Dean Geoffrey Garrett
Diversity, Equity and Inclusion
Teaching + Innovation
Experiential Learning Center
Open Expression Statement
BS Business Administration (BUAD)
BS Accounting (ACCT)
World Bachelor in Business (WBB)
BS Business of Cinematic Arts (BCA)
BS Artificial Intelligence for Business (BUAI)
Full-Time MBA (FTMBA)
Executive MBA (EMBA)
Part-Time MBA (MBA.PM)
International MBA (IBEAR)
Online MBA (OMBA)
MS Business Administration (MSBUSAD)
MS Business Analytics (MSBA)
MS Entrepreneurship + Innovation (MSEI)
MS Finance (MSF)
MS Global Supply Chain Management (MSGSCM)
MS Marketing (MSMKT)
MS Social Entrepreneurship (MSSE)
Master of Business for Veterans (MBV)
Master of Management Studies (MMS)
Master of Accounting (MAcc)
Master of Business Taxation (MBT)
Master of Business Taxation for Working Professionals (MBT.WP)
Data Sciences + Operations
Management + Organization
GC in Business Analytics
GC in Financial Analysis + Valuation
GC in Management Studies
GC in Marketing
GC in Optimization + Supply Chain Management
GC in Strategy + Management Consulting
GC in Sustainability + Business
GC in Technology Commercialization
GC in Library and Information Management – Online
Business Communication (BUCO)
Data Sciences and Operations (DSO)
Finance + Business Economics (FBE)
Leventhal School of Accounting (ACCT)
Lloyd Greif Center for Entrepreneurial Studies (BAEP)
Management and Organization (MOR)
Institutes + Centers
Randall R. Kendrick Global Supply Chain Institute
Peter Arkley Institute for Risk Management
VanEck Digital Assets Initiative
Institute for Outlier Research in Business
Lloyd Greif Center for Entrepreneurial Studies
USC Marshall Venture Fund
Brittingham Social Enterprise Lab
Neely Center for Ethical Leadership and Decision Making
Center for Effective Organizations
Center for Global Innovation
Center for Investment Studies
Initiative on Digital Competition
Giving + Support
Alumni Engagement + Resources
Vishal Gupta is an Associate Professor of Data Sciences and Operations at the USC Marshall School of Business. Because of his research interests and expertise, he also holds a courtesy appointment in USC Viterbi’s School of Engineering in Industrial and Systems Engineering and is an affiliate faculty with USC’s Center for AI and Society.
Before joining USC, Vishal Gupta completed his B.A. in Mathematics and Philosophy at Yale University, graduating Magna Cum Laude with honors, and completed Part III of the Mathematics Tripos at the University of Cambridge with distinction. He then spent four years working as a “quant” in finance at Barclays Capital, focusing on commodities modeling, derivatives pricing, and risk management.
Eventually, Vishal realized how much he missed working towards a larger mission of impact, and left the private sector to complete his Ph.D. in Operations Research at MIT in 2014.
Vishal’s research focuses on data-driven decision-making and optimization, particularly in settings where data are scarce. Such settings are common in applications that rely on personalization (like precision healthcare) and real-time decision-making (like risk management). Consequently, his research spans a wide variety of areas including revenue management, education, healthcare, and artificial intelligence.
Vishal has received a number of recognitions for his work, including the Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research, the Pierskalla Best Paper Prize, the Jagdish Sheth Impact of Research on Practice Award.
Areas of Expertise
Centers + Institutes
INSIGHT + ANALYSIS
Article: How Companies Can Avoid Ethical Pitfalls When Building AI Products
Vishal Gupta, Associate Professor of Data Science & Operations, presents a roadmap for companies considering the inevitable future of integrating AI in all facets of their model for Venture Beat.
Article: We Helped Greece Build an AI System to Make Covid-19 Testing More Efficient. Here's What We Learned.
Professors Kimon Drakopoulos and Vishal Gupta reflect on some of the lessons learned from their pioneering work deploying the first, nation-wide, artificial intelligence system for targeted COVID-19 screening for Entrepreneur.
Featured: Project Eva on PhocusWire
"How An Email To The Prime Minister And Algorithms Helped Greece Bring Tourists Back" offers a brief history of Project Eva on PhocusWire.
Featured: Project Eva in USC Pressroom
The AI-based project, nicknamed “Eva,” that uses data to support decision-making by the Greek government as it reopens the tourist industry vital to its economy amid the worldwide COVID-19 pandemic is showcased in USC Pressroom.
NEWS + EVENTS
Vishal Gupta Awarded INFORMS Wagner Prize for System to Curb COVID Spread in Greece
The prize was awarded to Gupta and his collaborators for the “Eva” system, which was deployed across all Greek borders to limit the influx of asymptomatic travelers.
USC Marshall announced a number of awards to faculty and staff in an end-of-semester virtual ceremony.
A Data-Driven Reopening
Researchers from USC Marshall and Wharton team up with the Greek Government to safely reopen to tourism.
New USC Marshall research creates a mathematical method for choosing who gets treated when resources and data are scarce—and proves it performs better than standard practice.
RESEARCH + PUBLICATIONS
In the summer of 2020, in collaboration with the Greek government, we designed and deployed Eva – the first national scale, reinforcement learning system for targeted COVID-19 testing. In this paper, we detail the rationale for three major design/algorithmic elements: Eva’s testing supply chain, estimating COVID-19 prevalence, and test allocation. Specifically, we describe the design of Eva’s supply chain to collect and process thousands of biological samples per day with special emphasis on capacity procurement. Then, we propose a novel, empirical Bayes estimation strategy to estimate COVID-19 prevalence among different passenger types with limited data and showcase how these estimates were instrumental for a variety of downstream decision-making. Finally, we propose a novel, multi-armed bandit algorithm that dynamically allocates tests to arriving passengers in a non-stationary environment with delayed feedback and batched decisions. All of our design and algorithmic choices emphasize the need for transparent reasoning to enable human-in-the-loop analytics. Such transparency was crucial to building trust and buy-in among policymakers and public health experts in a period of global crisis.
This chapter introduces the small-data, large-scale optimization regime, an asymptotic setting that arguably better describes certain data-driven optimization applications than the more traditional large-sample regime. We highlight unique phenomena that emerge in the small-data, large-scale regime, and show how these phenomena cause certain traditional data-driven optimization algorithms like sample average approximation (SAA) to fail. We then propose a new debiasing approach that has provably good performance in this regime, highlighting a new path forward for research and development into these types of applications.
Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, i.e., strategies that predict an individual customer’s valuation for a product and then offer a customized price tailored to that customer. While the appeal of personalized pricing is clear, it may also incur large costs in the form of market research, investment in information technology and analytics expertise, and branding risks. In light of these trade-offs, our work studies the value of personalized pricing strategies over a simple single price strategy. We first provide closed-form lower and upper bounds on the ratio between the profits of an idealized personalized pricing strategy (first-degree price discrimination) and a single price strategy. Our bounds depend on simple statistics of the valuation distribution and shed light on the types of markets for which personalized pricing has little or significant potential value. Second, we consider a feature-based pricing model where customer valuations can be estimated from observed features. We show how to transform our aforementioned bounds into lower and upper bounds on the value of feature-based pricing over single pricing. Finally, we demonstrate how to obtain sharper bounds by incorporating additional information about the valuation distribution (moments or shape constraints) by solving tractable linear optimization problems.
Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all travellers to restricting entry from select nations on the basis of population-level epidemiological metrics such as cases, deaths or testing positivity rates1,2. Here we report the design and performance of a reinforcement learning system, nicknamed Eva. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources on the basis of incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers as random surveillance testing, with up to 2–4 times as many during peak travel, and 1.25–1.45 times as many asymptomatic, infected travellers as testing policies that utilize only epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies3 that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.
Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests that one can decouple these unrelated problems and solve them separately without loss of generality. We propose a novel data-pooling algorithm called Shrunken-SAA that disproves this intuition. In particular, we prove that combining data across problems can outperform decoupling, even when there is no a priori structure linking the problems and data are drawn independently. Our approach does not require strong distributional assumptions and applies to constrained, possibly nonconvex, nonsmooth optimization problems such as vehicle-routing, economic lot-sizing, or facility location. We compare and contrast our results to a similar phenomenon in statistics (Stein’s phenomenon), highlighting unique features that arise in the optimization setting that are not present in estimation. We further prove that, as the number of problems grows large, Shrunken-SAA learns if pooling can improve upon decoupling and the optimal amount to pool, even if the average amount of data per problem is fixed and bounded. Importantly, we highlight a simple intuition based on stability that highlights when and why data pooling offers a benefit, elucidating this perhaps surprising phenomenon. This intuition further suggests that data pooling offers the most benefits when there are many problems, each of which has a small amount of relevant data. Finally, we demonstrate the practical benefits of data pooling using real data from a chain of retail drug stores in the context of inventory management.