Seminar: An Optimal Stopping Approach to Portfolio Risk Measurement

Portfolio risk measurement under the nested setting is a challenging computational problem, and has received increasing attention in recent years. This nested setting often requires mark-to-market reevaluation of the portfolio for a large number of possible scenarios of risk factors up to a future time horizon. When closed-form formula is not available, reevaluation may require intensive simulations that are time consuming. This paper aims to develop a new simulation method that is computationally efficient for measurement of conditional Value-at-Risk (CVaR) for the portfolio.

Seminar: Ranking and Selection with Covariates

We consider a new ranking and selection problem in which the performance of each alternative depends on some observable random covariates. The best alternative is thus not constant but depends on the values of the covariates. Assuming a linear model that relates the mean performance of an alternative and the covariates, we design selection procedures producing policies that represent the best alternative as a function in the covariates.

Seminar: Dynamic Pricing with Varying Cost

We consider a dynamic pricing problem where the firm tries to maximize the profit upon selling a product over the course of T periods. We do not assume decision maker’s foreknowledge on the demand. Traditionally, the cost is fixed and the problem may be formulated as a multi armed bandit problem, which is known to have an O(log T) lower bound on the expected regret. In this paper, we consider a setting where the cost may change over time and the optimal price is thus a function of the cost. We develop an upper confidence bound like (UCB-Like) algorithm to solve the problem.

Seminar: Dynamic Pricing with Varying Cost

We consider a dynamic pricing problem where the firm tries to maximize the profit upon selling a product over the course of T periods. We do not assume decision maker’s foreknowledge on the demand. Traditionally, the cost is fixed and the problem may be formulated as a multi armed bandit problem, which is known to have an O(log T) lower bound on the expected regret. In this paper, we consider a setting where the cost may change over time and the optimal price is thus a function of the cost. We develop an upper confidence bound like (UCB-Like) algorithm to solve the problem.

Seminar: To Ration Or Not To Ration? Selling To Strategic Customers Under Shortage Effect

In this paper, we study the dynamic pricing and availability decisions of a firm that repeatedly introduces new generations of a product over time. Customers are strategic and are affected by two types of scarcity effects----direct scarcity effect and relative scarcity effect. Firm-induced scarcity is never optimal in the absence of scarcity effects but it can be optimal when customers are affected by scarcity effects. We study the long-run optimal policy of the firm and characterize when firm-induced scarcity is optimal.

Seminar: Characterizing the dynamics underlying global spread of epidemics

Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g. for pandemic influenza, SARS and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe.

Seminar: Characterizing the dynamics underlying global spread of epidemics

Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g. for pandemic influenza, SARS and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe.

Seminar: Appointment Scheduling with a Waiting Time Target

This work is motivated by the appointment booking of specialist services at the ambulatory care center (ACC) in a major public hospital in Hong Kong. We study scheduling policies in a healthcare system in which patients' waiting times for medical consultations are governed by the Hong Kong Hospital Authority. Specifically, the ACC accepts only advanced bookings, and patients should receive consultation within a stipulated target waiting time. We study two forms of scheduling: allocation scheduling and advance scheduling.

Seminar: Managing Portfolio of Elective Surgical Procedures: A Multidimensional Inverse Newsvendor Problem

We consider the problem of allocating daily hospital service capacity among several types of elective surgical procedures. Our focus is on the interaction between two major constraining hospital resources: operating room and recovery bed capacity. In our model, each type of surgical procedure has an associated revenue, stochastic procedure duration and stochastic length-of-stay.

Seminar: Stochastic Optimization with Decisions Truncated by Random Variables

We study stochastic optimization problems with decisions truncated by random variables. The technical difficulty is that the optimization problems may not be convex programs due to the truncation. We develop a transformation technique to convert the original non-convex problems to equivalent convex ones. Our transformation allows us to prove the preservation of some desired structural properties, such as convexity, submodularity, and L-natural-convexity, under optimization operations, that are critical for identifying the structures of optimal policies and developing efficient algorithms.

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