Operations & Supply Chain Management

Many service systems are staffed by workers who work in shifts. In this work, we study the dynamic assignment of servers to different areas of a service system at the beginning of discrete time-intervals, i.e., shifts. The ability to reassign servers at discrete intervals, rather than continuously, introduces a partial flexibility that provides an opportunity for reducing the expected waiting time of customers.

Patients whose transfer to the Intensive Care Unit (ICU) is unplanned are prone to higher mortality rates and longer length-of-stay than those who were admitted directly to the ICU. Recent advances in machine learning to predict patient deterioration have introduced the possibility of proactive transfer from the ward to the ICU. In this work, we study the problem of finding robust patient transfer policies which account for uncertainty in statistical estimates due to data limitations when optimizing to improve overall patient care.

We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on observations at a single historical price? The decision-maker optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the revenue it can garner compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to broad non-parametric set. In particular, our framework applies to the widely used regular and monotone non-decreasing hazard rate (mhr) classes of distributions.

Deciding between the use of market orders and limit orders is an important question in practical optimal trading problems. A key ingredient in making this decision is understanding the uncertainty of the execution of a limit order, that is, the fill probability or the probability that an order will be executed within a certain time horizon. Equivalently, one can estimate the distribution of the time-to-fill. We propose a data-driven approach based on a recurrent neural network to estimate the distribution of time-to-fill for a limit order conditional on the current market conditions.

We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle.

Along with its interrelated companion volume, The Content, Impact, and Regulation of Streaming Video, this book covers the next generation of TV—streaming online video, with details about its present and a broad perspective on the future. It reviews the new technical elements that are emerging, both in hardware and software, their long-term trend, and the implications. It discusses the emerging ‘media cloud’ of video and infrastructure platforms, and the organizational form of such TV.

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions including e-mails, display ads and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, there is no formal justification for them and many of these fail even in simple canonical settings. The main contribution in this work is to develop an axiomatic framework for attribution in online advertising.

Auctions are widely used in practice. While also extensively studied in the literature, most of the developments rely on the significant common prior assumption. We study the design of optimal prior-independent selling mechanisms: buyers do not have any information about their competitors and the seller does not know the distribution of values, but only a general class it belongs to.