Projects and Labs
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 2021-2022 academic year, proposals are due on October 1, 2021 and April 1, 2022. If interested in submitting a proposal, please contact email@example.com.
Analytics Projects and Labs
Through our labs and projects, Analytics at Wharton incubates new ventures that support cutting-edge research and education in diverse industries and academic fields.
“Data analytics is the engine that powers Finance. With the support of Analytics at Wharton, I have been able to create a novel and entirely different type of finance course – Data Science for Finance – that will empower our students with cutting edge financial decision-making skills.”
— Michael Roberts, William H. Lawrence Professor, Professor of Finance
“The WiDS Conference is an invaluable resource to support women interested in learning business analytics and data science. By providing a platform for female professionals to share their research and professional journey, Analytics at Wharton is inspiring and supporting the next generation of data science leaders.”
– Mary Purk, Executive Director of Wharton Customer Analytics and AI for Business
COMPUTATIONAL SOCIAL SCIENCE
Uniting computer science, statistics, and social science to solve real-life problems through mass collaboration on path-breaking transparent research in partnership with industry, government, and civil society.
ENVIRONMENTAL, SOCIAL AND GOVERNANCE (ESG) ANALYTICS LAB
The ESG Analytics Lab focuses on developing high-quality, replicable academic research and pedagogy resulting in insights that can help current and future investors, asset managers and other ESG integrators make informed decisions.
Wharton Forensic Analytics Lab
Wharton Forensic Analytics Lab aims to be the world’s foremost source of research and teaching expertise on the application of data analytics to issues related to insider trading, financial irregularities, and corporate transparency.
INCENTIVIZED RESUME RATING (IRR)
A new tool to study what employers value in hiring, and work to eliminate bias.
Projects by the Numbers
Funded Projects Spring 2021
An Automated Solution to Causal Inference in Discrete Settings
Dean Knox, Assistant Professor of Operations, Information, and Decisions
Rachel Mariman, Research Project Manager
The goal of this project is to create a tool to automate causal inference from incomplete or imperfect data. This tool will reach a broad audience of applied researchers across the social and medical sciences by developing an easy-to-use front-end interface and implement more efficient back-end optimizations. In addition, the project will create a series of data applications to illustrate its ease of use. This project is funded by AI for Business.
The Elasticity of Taxable Income
Benjamin Lockwood, Assistant Professor of Business Economics and Public Policy
This project seeks to characterize and advertise a new statistical method for estimating the elasticity of taxable income in the presence of optimization frictions.
The Identification of Judgmental Forecasting Techniques
Philip Tetlock, Professor of Management
Dillon Bowen, Doctoral Candidate of Operations, Information, and Decisions
This project will compare the effectiveness of many judgmental forecasting techniques on a standard set of forecasting tasks, delivering a concise list of highly effective techniques and recommended best practices.
The Impact of an Experiential Learning Pathway on Knowledge Application and the Relevance to Future Employment Opportunities
Raghu Iyengar, Miers-Busch, W’1885 Professor; Professor of Marketing; Faculty Director, Wharton Customer Analytics
Nicole Wang Trexler, Associate Director of Data Science and Research
This project conducts a quasi-experimental research study to gain an in-depth look at how an experiential learning pathway impacts students’ informal learning journey. This information will help WCA design and deploy better experiential learning pathways to better serve the students at Wharton and within the Penn community.
Measuring the Narratives of the COVID-19 Pandemic
Duncan Watts, Stevens University Professor of Computer and Information Science, Communication, and Operations, Information and Decisions
Baird Howland, PhD Student, Annenberg School of Communication, Computational Social Science Lab at Penn,
Valery Yakubovich, Executive Director, Computational Social Science Lab at Penn
The goal of this project is to study our understanding of the COVID-19 pandemic – the remarkably varied conceptions of what is happening, why it is happening, and what should be done in response, with a rare combination of quantitative rigor and qualitative depth.
Unmasking Sex Trafficking Supply Chains with Machine Learning
Hamsa Bastani, Assistant Professor of Operations, Information, and Decisions
Pia Ramchandani, Doctoral Candidate of Operations, Information, and Decisions
In collaboration with the Tellfinder Alliance for Global Counter-Human Trafficking, this project will leverage unstructured, massive deep web data from leading adult-services websites using a novel machine learning framework to construct the first global network view of sex trafficking supply chains.
Wharton Forensic Analytics Lab Data Case Series
Dan Taylor, Associate Professor of Accounting
This five-part case series will highlight recent accounting frauds (e.g., Wirecard, Luckin Coffee, etc.) and how each of the frauds could have been detected using business analytics.
Funded Projects Fall 2020
AI’s Effect on Innovation and Productivity
Lorin Hitt, Zhang Jindong Professor; Professor of Operations, Information and Decisions
Lynn Wu, Associate Professor of Operations, Information and Decisions
This research explores how AI facilitates innovation by documenting specific cases and mechanisms on when AI technologies should be used to innovate and when they should not, and their implications on demand for different types of labor and productivity. This project is co-sponsored by AI for Business and Analytics at Wharton.
Applied Neuroscience and Business Analytics Summer Undergraduate Internships for Underrepresented Students
Michael Platt, James S. Riepe University Professor of Marketing, Neuroscience, and Psychology; Faculty Director, Wharton Neuroscience Initiative
Elizabeth Johnson, Executive Director, Wharton Neuroscience Initiative
Wharton Neuroscience Initiative will support two underrepresented undergraduate summer students focused specifically on Applied Neuroscience and Business Analytics for a 10-week summer internship program. These students will be part of a new, larger applied brain and cognitive science summer undergraduate internship program which aims to combat systemic inequalities and a lack of diversity that plague neuroscience, brain and behavioral science, analytics, and data science careers.
Data Analytics for Economic Efficiency in Energy Policy
Susanna Berkouwer, Assistant Professor of Business Economics and Public Policy
Arthur van Benthem, Associate Professor of Business Economics and Public Policy
This project aims to understand and quantify inefficiencies in government regulations related to the environment and to provide tangible recommendations for designing smarter policies that address environmental concerns while driving economic growth.
Developing and Using an AI Negotiator
Maurice E. Schweitzer, Cecilia Yen Koo Professor; Professor of Operations, Information and Decisions
T. Bradford Bitterly, Assistant Professor of Management, HKUST Business School
Alex Hirsch, Research Coordinator, Operations, Information and Decisions
This project will support the development and use of an AI-powered chatbot platform for negotiations. This project is co-sponsored by AI for Business and Analytics at Wharton.
Machine Learning and Hiring: Evidence from a Manufacturing Firm in China
Shing-Yi Wang, Associate Professor of Business Economics and Public Policy
Jing Cai, Assistant Professor at University of Maryland
This research explores how firms can improve their hiring decisions by employing state-of-the-art machine learning methods that use observable characteristics of applicants to predict their probability of staying in the job and their performance.
Reforming Police Misconduct Investigations
Dean Knox, Assistant Professor of Operations, Information, and Decisions
Rachel Mariman, Research Project Manager, Analytics at Wharton
Using unique access to an archive of administrative records on civilian complaints against police, this multi-wave experimental study seeks to assess how Philadelphia residents understand and perceive the current civilian complaint process, and systematically evaluate the impact of transparency initiatives on civic engagement and public trust in police.
Start-up to Scale-up: Large-sample Evidence from Online Job Postings
J. Daniel Kim, Assistant Professor of Management
Saerom (Ronnie) Lee, Assistant Professor of Management
This project examines the scaling of startups by applying cutting-edge machine learning and econometric tools to assess a novel big dataset of more than 200 million jobs.
Statistical Software for Single Cell CRISPR Screens
Eugene Katsevich, Assistant Professor of Statistics
Single cell CRISPR screen technology, proposed a few years ago, offers unprecedented opportunities to unravel the molecular mechanisms of human disease and guide drug development. The objective of this project is to produce a high-quality software implementation of Dr. Katsevich’s SCEPTRE methodology, with the goal of broad adoption among the genomics community.
Ryan Dew, Assistant Professor of Marketing
This research seeks to enhance our understanding of the role that aesthetics play in consumer decision-making and perceptions of companies and to develop effective tools that help companies craft data-driven visual brands, products, and platforms.
Wharton Undergraduate Capstone Course: Managing the Pandemic Money and Messages
Robert P. Inman, Richard King Mellon Professor Emeritus of Finance; Professor Emeritus of Business Economics & Public Policy
This project uses data collection for a student-led evaluation of the health and economic consequences of the Covid-19 pandemic and the effectiveness of national and state-wide policy responses to contain the coronavirus and to mitigate its health and economic consequences. The format for this evaluation will be a Capstone Course (BEPP 401) entitled, Managing the Pandemic: Messages and Money.
Funded Projects Spring 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 Fall 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.