Chaithanya Bandi
Chaithanya Bandi

Assistant Professor of Operations

Print Overview

Professor Chaithanya Bandi joined the department of Managerial Economics and Decision Sciences at the Kellogg School of Management in 2013. He received a Ph.D in Operations Research from MIT in 2013, and a Bachelors of Technology in Computer Science and Engineering from IIT Madras in 2008. He has worked in technology and Financial services companies such as Yahoo, Bell Labs, Lehman Brothers, and Investment Technology Group. Chaithanya is broadly interested in the problems of decision making under uncertainty, incomplete information and risk with applications to operations management. In particular, he has focussed on developing Robust Optimization based models to formulate key problems in applications such as queueing control, risk optimization, mechanism design, and online algorithms.

Print Vita
Ph.D., 2013, Operations Research, Sloan School of Management, Massachusetts Institute of Technology
B. Tech, 2008, Computer Science and Engineering, Department of Computer Science and Engineering, Indian Institute of Technology - Chennai (Madras)

Academic Positions
Teaching Faculty, Analytical Decision Modeling, Kellogg School of Management, Northwestern University, 2013-present
Teaching Assistant, Sloan School of Management, Massachusetts Institute of Technology, 2008-present

Other Professional Experience
Research Intern, Investment Technology Group, 2011-2011
Intern, Yahoo!, 2009-2009
Intern, Lehman Brothers, 2007-2007

Honors and Awards
ORC Best Paper Award
Finalist George Nicholon Best Paper
Presidential Fellowship to pursue doctoral studies, MIT
Best Presentation Award in Financial Services Section, INFORMS Annual Meeting

Print Research
Research Interests
Robust Optimization, Applied Probability, and Optimization methods with applications to Control and Analysis of large scale multi-class queueing networks, Mechanism Design, and Finance-OM interface.

Bandi, Chaithanya and Dimitris Bertsimas. 2014. Optimal Design for Multi-Item Auctions: A Robust Optimization Approach. Mathematics of Operations Research.
Bandi, Chaithanya and Dimitris Bertsimas. 2014. Robust Option Pricing. European Journal of Operational Research. 239(3): 842-853.
Bandi, Chaithanya and Dimitris Bertsimas. 2012. Tractable stochastic analysis in high dimensions via robust optimization. Mathematical Programming. 134(1): 23-70.
Bandi, Chaithanya, Dimitris Bertsimas and Nataly Youssef. 2015. Robust Queueing Theory. Operations Research.
Working Papers
Bandi, Chaithanya, Phebe Vayanos and Nikolaos Trichakis. 2016. Robust Wait Time Estimation in Resource Allocation Systems with an Application to Kidney Allocation.
Bandi, Chaithanya and Dimitris Bertsimas. 2014. Optimal Spending Patterns for a foundation, endowment or an individual - A Multi-Period Robust Optimization Approach.
Bandi, Chaithanya and Rajarshi Ghosh. 2015. Robust All-Pay Auctions with Applications to Crowdsourcing tournaments.
Bandi, Chaithanya and Dimitris Bertsimas. 2015. Optimal Variable Length Coding via Robust Optimization.
Bandi, Chaithanya and Dimitris Bertsimas. 2015. Network Information Theory via Robust Optimization.
Bandi, Chaithanya, Dimitris Bertsimas and Nataly Youssef. 2014. Robust Transient Queueing Theory.
Bandi, Chaithanya, Dimitris Bertsimas, Erik Brynjolfsson, Shachar Reichman and John Silberholz. 2014. Dashboard for Academia The Analytics of Academic Impact.
Bandi, Chaithanya, Dinesh Garg, Sachin Garg and KrishnaPalSingh Rathore. 2011. "Dynamic Pricing Models for Online Advertising." United States Patent US 2011/0166927 A1, filed 6/1/2011.

Print Teaching
Full-Time / Evening & Weekend MBA
Analytical Decision Modeling (OPNS-450-0)
This course focuses on structuring, analyzing and solving managerial decision problems on Excel spreadsheets. We address problems of resource allocation (how to use available resources optimally), risk analysis (how to simulate the effects of uncertainty in problem parameters), decision analysis (how to analyze sequential decisions involving uncertainty), data analysis (how to synthesize the available data into useful information) and forecasting (how to extrapolate past observations into the future). In each area, we pose specific problems from operations, finance and marketing, structure them on Excel spreadsheets, and analyze and solve them using the available Excel commands, tools and add-ins. The course involves a hands-on, in-class learning experience in modeling and analyzing a variety of business decision problems on a common spreadsheet platform. It should, therefore, enhance one's problem-solving capabilities as well as spreadsheet skills. A good working knowledge of Microsoft Excel is required.

Analytical Decision Modeling (OPNSM-450-0)

Queueing Networks: Performance Analysis (OPNS-522-0)
The course is to introduce the basic methods that are often used for analyzing performance in queueing network models. The students will learn how to characterize various queueing performance measures and apply to managerial decision making. Students should have familiarity with Markov process.

Executive MBA
Decision Modeling and Optimization in Excel (OPNSX-450-0)
Analytical Decision Modeling focuses on structuring, analyzing and solving decision-problems spreadsheets. Problems involving optimal resource allocation and risk analysis are studied through applications in operations, finance and marketing. Some decision analysis, data analysis and forecasting is also covered. The course assumes working knowledge of Microsoft Excel.