Advancing AI Knowledge
Through experiential learning programs and sponsored research, students and faculty are empowered to deeply explore how artificial intelligence will impact the future of business. AIB is proud to partner with faculty across the University in an interdisciplinary effort to advance AI knowledge.
Call for Proposals Due April 1, 2021
Wharton and Penn faculty are invited to submit proposals that demonstrate the need for financial support and infrastructure to enhance faculty research that showcase the applications of machine learning on the business and social implications of AI. We encourage faculty to work across the University in an interdisciplinary way to advance AI knowledge. If interested in submitting a proposal, please contact analytics@wharton.upenn.edu.
Sponsored Research
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.
Developing and Using an AI Negotiator
Maurice E. Schweitzer, Cecilia Yen Koo Professor; Professor of Operations, Information and Decisions
This project will support the development and use of an AI-powered chatbot platform for negotiations.
The Problems and Perils 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 Deep Learning: Deep Reinforcement Learning
Etan A. Green, Assistant Professor of Operations, Information, and Decisions
This research project trains artificial intelligence to make optimal offers in negotiations on eBay.
Faculty Director
Kartik Hosanagar
The Wharton School
John C. Hower Professor
Professor of Operations, Information and Decisions
Research Interests: trust in AI; impact of algorithms on consumers and society
Featured Course: OIDD 399, 699, 899: AI, Business, and Society
Affiliated Faculty
Hamsa Bastani
The Wharton School
Assistant Professor of Operations, Information and Decisions
Research Interests: algorithmic decisions; algorithmic design; natural language processing (NLP) systems for online trafficking; meta-learning
Ryan Dew
The Wharton School
Assistant Professor of Marketing
Research Interests: marketing applications of machine learning (ML); algorithmic decisions; machine learning (ML) for CRM and branding
Edgar Dobriban
The Wharton School
Assistant Professor of Statistics
Research Interests: theoretical foundations of deep learning; machine learning (ML) for vulnerability identification; large-scale machine learning (ML)
Featured Course: STAT 991: Topics in Deep Learning
Amit Gandhi
The Wharton School
Professor of Economics and Marketing
Research Interests: quantitative analytics; economics of technology; impact of technology; cloud computing service
Michael Kearns
Penn Engineering
Professor and National Center Chair
Research Interests: machine learning; algorithmic fairness; differential privacy; algorithmic game theory; quantitative finance
Featured Course: CIS 399: Science of Data Ethics
Steven O. Kimbrough
The Wharton School
Professor of Operations, Information and Decisions
Penn Arts & Sciences
Professor of Philosophy
Research Interests: agent-based modeling; machine learning (ML) for constrained optimization; algorithmic decisions
Brian Litt, MD
Perelman School of Medicine
Professor of Neurology and Professor of Bioengineering
Research Interests: computational neuroscience; deep brain stimulation; network neuroscience
Featured Center: Penn Health-Tech
Jason H. Moore, PhD, FACMI
Perelman School of Medicine
Edward Rose Professor of Informatics
Director, Institute for Biomedical Informatics
Research Interests: Artificial intelligence; automated machine learning; biomedical data science; biomedical informatics
Featured Center: Institute for Biomedical Informatics
Daniel Rock
The Wharton School
Assistant Professor of Operations, Information and Decisions
Research Interests: valuation of AI-related skill; impact of AI on the workforce; measurement of AI-related capital investment; machine learning in economics
Aaron Roth
Penn Engineering
Professor of Computer and Information Science
Research Interests: algorithmic fairness; algorithmic game theory; private data analysis; algorithmic foundations of data privacy
Weijie Su
The Wharton School
Assistant Professor of Statistics
Research Interests: deep learning (DL) for statistical estimation; privacy-preserving machine learning (ML); large-scale optimization
Featured Course: STAT 991: Optimization Methods in Machine Learning
Prasanna Tambe
The Wharton School
Associate Professor of Operations, Information and Decisions
Research Interests: HR applications of AI & data science; impact of AI on the labor market
Featured Course: OIDD 255X: AI, Business, and Society
Lyle Ungar
Penn Engineering
Professor of Computer and Information Science
Research Interests: explainable AI; natural language processing (NLP) methods for psychology and medical research; social media analysis; bioinformatics
Featured Course: CIS 520: Machine Learning
Kevin Werbach
The Wharton School
Professor of Legal Studies & Business Ethics
Research Interests: AI and Blockchain; AI ethics; algorithmic accountability; impact of AI on social systems
Featured Course: LGST 242/642: Big Data, Big Responsibilities: The Law and Ethics Of Business Analytics
Lynn Wu
The Wharton School
Associate Professor of Operations, Information and Decisions
Research Interests: impact of AI; AI-enabled innovation; data analytics for economic indicator prediction; biases in emerging technologies
Linda Zhao
The Wharton School
Professor of Statistics
Research Interests: predictive machine learning (ML) models; network formation; predictive model with network data; bayesian methods; signal detection
Featured Program: Data Science Live, Fall 2019; Data Science Live, Spring 2019
Featured Course: STAT471/571/701: Modern Data Mining