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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 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 dynamic optimization 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.

Some of my early work on dynamic optimization under uncertainty has followed the \textbf{\textit{Robust Optimization (RO)}} paradigm, a methodology developed 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., a lack of ample historical data or inherent non-stationarities. The standard approach is to assume that only some partial information is available (e.g., support, moments, etc.), and to seek feasible decisions that optimize the worst-case (expected) performance.} %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 primary focus has been on dynamic (i.e., multi-stage) problems, where certain decisions can be delayed until some uncertainty is resolved. 
My research started with \cite{BerIanParMOR}, where I investigated a classical model for inventory management under limited demand information, for which I showed that a new class of ordering policies, depending affinely on the history of observed demands, is actually optimal. The result is \emph{practically} relevant: such policies retain an intuitive interpretation (new orders are assigned to partially meet all backlogged demands), and optimal policies are easy to find, by solving a single (linear) optimization problem of small size. Interestingly, in \cite{IanShaSvi}, I showed that such ``simple'' policies remain optimal even if the cost structure becomes ``complex'' (e.g., arbitrary convex ordering costs), when typical approaches such as Dynamic Programming (DP) become intractable or prescribe very complex policies. This allows adapting the approach to more complex capacity planning problems under uncertainty, such as the design of flexible retailer-supplier contracts with pre-commitments and penalties. %\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.}
From a \emph{theoretical} perspective, these result were the first of their kind, and the proof techniques were entirely novel, combining continuous and discrete optimization to construct the optimal strategy.\footnote{In [6], I also examined more complex supply networks, where I showed how optimal policies can be obtained by solving a hierarchy of tractable, convex optimization problems. The approach performs well in simulation, and places little burden on the decision maker, who can adjust the performance-computation trade-off through a single parameter.}

Enabling these results was a simple---yet critical---observation that %the well-known Bellman optimality principle of dynamic programming (DP) is no longer a {\it necessary} criterion for optimality in dynamic RO models \cite{DelIan}. Put differently,
 my work pointed out: namely, that {\it multiple} solutions or policies typically exist in RO that achieve the same worst-case performance, but only some of which also satisfy the well-known Bellman optimality principle \cite{DelIan}. In this sense, my work in \cite{BerIanParMOR,BerIanParIEEE,IanShaSvi} showed how this degeneracy can help: among these policies, some may have a ``simple'' structure, and be obtained by solving ``simple'' problems, without resorting to potentially intractable DP recursions. To argue the existence and to retrieve such policies though, one cannot entirely rely on the usual recursive DP arguments, and new techniques must be developed. 

% At the heart of these results lies a simple -- but critical -- observation that my work highlighted. Namely, that the well-known Bellman optimality principle of dynamic optimization is a sufficient but {\it not} a necessary criterion for optimality in dynamic RO models, where the exact distribution is unknown

% In this context, my work made a simple -- but somewhat critical -- observation that the well-known Bellman optimality principle, which is a necessary and sufficient governs optimal solutions to a dynamic problem under ample information (e.g., when exact distributions are known)

%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.} with simpler structure than the latter.

In a different sense, the same observation also highlighted a potentially serious flaw of the RO paradigm: namely, that the degeneracy may cause severe inefficiencies and suboptimal performance when non-worst-case outcomes materialize. To this end, in [2], I introduced and formalized the concept of Pareto efficiency in RO. %The concept mimics the corresponding one in economics, engineering and multiobjective optimization. 
I showed that decisions generated using classical RO methods need not have this property, and that particular algorithms/software used in RO problems are {\it guaranteed} to produce inefficient outcomes. I also provided simple procedures for testing whether a given solution is Pareto-efficient, and for generating Pareto-efficient outcomes. This methodology was 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. 

This early research allowed me to recognize some of the benefits and pitfalls of RO first-hand, and has lead me to subsequently explore in more depth its connections with axiomatic approaches for decision-making under limited information and ambiguity, developed in economics, decision analysis, and mathematical finance.\footnote{Related research on which I do not expand includes [3], which proposes approximating a decision maker's static risk preferences (which may be ``easy'' to state, but dynamically inconsistent) through axiomatically justified dynamic risk measures.} I remain convinced that RO carries benefits, both in providing computational solutions to complex problems, %that might be otherwise intractable (e.g., under exact distributional information), 
but also in supporting ``simple'' solutions that seem to work well (in keeping with the established principle that the more tailored solutions are to existing information, the more sensitive they become to slight changes...) At the same time, I have also come to believe that na\"{i}vely applying RO methods without accounting for new information or preferences beyond worst-case scenarios can generate considerable inconsistencies and losses, compounding the inherent conservativeness of the approach. 

In this spirit, some of my active research continues exploring certain complex problems through a robust/limited-information lense, aiming to uncover (conditions leading to) simple rules and policies, while accounting for the possibility of learning from new information. For instance, in \cite{IanTriYoo}, I consider the problem of determining when to monitor and possibly alter the course of several processes governing future outcomes---a generic problem with many applications, from contracting (e.g., collateralized lending, where the borrower's assets and cashflows are dynamically evaluated to assess whether the outstanding loan is ``safe'') to healthcare (where physicians determine the frequency of office visits or testing procedures in the treatment of chronic conditions\footnote{Related research includes \cite{NegBimBraIan}, which develops adaptive treatment policies for chronic illnesses based on dynamically monitoring a new patient's state in order to reduce the uncertainty related to the patient's response to treatment.}). In a robust model where monitoring results in new information about future process values, I provide general conditions under which pre-committing to a set of equi-distant monitoring times is just as good as adjusting these times dynamically. Similarly, in \cite{BidIanSwi}, I study the design of procurement strategies under disruption risk and limited information about the firm's deeper supply tiers, discussing when a simple uniform split of orders among immediate suppliers is optimal. 

More broadly, I believe that robustness can play an important role in analyzing decisions involving complex systems, involving individuals, firms, and societies at large. As some of the examples began to highlight, one such immediate area is the design of (long-term) contracts between multiple parties with decision-making power, where uncertainty, dynamics, moral hazard, contractual incompleteness, and learning are critical features to take into account. This also brings me to the second main thread in my research, which has focused on understanding how contractual arrangements---and particularly material and financial flows---can be designed or improved when the various parties are modeled in more detailed and operationally-salient ways.\footnote{Additional related research includes \cite{IanTrich}---which discusses how transaction costs due to market impact could be split and how trading decisions could be allocated to balance fairness and efficiency when portfolio managers simultaneously handle multiple client accounts, and \cite{ChuIanTri}---which proposes systematic ways for setting the value of points in a loyalty program, and discusses how firms abiding by IFRS accounting standards can use their loyalty programs as hedging tools against uncertainty.}

%Some of my initial projects in this area study firms' incentives to use their operating capabilities to shift risk to their creditors, once their debt is in place. The goal is both to understand the operating distortions caused by such risk-shifting incentives, but also to quantify the degree to which common lending contract terms can mitigate the resulting inefficiencies (i.e., agency costs) through a suitable, ``operations-centric'' design. I focus on inventory-heavy firms endowed with flexibility in replenishing inventory and/or in dynamically adjusting prices to liquidate remaining stock \cite{IanTrichTsou,BesIanTri}. In this context, my work documents substantial operating distortions---with non-threshold inventory policies \cite{IanTrichTsou} and (increasingly) smaller price markdowns that compound over time \cite{BesIanTri}---leading to (increasingly) substantial agency costs when debt is fairly priced. However, I find that practical terms and contingencies commonly encountered in lending contracts---such as financial or borrowing base covenants (combined with suitably appraising the collateralized inventory) or amortization schedules---are either able to fully alleviate inefficiencies \cite{IanTrichTsou} or at least substantially reduce them \cite{BesIanTri}. By explicitly characterizing the optimal covenant tightness, my work also highlights that inventory-heavy firms with enhanced operating flexibility and/or operating in better markets must also be willing to abide by more and/or tighter covenants, so as to fully reap the benefits of their improved operating conditions \cite{IanTrichTsou}. 

Some of my initial projects in this area study the inefficiencies, or agency costs, that arise when leveraged firms use their operating capabilities to shift risk to their creditors, and quantifies the degree to which common debt contracts are able to mitigate them through a suitable, ``operations-centric'' design. My work documents substantial operating policy distortions when inventory-heavy firms are capable of adjusting their inventory levels and/or dynamically price to liquidate remaining stock. Specifically, I find that they tend to liquidate less inventory \cite{IanTrichTsou} and apply (increasingly) smaller price markdowns that compound over time \cite{BesIanTri}. These distortions lead to (increasingly) substantial agency costs when debt is fairly priced. However, I find that practical terms and contingencies commonly encountered in debt contracts---such as financial or borrowing base covenants (combined with suitably appraising the collateralized inventory) or amortization schedules---are either able to fully alleviate inefficiencies \cite{IanTrichTsou} or at least substantially reduce them \cite{BesIanTri}. By explicitly characterizing the optimal covenant tightness, my work also highlights that inventory-heavy firms with enhanced operating flexibility and/or operating in better markets must also be willing to abide by more and/or tighter covenants, so as to fully reap the benefits of their improved operating conditions \cite{IanTrichTsou}.

This work also highlights that the most effective contractual mechanisms the borrower's cashflows (and collateralized inventory), thus highlighting the importance of {\it dynamic} risk controls when lending to borrowers with enhanced operating flexibility. 

In a different Additional research in this area focused on understanding how procurement contracts can be designed with a broader view of the firm's supply base, including its immediate and distant suppliers. This work was motivated to a large degree by discussions with industry partners; it includes \cite{AngIanSw}---where I examine the sourcing strategies of a firm that cannot directly contract with its more distant and potentially unreliable suppliers, but can influence the sourcing strategies of its immediate suppliers through contracts---as well as \cite{ZegIanLee}---where I study how firms sourcing from many small suppliers (as is typical in agricultural supply chains) can incentivize the adoption of better management practices and create shared value through choices of sourcing channels and payment functions. The findings therein confirm certain observations reported in the media and by the industry partners, such as the diamond-shaped supply chains that Toyota discovered in its internal investigation following the T$\bar{\textup{o}}$hoku earthquake \cite{AngIanSw}, or the success/failure of simple changes in contracting channels in the wool supply chain \cite{ZegIanLee}. They also showcase the importance of certain operational and structural details in the contract design (e.g., the degree of overlap in the deeper supply tiers \cite{AngIanSw}, their segmentation \cite{ZegIanLee}, their relative cost-effectiveness \cite{AngIanSw,ZegIanLee}, etc.), while quantifying the ``sticks'' \cite{AngIanSw} or ``carrots'' \cite{ZegIanLee} that can improve efficiency. The work also suggests immediate follow-up research focused on better understanding sourcing in complex supply chains with limited visibility \cite{BidIanSwi}, but also a more broad research agenda, aimed at understanding how large firms can coordinate their procurement and 

%Motivated by a discussion that Robert Swinney and I had with a senior procurement executive at Toyota in the aftermath of the T$\bar{\textup{o}}$hoku earthquake, in \cite{AngIanSw}, I study the sourcing/procurement decisions of a firm that cannot directly contract with its distant and potentially unreliable (i.e., Tier~2) suppliers, but can influence the sourcing strategies of its immediate (i.e., Tier~1) suppliers through its offered contracts. I find that the sourcing strategy not only critically depends on the supply chain structure in Tier~2, but that the firm and its Tier~1 suppliers may have different preferences over how this structure should look: the firm would always prefer less overlap, helping it mitigate disruption risks, but its Tier~1 suppliers would prefer sourcing from common (and even inefficient) Tier~2 suppliers in equilibrium. This provides motivation for the ``diamond-shaped'' supply chains that Toyota discovered in its internal investigation, as well as its initiatives towards uncovering the structure of its deeper supply tiers, in spite of the resistence from Tier~1 suppliers in revealing such information (which also motivates our subsequent work in \cite{BidIanSwi}). 

%Motivated by a discussion with a senior procurement executive at Toyota in the aftermath of the T$\bar{\textup{o}}$hoku earthquake, in \cite{AngIanSw}, I study the sourcing strategies of a firm that cannot directly contract with its distant and potentially unreliable suppliers, but can influence the sourcing strategies of its immediate suppliers through contracts. I find that not only does the strategy critically depend on the supply base structure, but that the firm and its immediate suppliers may have different preferences over this structure, with the firm favoring less and the suppliers favoring more overlap (even over inefficient suppliers)---a severe moral hazard problem that cannot be mitigated through side payments, but can be resolved through contractual penalties. This motivates the ``diamond-shaped'' supply chains that Toyota discovered in its internal investigation, and also its initiatives towards uncovering the structure of deeper supply tiers (the resistance from Toyota's immediate suppliers in revealing information also motivates our current work in \cite{BidIanSwi}). 

%Some of my work in this area includes \cite{AngIanSw}---where I examine the sourcing strategies of a firm that cannot directly contract with its distant and potentially unreliable suppliers, but can influence the sourcing strategies of its immediate suppliers through contracts, as well as \cite{ZegIanLee}---where I study how firms sourcing from many small suppliers (as is typical in agricultural supply chains) can incentivize the adoption of better/sustainable management practices and create shared value through choices of sourcing channels and payment functions. This work showcases the importance of certain operational and structural details in the contract design (e.g., the structure of deeper tiers in a supply chain \cite{AngIanSw}, their degree of segmentation or their efficiency \cite{ZegIanLee}, etc.), and also highlights that achieving certain desirable improvements in the efficiency of relationships often requires combining more substantial changes  

%In complex supply chains, the benefits and costs of technological innovations do not always accrue equitably to all parties; their adoption may thus critically depend on sourcing relationships and incentives. In a setting with uncertain and endogenous process yield, we study the potential of two features—contract design and sourcing channel—to create mutual benefit in decentralized value chains, where suppliers bear the costs of new technologies while benefits accrue primarily to buyers. Our focus is on agricultural value chains, where parties may transact through a channel that blends farmers’ produce (‘commodity-based channel’) or that allows a one-on-one interaction between farmer and processor (‘direct-sourcing channel’). Our study provides insights to companies seeking to incorporate responsible sourcing strategies while also creating economic value—a concept called ‘creating shared value.’ We identify that the technology’s ‘cost elasticity’ drives whether switching sourcing channel, changing contract structure, or adopting an integrated change is necessary to create shared value. This highlights that value chain innovations need to be properly designed— and sometimes combined—to achieve sustainable implementation. We also find that certain simple contracts with a linear or bonus structure are optimal, while other intuitive contracts could be detrimental. Using a dataset of farms in Patagonia, Argentina, we estimate that the proposed mechanism could increase average supply chain profit by 6.9\% while realizing positive environmental benefits.

%This underscores the fact that simplicity and robustness often go hand in hand: simple policies---such as those calculated under limited information---tend to be more robust than highly tailored ones---that rely on substantially more information. This concept, which is akin to the well-known concept of over-fitting is statics, 

%\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.}

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.

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
\begin{thebibliography}{99}
\small

\bibitem{ZegIanLee} J. de Zegher, D. A. Iancu, and H. Lee, ``Designing Contracts and Sourcing Channels to Create Shared Value,'' {\it Forthcoming in} \textbf{Manufacturing \& Service Operations Management}.

\bibitem{NegBimBraIan} D. Negoescu, K. Bimpikis, M. Brandeau, D. A. Iancu, ``Dynamic Learning of Patient Response Rates: An Application to Treating Chronic Diseases,'' {\it Forthcoming in} \textbf{Management Science}.

\bibitem{AngIanSw} E. Ang, D. A. Iancu, and R. Swinney, ``Disruption Risk and Optimal Sourcing in Multi-tier Supply Networks,'' {\it Forthcoming in} \textbf{Management Science}.
\vspace{-5pt}

\bibitem{IanTrichTsou} D. A. Iancu, N. Trichakis, and G. Tsoukalas, ``Is Operating Flexibility Harmful Under Debt?,'' {\it Forthcoming in} \textbf{Management Science}.

\bibitem{DelIan} E. Delage and D. A. Iancu, ``Robust Multi-stage Decision Making,'' \textbf{TutORials in Operations Research}, pp. 20-46, 2015.

\bibitem{IanTrich} ``Fairness and Efficiency in Multiportfolio Optimization,'' D. A. Iancu and N. Trichakis, \emph{Forthcoming in \textbf{Operations Research}}.

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

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

\bibitem{IanShaSvi} ``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}.

%\bibitem ``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}.

\bibitem{BerIanParIEEE} ``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}.

\bibitem{BerIanParMOR} ``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}.

\bibitem{ChuIanTri} S-Y. Chun, D. A. Iancu, and N. Trichakis, ``Setting the Value of Loyalty Points,'' {\it submitted for publication}.

\bibitem{BesIanTri} O. Besbes, D. A. Iancu, and N. Trichakis, ``Dynamic Pricing under Revenue Targets: Incentives and Spiraling,'' {\it Under second round of review in} \textbf{Management Science}.

\bibitem{ZegIanLeePlam} J. de Zegher, D. A. Iancu, H. Lee, and E. Plambeck, ``Operational Strategies to Achieve No-Deforestation Commitments in Palm Procurement.''

\bibitem{IanUic} ``Planning for Disaster Recovery and Relief in the Philippines,'' D. A. Iancu and J. Uichanco.

\bibitem{IanTriYoo} Ian D. A. Iancu, N. Trichakis, and D.-Y. Yoon, ``Optimal Monitoring Policies Under Limited Information,'' {\it working paper}.

\bibitem{IanWei} D. A. Iancu and Y. Wei, ``On the Role of Discrete Convexity in Robust Optimization.''

\bibitem{BidIanSwi} H. Bidkhori, D. A. Iancu, and R. Swinney, ``Managing Disruption Risk in Supply Chains with Limited Visibility.''

\bibitem{IanTur} ``Optimal Growth Policies in Online Advertising,'' D. A. Iancu, and J. Turner.
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