Affiliated Faculty
 

Easton Center Instructors

 

Todd Holloway

Senior Director of Data and Personalization Science, Nike

Machine Learning & Artificial Intelligence

Sunil Rajaraman

Entrepreneur In Residence, Foundation Capital

Product Management

Michael Montgomery

President, Montgomery & Co, LLC

Technological Innovations in Media and Entertainment

Jennifer McCaney

Executive Director, UCLA Biodesign

Healthcare Technology Management

John Blevins

Managing Partner, Navigation Pointe

Cloud Computing & Big Data

Nathan Wilson

President & CEO, Open Source Medical Software Corp

Raising Capital for Early-stage Tech Ventures

Terry Kramer

Faculty Director, Easton Technology Management Center

Tech Management;
Evolution & Innovation in Global Mobile Industry

 

Ph.D. Research Grants

 

Yi-Chun Chen

DOTM Ph.D. Student

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Decision Forest: A Nonparametric Approach to Modeling Irrational Choice.

Customer behavior is often assumed to follow weak rationality, which implies that adding a product to an assortment will not increase the choice probability of another product in that assortment. However, an increasing amount of research has revealed that customers are not necessarily rational when making decisions. In this paper, we study a new nonparametric choice model that relaxes this assumption and can model a wider range of customer behavior, such as decoy effects between products. In this model, each customer type is associated with a binary decision tree, which represents a decision process for making a purchase based on checking for the existence of specific products in the assortment. Together with a probability distribution over customer types, we show that the resulting model -- a decision forest -- is able to represent it any customer choice model, including models that are inconsistent with weak rationality. We theoretically characterize the depth of the forest needed to fit a data set of historical assortments and prove that asymptotically, a forest whose depth scales logarithmically in the number of assortments is sufficient to fit most data sets. We also propose an efficient algorithm for estimating such models from data, based on combining randomization and optimization. Using synthetic data and real transaction data exhibiting non-rational behavior, we show that the model outperforms the multinomial logit and ranking-based models in out-of-sample predictive ability.

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Faculty Advisor: Velibor Misic

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Sema Nur Kaynar Keles

DOTM Ph.D. Student

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Learning Hidden Action Principal Agent Models

Description of research: "We propose a method for estimating principal agent models from historical data on contracts and associated outcomes. We show that the estimator is statistically consistent and can be formulated as a mixed integer linear program. We then introduce a solution technique based on hypothesis testing and column generation, and show that it produces an asymptotically optimal solution."

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Faculty Advisor: Auyon Siddiq

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Ali Fattahi

DOTM Ph.D. Student

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In his summer project Ali Fattahi, mentored by Professor Reza Ahmadi, studied 1) major challenge in the current forecasting approach that ignores the relationships between options and, as a result, the forecasts are frequently incorrect, which results in excess inventories, shortages, and customer dissatisfaction, 2) a new variation of the Non-Negative Least-Squares problem, and 3) determinination of how many units of each part is required over the planning horizon, known as the parts capacity-planning problem 

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Araz Khodabakhshian

DOTM Ph.D. Student

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TBA

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Pankaj Jindal

DOTM Ph.D. Student

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Analysis of stock market reaction to data breaches (tentative title)

Brief description of research: The scope of this research project is still evolving. The primary focus of this research work is to understand the impact of data breaches on stock returns of the firms that have faced data breaches. To do this, we conduct an empirical study by utilizing data consisting of firms that suffered data breaches from 2005 till 2018, along with the number of records exposed in each breach and the nature of the breach. Further, we match and combine this data with financial and accounting information on the breached firms, from CRSP and COMPUSTAT databases. Using this data, we first conduct an event-study to determine the short-term abnormal returns due to a data breach. Second, we try to analyze how the abnormal returns are moderated by certain operational levers such as employee productivity and technological innovation in a firm. Finally, we are planning to associate a causal nature to data breaches on abnormal returns by finding matching firms that did not face data breaches but are otherwise similar to the firms that faced data breaches. Overall, we hope to be able to estimate the magnitude of these impacts and subsequently make some managerial recommendations on how to best avoid and react to such incidents.

Faculty Advisor(s): Prof. Charles Corbett

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Bobby Nyotta

DOTM Ph.D. Student

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Free Rides in Dockless, Electric Vehicle Sharing Systems

Brief description of research: We study free-ride policies as a mechanism to incentivize customer behavior in an electric vehicle sharing system (EVSS) with ``dockless" or ``free-floating" parking. A balanced system has a fleet that is adequately charged and evenly dispersed throughout the city. If left to unfold naturally, the system would fall out of balance and revenue and customer experience might suffer. Most sharing systems use manual repositioning to achieve this balance, but we consider pricing incentives as an alternative method. We develop an infinite-horizon dynamic program to analyze free-ride policies. We focus on an EVSS that offers free rides to customers if they return vehicles to charging stations. We build on this initial formulation to construct a mixed-integer program that determines when to offer based on an intuitive, battery-threshold rule. We also extend the model to accommodate more general discount-based policies. In a discrete-event simulation model using real operations data from an EVSS, we compare the performance of this simple policy against a Fine-Based policy. We show that more battery for a vehicle is not always lucrative and that the optimal free-ride policy does not have an intuitive structure and may be difficult to implement. In contrast, the battery-threshold policy based on a single-vehicle setting is simple to understand and implement. We use our simulation to test more flexible ride discounts and multi-vehicle policies. Our analysis shows that the effectiveness of ride discounts can vary considerably. We discover Fine-Based policies can generate slightly more revenue, but the free-ride policies offer a superior customer experience. On other key performance indicators, the benefits of fine-based and discount-based policies depend on the network parameters.

Faculty Advisor(s): Fernanda Bravo and Keith Chen

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Taylor Corcoran

DOTM Ph.D. Student

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TBA

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Anna Saez De Tejada Cuenca

DOTM Ph.D. Student

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In her summer project, Anna Saez De Tejada Cuenca, studied unauthorized subcontracting in apparel supply chains. This project has lead to a research paper, with the title "Can Brands Claim Ignorance? Unauthorized Subcontracting in Apparel Supply Chains. This paper is currently under major revision at Management Science. Her research advisor is Felipe Caro. In this project she also collaborated with Leonard Lane from UC Irvine Paul Merage School of Business.

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Easton Faculty

 

Learn from UCLA Anderson faculty, who rank among the best in the world and are highly regarded for their groundbreaking work.

Terry Kramer

Faculty Director
Adjunct Professor of Decisions, Operations and Technology Management

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Fernanda Bravo

Assistant Professor of Decisions, Operations, and Technology Management

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