New Directions in
Management Science and Engineering

Content Referral on the Internet

Date: Thursday, October 11
Speaker: Assaf Zeevi, Henry Kravis Professor at the Graduate School of Business, Columbia University

Search engines have been a transformative force, shaping the way we seek content and navigate the world wide web. They have also provided one of the most successful and robust revenue models on the Internet in the form of sponsored search. Recent trends in online user behavior exhibit interesting new phenomena, one of which is the increasing prevalence of "wandering'' type patterns; seeking content, yet in a less targeted manner. What are these users looking for? What current content is potentially attractive? And finally, how does one match the two in real time? This talk will explore some of these questions, with the intent of illustrating the use of data to inform modeling, and some of the analytical challenges that ensue.

A General Framework for Systemic Risk

Date: Tuesday, October 23
Speaker: Ciamac Moallemi, Associate Professor in the Decision, Risk, & Operations Division of the Graduate School of Business at Columbia University.

Systemic risk [SR] refers to the risk of collapse of an entire complex system, as a result of the actions taken by the individual component entities or agents that comprise the system. SR is an issue of great concern in modern financial markets as well as, more broadly, in the management of complex business and engineering systems. We propose an axiomatic framework for the measurement and management of SR based on the simultaneous analysis of outcomes across agents in the system and over scenarios of nature. Our framework defines a broad class of SR measures that accommodate a rich set of regulatory preferences. This general class of SR measures captures many specific measures of SR that have recently been proposed as special cases, and highlights their implicit assumptions. Moreover, the SR measures that satisfy our conditions yield decentralized decompositions, i.e., the SR can be decomposed into risk due to individual agents. Furthermore, one can associate a shadow price for SR to each agent that correctly accounts for the externalities of the agent’s individual decision-making on the entire system. This is joint work with Chen Chen and Garud Iyengar

The Genomics Revolution

Date: Thursday, November 15
Speaker: David Heckerman, Senior Director, eScience Research Group, Microsoft Research

A little over a decade ago, the first human genome was sequenced at a cost of about one-hundred million dollars. Today, it costs well under ten thousand dollars. In fact, the cost is dropping much more quickly than the rate of Moore’s law. As a result, genomics data is becoming widely available and widely used. I will discuss elements of this genomics revolution relevant to Management Science and Engineering, including probabilistic modeling for the identification of genetic causes of disease. I will also touch on legal and ethical issues surrounding this revolution.

Save the Date!

Date: Thursday, December 6
Speaker: Rajshree Agarwal, Chaired Professor in Entrepreneurship and Strategy at University of Maryland

Sparse Machine Learning for Large Text Corpora

Date: Tuesday, May 15
Speaker: Laurent El Ghaoui, Professor of EECS and IEOR at UC Berkeley

Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. These methods can be extremely useful for understanding large collections of text documents, without requiring user expertise in machine learning. Our approach relies on three main ingredients: (a) multi-document text summarization and (b) comparative summarization of two corpora, both using sparse regression or classification; (c) sparse principal components and sparse graphical models for unsupervised analysis and visualization of large text corpora. In this talk I will describe some theoretical and algorithmic challenges related to these approaches, as well as experiments pertaining to real-life text databases, from scientific literature (PubMed), news and internal industry reports (commercial pilots reports).

Adventures in Policy Modeling!

Date: Tuesday, April 17
Speaker: Ed Kaplan, William N. and Marie A. Beach Professor of Mgmt Sciences, ChE & EnvE Engineering & Public Health, Yale University

Policy Modeling refers to the application of operations research, statistics, and other quantitative methods to model policy problems. Recognizing that analyses of all sorts often exhibit diminishing returns in insight to effort, the hope is to capture key features of various policy issues with relatively simple “first-strike” models. Problem selection and formulation thus compete with the mathematics of solution methods in determining successful applications: where do good problems come from? how can analysts tell if a particular issue is worth pursuing? In addressing these questions, I will review some personal adventures in policy modeling selected from public housing, HIV/AIDS prevention, bioterror preparedness, suicide bombings and counterterrorism, in vitro fertilization, predicting presidential elections, and March Madness.

Engineering Happiness

Date: Thursday, April 12
Speaker: Rakesh Sarin, Paine Professor of Management, UCLA Anderson School

We develop and apply a unique and novel application of analytics and Decision Analysis to the study of happiness. Our results should be useful to individuals seeking to become happier and to organizations that wish to improve customer and employee satisfaction and productivity. Our model begins with the fundamental equation: HAPPINESS = REALITY – EXPECTATIONS. Following this, we propose a set of six laws that modify the fundamental equation, making it more precise and applicable to a wide range of situations and choices.