Projects

Data Science and Business Analytics Fund

The Data Science and Business Analytics Fund will support new opportunities to expand the impact of big data analytics on areas as diverse as sports and entertainment, healthcare and medicine, and network science and the media, drawing on Wharton faculty’s diverse research excellence in areas that are being transformed by the opportunities created by unprecedented volumes of digital, numerical
and text-based data.

Call for Proposals

Wharton faculty are invited to submit proposals that demonstrate the need for financial support and infrastructure to enhance faculty research, student learning opportunities and engagement with industry and alumni. Proposals are reviewed on a bi-annual basis. For the 2020-2021 academic year, proposals are due on October 1, 2020 and April 1, 2021. If interested in submitting a proposal, please contact analytics@wharton.upenn.edu.

Featured Project

Incentivized Resume Rating (IRR) – a new tool to study what employers value in hiring, and work to eliminate bias.

Projects by the Numbers

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40 project submissions
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16 projects funded
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11 departments funded
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over $500,000 invested

Funded Projects 2020

Amenity Value of Green Space

Susan Wachter, Albert Sussman Professor of Real Estate, Professor of Finance
Shane Jensen, Professor of Statistics, Department of Statistics

This project seeks to identify the neighborhood amenity value of transforming blighted and vacant lots into maintained green open space by deploying spatial techniques and integrating multiple data sources to improve our understanding of the dynamics of urban change and identify how residents value “greener” neighborhoods.

Analysis of Digital Experimentation in Industry

Kartik Hosanagar, John C. Hower Professor of Technology & Digital Business; Professor of Marketing

This project seeks to conduct original academic research to better understand how real-world firms use A/B testing software and how consumers respond to online experiments.

Better Big Data to Prevent Burnout and Improve Teams

Ken Moon, Assistant Professor in Operations, Information and Decisions

In collaboration with three highly sophisticated, intensive-care clinics operating within the University of Pennsylvania hospital system, this project equips medical providers with individually worn biometric sensors that closely track the workplace demands and stresses they experience while navigating swings in high acuity patients’ urgent needs.

Big Data and Analytics in Housing

Benjamin Keys, Rowan Family Foundation Associate Professor of Real Estate
Maisy Wong, James T. Riady Associate Professor of Real Estate; Assistant Director, Grayken Program in International Real Estate at the Zell/Lurie Real Estate Center

This project procures access to the The Corelogic Multiple Listing Services (MLS) database and includes 10 million observations of property listing data, and more than 600 variables that describe listing details. Professor Ben Keys will use the dataset for his research agenda: How is Climate Change Reshaping Housing and Mortgage Markets? Professor Maisy Wong will use the dataset to study the returns to scale of MLS platforms.

Health Care Data & Analytics Course

Matthew Grennan, Assistant Professor of Health Care Management

This funding supports the development of a new course: Healthcare Data & Analytics. This course will introduce MBA students to the healthcare data landscape and teach students to use algorithms, machine learning, AI, and causal inference to convert business questions into implementable solutions. This course will highlight real healthcare data analytics problems and discuss future trends and business opportunities in this field.

Incentivized Resume Rating

Corinne Low, Assistant Professor of Business Economics and Public Policy
Judd B. Kessler, Associate Professor of Business Economics and Public Policy

This project expands upon the recently published paper in American Economic Review, Incentivized Resume Rating: Eliciting Employer Preferences without Deception, by building two platforms to disseminate the research tools and insights from the IRR method targeted at researchers/policymakers and firms.

Learning by Doing

Wharton Customer Analytics

This project will develop real-world case studies for teaching data analytics and data science in the classroom based on current datasets from Wharton Customer Analytics partners. In addition, Wharton Customer Analytics will develop a series of in-person workshops that would provide an overview of the analytics basics that are necessary to understand data science, data management, and data visualization. In addition, these workshops will be supplemented by online modules and professional video recorded content for use by students, faculty, and alumni.

Wharton Energy Analytics Lab

Edgar Dobriban, Assistant Professor of Statistics
Eric J. Tchetgen Tchetgen, Luddy Family President’s Distinguished Professor, Professor of Statistics
Steven O. Kimbrough, Professor of Operations, Information and Decisions

Wharton Energy Analytics Lab brings to bear cutting-edge applications of machine learning techniques to position Wharton as an undisputed leader in connecting data analytics and energy markets to face societal challenges. The lab will develop research, teaching expertise, knowledge dissemination and outreach efforts to diverse audiences.

Funded Projects 2019

Data Science for Finance

Michael R. Roberts, William H. Lawrence Professor of Finance

This funding supports the development of a new course: Data Science for Finance. The course will introduce students to data science for financial applications using the Python programming language and its ecosystem of packages (e.g., Dask, Matplotlib, Numpy, Numba, Pandas, SciPy, Scikit-Learn, StatsModels). To do so, students will investigate a variety of empirical questions from different areas within finance including: FinTech, investment management, corporate finance, corporate governance, venture capital, private equity, and entrepreneurial finance. The course will highlight how big data and data analytics shape the way finance is practiced.

Effective Text Processing in New Domains: Transfer Learning for Word Embeddings

Hamsa Bastani, Assistant Professor of Operations, Information, and Decisions

While modern data analytics are incredibly effective at extracting valuable insight from text data (e.g., product reviews, nurses’ notes, etc.), they require an enormous amount of data; this research seeks to increase the applicability of state-of-the-art text analytics algorithms by transferring word embeddings for large-scale data to domains with small- and medium-scale datasets.

Environmental, Social, and Governance Analytics Lab

Witold Henisz, Deloitte & Touche Professor of Management in Honor of Russell E. Palmer, former Managing Partner and Director, Wharton Political Risk Lab

This project focuses on analyzing the materiality of businesses’ environmental, social and governance risks and opportunities and promotes faculty research, teaching, and student learning in this area.

People Analytics Video Project

Laura Zarrow, Executive Director of Wharton People Analytics

People Analytics will produce a slate of instructive, engaging, 5-15-minute videos from previous conferences and events that illuminate aspects of people analytics for students, industry professionals, and alumni.

The Promise and Peril of Algorithms in Human Resources

Prasanna Tambe, Associate Professor of Operations, Information, and Decisions

This research project conducts an empirical exploration of the relative costs and benefits of using machine learning based tools on video job application data during the hiring process.

The Science of the Deal: Deep Reinforcement Learning for Optimal Bargaining on eBay

Etan A. Green, Assistant Professor of Operations, Information, and Decisions

This research project trains an artificial intelligence to make optimal offers in negotiations on eBay.

Wharton Forensic Analytics Lab

Daniel Taylor, Associate Professor of Accounting

This project will develop research and teaching expertise on the application of Big Data and predictive analytics to issues related to insider trading, financial irregularities, and fraud. The Lab will aim to create new tools and technologies, academic research, and teaching and educational materials.

Women in Analytics and Data Science Conference

Mary Purk, Executive Director of Wharton Customer Analytics
Linda Zhao, Professor of Statistics

This conference, for Penn students, aims to inspire and educate data scientists, regardless of gender, and support women in analytics and data science-related careers. Planned for February 14, 2020, on Penn’s campus, this event is part of the larger Women in Data Science (WiDS) initiative originated at Stanford in November 2015 and includes a global conference, 150+ regional events, a datathon, and numerous podcasts.

If you are interested in submitting a proposal to Analytics at Wharton, please contact us at analytics@wharton.upenn.edu