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George Leland Bach Teaching Award for Excellence in the MBA Classroom,  Tepper School of Business

Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes

Vertex downgrading to minimize connectivity

Order fulfillment under pick failure in omnichannel ship-from-store programs

A new integer programming formulation of the graphical traveling salesman problem

Dynamic pricing with monotonicity constraint under unknown parametric demand model

Iterative Methods in Combinatorial Optimization (Cambridge Texts in Applied Mathematics, Series Number 46)

Customer-Centric Marketing: A Pragmatic Framework (Mit Press)

Dr. R. Ravi is the Andris A. Zoltners Professor of Business, and Professor of Operations Research and Computer Science at Carnegie Mellon University. Ravi received his bachelor's degree from IIT, Madras, and Master's and doctoral degrees from Brown University, all in Computer Science. Ravi has been at the Tepper School of Business since 1995 where he served as the Associate Dean for Intellectual Strategy from 2005-2008, and Chair of the Future Educational Delivery Committee that launched the online hybrid Tepper MBA in 2013. Ravi's main research interests are in algorithms for combinatorial optimization, and their applications in the intersection of business and technology. Ravi is interested in networks and their effects in business, a subject on which he introduced a new MBA class. He is also interested in customer-centric marketing and how to accomplish this using optimization methods on large data sets, on which he co-developed another new MBA class and co-wrote a book. On the academic side, Ravi's research has been supported by the U.S. National Science Foundation Office of Naval Research and Air Force Office of Scientific Research. He has supervised over a twenty doctoral students and developed over half a dozen new graduate classes. He served as area editor for the INFORMS flagship journal Operations Research in charge of the discrete optimization area from 2012 to 2017. He has held the title of Rohet Tolani Distinguished Professor between 2014 and 2018, and Carnegie Bosch Professor between 2006 and 2013. He was elected a fellow of the INFORMS in 2017. Ravi is currently Director of Analytics Strategy at the school, and an Amazon Scholar.

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Tepper Faculty Member Tapped to Join Amazon Scholars Program

Display Advertising Switched to First-price Auctions After Adoption of Header Bidding, New Study Finds

Professor of Business, and Professor of Operations Research and Computer Science

What if AI transformed your job so you had four-day weekends or never even had to work again?



The rapid advancement of artificial intelligence is poised to dramatically transform the landscape of work. As routine tasks are automated by AI, new efficiencies are created, freeing up time for workers — but what should they do with that time? Use it for higher-level thinking and decision-making? Reduce their work hours and have a four-day weekend?  Add new skills and responsibilities to their job? Will jobs be lost, replaced or displaced?



Our experts discuss the impact on wages, and the industries and jobs that are at risk or poised to thrive in an AI-powered workplace. They also address the need for workers to adapt and upskill or reskill themselves, the risks and ethical issues associated with AI in the workplace, and the role of regulation and policy.



Joining us are three experts on AI in business and work. R. Ravi is the Andres Zoltners Professor of Business, and Professor of Operations Research and Computer Science at Carnegie Mellon University. Laurence Ales is a Professor of Economics at the Tepper School of Business at Carnegie Mellon University. And Calum Chace is a global speaker, advisor, and the bestselling author of “Surviving AI” and “The Economic Singularity.”

Clocking In: AI Transforms the Future of Work

R. Ravi, associate dean of intellectual strategy and Carnegie Bosch Professor of Operations Research and Computer Science, anaylzes the broad reach of Google's Open Handset Alliance. Google's announcement included 34 partners from around the world covering providers, manufacturers and applications developers. The search giant said it hoped the alliance would be the foundation for a new kind of mobile communications experience, with applications and capabilities unimaginable today. Ravi discusses the strategy and implications of Google's effort.

Faculty Commentary: Google Open Handset Alliance

In the stochastic knapsack problem, we are given a knapsack with size B, and a set of jobs whose sizes and rewards are drawn from a known probability distribution. However, the only way to know the actual size and reward is to schedule the job---when it completes, we get to know these values. How should we schedule jobs to maximize the expected total reward? We know constant-factor approximations for this problem when we assume that rewards and sizes are independent random variables, and that we cannot prematurely cancel jobs after we schedule them. What can we say when either or both of these assumptions are dropped?    Not only is the stochastic knapsack problem of interest in its own right, but techniques developed for it are applicable to other stochastic packing problems. Indeed, ideas for this problem have been useful for budgeted learning problems, where one is given several arms which evolve in a specified stochastic fashion with each pull, and the goal is to pull the arms a total of B times to maximize the reward obtained. Much recent work on this problem focus on the case when the evolution of the arms follows a martingale, i.e., when the expected reward from the future is the same as the reward at the current state. However, what can we say when the rewards do not form a martingale?    We give constant-factor approximation algorithms for the stochastic knapsack problem with correlations and cancelations, and also for some budgeted learning problems where the martingale condition is not satisfied, using similar ideas. Indeed, we can show that previously proposed linear programming relaxations for these problems have large integrality gaps. We propose new time-indexed LP relaxations; using a decomposition and ``shifting'' approach, we convert these fractional solutions to distributions over strategies, and then use the LP values and the time ordering information from these strategies to devise a randomized scheduling algorithm. We hope our LP formulation and decomposition methods may provide a new way to address other correlated bandit problems with more general contexts.    This is joint work with Anupam Gupta, Ravishankar Krishnaswamy and Marco Molinaro, all from CMU. The paper is available at  http://arxiv.org/abs/1102.3749

Approximation Algorithms for Correlated Knapsacks and Non-Martingale Bandits

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