., by continuously monitoring the (self-reported) state of the patient, but also by (not) observing the occurrence of infrequent health events, such as relapses or disease flare-ups. Recognizing that the timing and severity of such events carries critical information for treatment design is a key point of departure in our framework compared with typical models used in healthcare. We show that our model can be analyzed in closed form for several settings of interest, resulting in optimal policies that are intuitive and have practical appeal. We showcase the effectiveness of the methodology by developing a treatment policy for multiple sclerosis, which is able to identify non-responders earlier than standard guidelines, leading to improvements in quality-adjusted life expectancy, as well as significant cost savings.

In more recent work, I have also started examining several new issues that I believe are critical when developing operationally implementable solutions. The first is the role of agency issues, which I have explored in two classical operational contexts (sourcing in multi-tier supply chains [10], and inventory replenishment and liquidation under debt [8]). The second is the interplay between traditional operational decisions and financial, accounting or marketing considerations, which I have looked at in the context of financial covenants in inventory-based lending [8], as well as in ongoing research on dynamic pricing under debt [12] and the management of loyalty programs [11]. I briefly elaborate on some of these below.

\textbf{\textit{Disruption Risks in Supply Chains}}. Despite extensive research in supply chain management, most models examining the contractual agreements in a complex supply chain typically ignore either the agency issues or the multi-tier structure of the chain (e.g., assuming a unique tier, of direct suppliers to a manufacturer). In [10], I discuss optimal sourcing decisions in a multi-tier chain, where the manufacturer is unable to directly dictate the sourcing decisions of its immediate Tier~1 suppliers (from a set of risky Tier 2 suppliers), but can influence their strategy through the contracts offered. I find that the manufacturer's optimal strategy critically depends on the degree of overlap in the supply chain, with move overlap inducing less reliance on direct mitigation (procuring excess inventory or multisourcing in Tier~1), and more on indirect mitigation (paying larger prices, and inducing Tier 1 suppliers to mitigate risk). While the manufacturer always prefers less overlap, Tier 1 suppliers may prefer a different configuration; this results in a severe moral hazard problem, which cannot be mitigated through side payments, but can be alleviated through penalty contracts. I also show that, when the supply chain structure is unknown, sourcing according to a robust strategy can significantly reduce the  manufacturer's profit loss, particularly at low margins.

\textbf{\textit{Financial Covenants in Inventory-based Lending}}. Classical papers focusing on operational decisions such as inventory replenishment or liquidation typically ignore the financing terms through which the inventory was purchased. In [8], I study the interplay between financial covenants and the operational decisions of a retailer who obtains financing through a secured (inventory-based) lending contract. While it is widely held that covenants serve to alleviate risk and protect lenders, the ways in which a retailer adapts his operations in response have not been studied. I show that retailers under debt manage inventory through riskier policies, involving surprising (non-threshold) behavior, and characterize the market conditions (involving demand distribution, growth potential, profit margin, inventory depreciation rate) under which covenants emerge as strictly necessary terms in lending agreements. I show that these conditions are routinely met in practice, and provide operational insights for the optimal design of covenants, arguing that additional operational flexibility can impact their effectiveness in a surprising, non-monotonic way.

%Broadly speaking, my research thus far has been focused on providing new ways for understanding and computing decisions in complex operational settings, with a special emphasis on problems involving risk and severe uncertainty. My work has followed two major streams, by developing new methodology, and understanding how this can be used to guide decision-making in particular concrete applications. I elaborate on each of these below.
%On the methodological front, most of my research has followed the robust optimization paradigm, whereby a decision maker is faced with the problem of optimizing a system under very limited information about future outcomes. Prior to my work, several papers had applied such models in dynamic settings, and had advocated the use of \emph{adjustable decision rules}, as an alternative to the classical Dynamic Programming (DP) paradigm.\footnote{Such decision rules essentially stipulate that the policy governing actions at future times should depend directly on observed uncertainties, as opposed to being parameterized in the states of the system.} Of key interest was the class of \emph{linear / affine} rules, which was known to be computationally tractable, and to deliver excellent performance in a variety of applications. When limiting attention to such policies, however, a key question of concern was sub-optimality, i.e., whether these considerably underperform as compared with the optimal policy. In a sequence of papers ([1], [3]), we show that such affine policies are, in fact, \emph{provably optimal}, in several relevant operational problems, including inventory replenishment and capacity planning with convex production costs, as well as the design of flexible retailer-supplier pre-commitment contracts. From a practical viewpoint, these results are important since they provide simple and intuitive interpretations for the optimal policies, and concrete guidelines for finding them, by solving simple (e.g., linear) optimization problems, even in settings where the classical DP paradigm would be intractable or would prescribe very complex policies. From a theoretical perspective, the results are the first of their kind, combining techniques from a variety of areas, such as polyhedral geometry, super-modularity, and lattice programming. Critically, however, they are relevant since they underscore several substantial differences between decision making under severe uncertainty and decision making under exact distributional information.\footnote{Intuitively, the Bellman optimality principle may be an unnecessarily stringent criterion for dynamic RO models, while it is strictly required when the probability distribution is unique. As such, RO models may admit structurally ``simpler'' optimal policies, and new methodology can be developed to find such policies, going beyond the direct use of DP.}
%The earlier results critically depended on a feature that is intrinsic in robust decision making -- namely, that such problems often admit multiple optimal policies. While useful for finding simple policies in dynamic settings, this degeneracy may also cause serious inefficiencies in the decision process. To this end, in follow up work [5], we formalize and adapt the notion of Pareto efficiency in a robust decision-making context, and show that the classical robust optimization framework need not produce solutions with this property.\footnote{We also prove that whether such solutions are generated critically depends on the software (i.e., algorithms) used to solve the resulting optimization problems.} We also provide a simple procedure to test whether a given solution is Pareto-efficient, and (if not) to generate such Pareto-efficient solutions. The procedure generates decisions with considerably improved performance in several applications, and does not increase the computational complexity of the nominal robust framework, resulting -- essentially -- in a strict enhancement of the framework.
%On the applications front, my research has examined a broad range of problems, in supply chain management, revenue management, as well as financial engineering and healthcare. The unifying theme has been the use of quantitative tools to guide decision-making under risk, as well as a focus on understanding how operational decisions influence (and are influenced by) other considerations within and outside of the firm. I discuss some of these below.

%In [6], we deal with the problem faced by a portfolio manager in charge of multiple accounts. We argue that, due to market impact costs, this setting differs in several subtle ways from the classical (single account) case, with a key distinction arising from the performance of each individual account being coupled with the trading strategies of other accounts. We propose a novel approach for jointly determining the trades and splitting the associated market impact costs across all the accounts. Our framework is flexible, allowing the manager to balance conflicting objectives, such as maximizing (risk-adjusted) aggregate wealth and distributing the gains equitably; it is also tractable, requiring convex optimization problems to be solved; furthermore, in numerical studies, it significantly outperforms existing methods employed in the industry or discussed in the literature.

%In [7], we discuss optimal sourcing decisions in a multi-tier supply chain, consisting of three levels: a manufacturer, Tier 1 suppliers, and Tier 2 suppliers prone to disruption risk (e.g., from natural disasters, like earthquakes or floods). The manufacturer may not directly dictate which Tier 2 suppliers are used, but may influence the sourcing strategy of Tier 1 suppliers, and the resulting supply chain configuration, via contractual offerings. We find that the manufacturer's optimal strategy critically depends on the degree of overlap in the supply chain, with move overlap inducing less reliance on direct mitigation (procuring excess inventory and multisourcing in Tier 1), and more on indirect mitigation (paying larger prices, and inducing Tier 1 suppliers to mitigate disruption risk). We also show that, while the manufacturer always prefers less overlap in the supply chain, Tier 1 suppliers may prefer a different configuration; this results in a severe moral hazard problem, which cannot be mitigated through side payments, but which can be alleviated through penalty contracts. We also show that sourcing according to a robust strategy can significantly reduce the profit loss for the manufacturer, particularly at low or intermediate unit-revenues.

%In [8], we study the interplay between financial covenants and the operational decisions of a retailer that obtains financing through a secured (asset-based) lending contract. While it is widely held that covenants serve to alleviate risk and protect lenders, the ways in which a retailer adapts his operations in response have not been studied. We show that retailers under debt manage inventory through riskier policies, involving potentially surprising (non-threshold) behavior. We characterize the market conditions, involving demand distribution, growth potential, profit margin, and inventory depreciation rate, under which covenants are necessary, and argue that these are routinely met in practice. We show that covenants are not substitutable by other contractual terms, such as interest rates and loan limits, and provide operational insights for their optimal design. We discuss when covenants ensure that system-optimal decisions are taken in equilibrium, and show that additional operational flexibility can impact their effectiveness in a surprising, non-monotonic way.

%In [9], we deal with the problem of developing optimal treatments for particular chronic illnesses, such as multiple sclerosis or Crohn's disease. Currently available medication for such diseases is often effective only for a subgroup of the population, and biomarkers accurately assessing the response of a new patient do not exist. As such, physicians learn about drug effectiveness of primarily through risky experimentation, i.e., by initiating treatment and monitoring the patient's response. Precise guidelines for discontinuing treatment are often lacking or left entirely at the physician's discretion. We introduce a quantitative framework for developing adaptive, personalized treatments for such chronic diseases. Our model acknowledges that drug effectiveness can be assessed by aggregating information from several channels, e.g., by continuously monitoring the (self-reported) state of the patient, but also by (not) observing the occurrence of infrequent health events, such as relapses or disease flare-ups. Recognizing that the timing and severity of such events carries critical information for treatment design is a key point of departure in our framework compared with typical models used in healthcare. We show that our model can be analyzed in closed form for several settings of interest, resulting in optimal policies that are intuitive and have practical appeal. We showcase the effectiveness of the methodology by developing a treatment policy for multiple sclerosis, which is able to identify non-responders earlier than standard guidelines, leading to improvements in quality-adjusted life expectancy, as well as significant cost savings.

In my active research, I am following up on several of these problems, with the dual goal of (a) developing a better understanding of the incentive mechanisms at play in complex operational settings, and (b) developing tractable and implementable decision support tools. To give some concrete examples, in [12], [15] and [16], I am currently looking at the design of optimal monitoring policies, leveraging the use of IT systems to dynamically learn the operational status and performance of a borrower. In [11] and [17], I am looking at how the long-term design and management of loyalty and reward programs and of contracts for ad display is influenced by marketing, operational, but also financial and accounting considerations. Finally, in [13], I study the impact of climate risk on the design of long-term contracts, including financial subsidies for the adoption of sustainable practices in coffee and wool supply chains, and in [14] I look at policies for disaster relief in the Philippines, in the face of severe uncertainty (e.g., typhoon paths), and issues of equity in the availability of supplies. In addition to these problems, I also intend to pursue several methodological questions, related primarily to the development of specialized, computationally tractable, and data-driven tools for the design of robust long-term contractual agreements.\\[-10pt]

\begin{center}
  {\Large Teaching Statement}
\end{center}
\vspace{-5pt}

  My primary teaching responsibilities have involved the MBA classes on ``Optimization and Simulation Modeling'' (OIT 245/247)%\footnote{OIT 245/247 are part of the foundational requirement for our first-year MBA students. Both courses teach basic skills for developing structured, quantitative models suitable for business decision making. Topics include basic optimization (linear, discrete, non-linear), as well as Monte-Carlo simulation and sensitivity analysis, all taught in a spreadsheet environment (currently, Microsoft Excel). Traditionally, both courses were offered during the first half of the winter quarter, in a very compact format (9 class sessions, of 145 minutes each), and consisted primarily of lecturing, coupled with some in-class exercises.}
, taught jointly with my colleague, Kostas Bimpikis. In addition, I have also developed a new PhD class on inventory management (OIT 624), offered in alternate years. %I briefly discuss both of these below.

  During the past three years, Kostas and I have considerably reshaped the content, as well as the way in which OIT 245/247 are taught. As part of this effort, I developed six mini-cases that we currently use to illustrate applications of linear (discrete) optimization, and, in conjunction with Kostas, gradually proceeded to eliminate or substantially rewrite most of the other mini-cases, with the goal of showcasing more modern application areas of optimization and simulation. %\footnote{In particular, I developed mini-cases on: online advertising (``Marine Weekly'', 2012, reviewed in 2014), restaurant operations (``Impromptu'', 2012), optimizing family financials (``Family Financial Plan'', 2014), determining optimal diets (``Optimal Diet Problem'', 2014), rebalancing portfolios under transaction costs (``Portfolio Management'', 2014), engaging in store expansion and dual-channel operations (``Whole Wallet'', 2014), and helped write a mini-case on optimal sourcing and pricing for a coffee roasting business (``Starbucks Minicase'', 2013-2014).}  
During 2013, we also started exploring the use of educational technology, primarily with the goal of gradually ``flipping'' the classroom model. Initially, we developed videos that students could watch, either as preparation or to revisit concepts taught in class. Given the success of the videos, coupled with feedback from the students, I decided to explore an entirely different class format during winter 2014, moving away from a tiered classroom to a lab environment (the RAIL classroom, B312). Consequently, I also shifted the in-class focus away from lecturing, and more towards hands-on exercises, conducted in teams of two, under the supervision of the teaching staff. The class preparation was also reduced (students would  watch videos or read short hand-outs, and turn in weekly assignments), and the new format allowed me to ensure that weaker students are able to follow the material, while keeping more advanced students engaged. %\footnote{All teams would be required to fill out a shared Google sheet, allowing me to track real-time performance and help teams that were falling behind, while also reserving 20-30 minutes at the end of class to discuss the solution and the ``general lessons''.} 
The new format seemed to work quite well, and we are currently implementing it across all sections of OIT 245/247.

In the near future, I intend to continue experimenting with educational technology and new teaching paradigms (e.g., peer evaluations, a quantitative assignment of team composition, depending on students' backgrounds or progress, etc.), with the goal of making learning more fun and engaging, but also more effective. In terms of new material, I believe there is considerable scope for developing more classes on quantitative modeling. Over the past 2-3 iterations of OIT 245/247, I have been consistently faced with questions concerning follow-up options, only to point students to courses outside the GSB, and not entirely suited to their needs. Of key note is an advanced elective, combining optimization and simulation modeling with ideas from the Data \& Decisions classes (OIT 265/267). This new class could cover some new methodological tools (e.g., simple optimization under uncertainty, which we currently cannot teach in OIT 245/247 due to the timing of OIT 265/267), while devoting enough class time for projects around the use of data-driven analytical tools for real-world problems. Such a class would align our curriculum well with the growing focus (in industry and academia) on business analytics, while also addressing a gap in the current offerings. %Another potential offering in the MBA curriculum would be an advanced elective, focusing on the interplay between operational decisions and financial/accounting considerations. HBS currently offers such a class, and I believe this could be well tied with my own research agenda.

In terms of PhD teaching, I intend to keep offering the class on inventory management, but in a slightly changed format, devoting half the class to traditional models and theory, and half to new directions for research. I would also like to offer a new PhD class on optimization methods, better suited to students in a business school. Topics would include fundamentals (linear and nonlinear optimization, duality concepts, KKT, combinatorial optimization, etc.), but also new topics (such as conic, semidefinite, polynomial or robust optimization), covering theoretical, as well as computational and implementation issues. 

\newpage
{\it Bibliography:}
\begin{enumerate}
\item ``Fairness and Efficiency in Multiportfolio Optimization,'' D. A. Iancu and N. Trichakis, \emph{Forthcoming in \textbf{Operations Research}}.

\item ``Pareto Efficiency in Robust Optimization,'' D. A. Iancu and N. Trichakis, \emph{\textbf{Management Science}, vol. 60, No. 1, pp. 130-147, 2014}.

\item ``Tight Approximations of Dynamic Risk Measures,'' D. A. Iancu, M. Petrik, and D. Subramanian, \emph{Forthcoming in \textbf{Mathematics of Operations Research}}.

\item ``Supermodularity and Affine Policies in Dynamic Robust Optimization,'' D. A. Iancu, M. Sharma, and M. Sviridenko, \emph{\textbf{Operations Research}, vol. 61, No. 4, pp. 941-956, 2013}.

\item ``A General Purpose Local Search Algorithm for Binary Optimization,'' D. Bertsimas, D. A. Iancu, and D. Katz-Rogozhnikov, \emph{\textbf{INFORMS Journal on Computing}, vol. 25, No. 2, pp. 208-221, 2013}.

\item ``A Hierarchy of Near-Optimal Policies for Multi-stage Adaptive Optimization,'' D. Bertsimas, D. A. Iancu, and P. Parrilo. \emph{\textbf{IEEE Transactions on Automatic Control}, vol. 56, No. 12, pp. 2809-2824, 2011}.

\item ``Optimality of Affine Policies in Multi-stage Robust Optimization,'' D. Bertsimas, D. A. Iancu, and P. Parrilo. \emph{\textbf{Mathematics of Operations Research}, vol. 35, No. 2, pp. 363-394, 2010}.

\item ``Operationalizing Financial Covenants,'' D. A. Iancu, N. Trichakis, and G. Tsoukalas. Submitted to \emph{\textbf{Management Science}}.

\item ``Dynamic Learning of Patient Response Rates: An Application to Treating Chronic Diseases,'' K. Bimpikis, M. Brandeau, D. A. Iancu, and D. Negoescu. Submitted to \emph{\textbf{Management Science}}.

\item ``Disruption Risk and Optimal Sourcing in Multi-tier Supply Networks,'' E. Ang, D. A. Iancu, and R. Swinney. Revise and Resubmit, \emph{\textbf{Management Science}}.

\item ``Long-Term Management of Loyalty and Reward Programs,'' S-Y. Chun, D. A. Iancu, and N. Trichakis.

\item ``Dynamic Pricing Under Financial Covenants,'' O. Besbes, D. A. Iancu, and N. Trichakis.

\item ``Sustainability, Climate Risk, and Optimal Sourcing in Coffee Supply Chains,'' D. A. Iancu and Joann de Zegher.

\item ``Planning for Disaster Recovery and Relief in the Philippines,'' D. A. Iancu and J. Uichanco.

\item ``Optimal Monitoring and Liquidation in Long-Term Debt Agreements,'' D. A. Iancu, D.-Y. Yoon, and N. Trichakis.

\item ``Robust Contracting and Revolver Management in Asset-Based Lending,'' D. A. Iancu, S. Moazeni, and N. Trichakis.

\item ``Optimal Growth Policies in Online Advertising,'' D. A. Iancu, and J. Turner.

\item ``Dynamic Pricing and Customer Choice in the Theater Industry,'' S.-Y. Chun, D. A. Iancu, and Y. Yang.
\end{enumerate}


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