Algorithms, machine learning, and blockchain are among the many technology breakthroughs roiling the business world these days as connectivity, artificial intelligence, and automation fuel one of the biggest upheavals in business practices in the history of industry.

From AI in the workplace and climate technology to big data market analysis and algorithms to improve dating app insights, the work of our faculty is everywhere because technology has forced every business to become digitally literate.

CBS researchers not only study and delineate this phenomenon, they also drive it in many sectors. Within the digital space, our professors write code and build and study huge data sets. They coach businesses striving to leverage the huge amounts of data now available.

For example, real estate firms are benefitting from the research of CBS Professor Stijn Van Nieuwerburgh, the Earle W. Kazis and Benjamin Schore Professor of Real Estate, who demonstrates the value of data analysis with his studies using big data to find new perspectives on real estate market trends.

Vice Dean for Research Oded Netzer, the Arthur J. Samberg Professor of Business and an Amazon Scholar, explores the use of unstructured data (text, image, video, and audio) to help companies make better data-driven decisions.

In the field of finance, Ciamac C. Moallemi, the William Von Mueing Professor of Business, uses large-scale stochastic systems with applications in financial engineering to understand decision-making under uncertainty.

And Daniel Russo, the Philip H. Geier Jr. Associate Professor of Business, uses machine learning to develop algorithms that help Spotify tailor music recommendations to individual listeners.

These are just a few examples of the ways CBS researchers are contributing to the digital revolution.

The following selections of research highlight some of the ways our faculty are developing the models and tools to facilitate data collection, as well as applying those tools to make discoveries.

Advertising

Santiago Balseiro

  • Informing Online Advertising
  • Professor Santiago Balseiro’s research on auction design is offering new tools to big tech. He’s developing innovative methods of researchbased online advertising that have gained the interest of Google, Meta, Microsoft, and Yahoo.

Hortense Fong

  • Machine Learning Emotions in Advertising
  • Professor Hortense Fong has developed a neural network to predict emotional response to music. In application, her work offers advertisers a method for testing the effectiveness of emotion-based ad placements in videos. She has shown that emotion-based ads can produce higher brand recall rates if they are placed near content expressing similar emotions.

Oded Netzer

  • Driving E-Commerce
  • Professor Oded Netzer is an Amazon Scholar whose work helps fuel the platform’s Advertising Analytics and Insights initiative.

Olivier Toubia and Shawndra Hill

  • Improving Search Engine Performance
  • Professors Olivier Toubia and Shawndra Hill are shaping search engine performance with new models that could help search engines and advertisers better match their results with customers’ preferences.

Artificial Intelligence

Hongseok Namkoong

  • Prioritizing Fairness in the Workplace
  • Professor Hong Namkoong is using machine learning to design office workflows organized around considerations of fairness. He and his team have developed a framework for evaluating and contextualizing fairness within complex organizations.

Consumer Behavior

Gita Johar and Yu Ding

  • Finding the Truth in News
  • Professor Gita Johar and Yu Ding, PhD '22, have devised an innovative approach to combat misinformation by leveraging crowdsourcing to verify the truthfulness of news. Their proposal involves third-party fact checkers, such as Gigafact contributors or PolitiFact, and news platforms like Twitter and Facebook, enabling users to rate the similarity of news in articles that cover the same topic. According to the researchers, similarity ratings can be an effective tool for reducing bias in veracity testing by revealing shared facts among similar articles.

Daniel Russo

  • Using Machine Learning to Affect Decision-Making
  • Professor Daniel Russo is working with Spotify to enhance personalized recommendations. He’s building machine learning algorithms that optimize for the long term and are capable of learning by trial and error.

Fanyin Zheng

  • Improving Dating Apps
  • Professor Fanyin Zheng is helping increase match rates on dating apps. Her algorithms leveraging user behavior have improved match rates by 40 percent.

Sandra Matz

  • Using Data to Understand Psychology
  • Professor Sandra Matz’s research shows that the data used to create psychological profiles of consumers can be employed in positive ways. Specifically, her work has helped to reduce college dropout rates and identify early signs of depression, which can lead to timely intervention and support.

Corporate Strategy

Tania Babina

  • Showing the Benefits of Sharing
  • Professor Tania Babina is informing bank regulation with her work on consumer data collection. She finds that policies making it easier for banks to share customer data lead to an increase in fintech investment because companies with more data can improve customer screening processes and product design.

Data Analysis

Laura Veldkamp

  • Updating Finance Tools for the Data Economy
  • Professor Laura Veldkamp is developing new finance tools to price data collection. She’s measuring the potential value added to companies that use digitized information to reduce uncertainty and minimize risks.

Omar Besbes

  • Proving Data Makes a Difference
  • Professor Omar Besbes is providing evidence to support the use of data analytics in the workplace. Through his research, he has demonstrated that companies can achieve better solutions in areas such as capacity management and pricing, even when using small data sets.

Finance

Tomasz Piskorski

  • Exposing Shadow Banking
  • Professor Tomasz Piskorski has found that when traditional banks contract under regulatory constraints in the mortgage market, fintech and shadow-bank lending rise. His mortgage lending models suggest regulation accounts for roughly 60 percent of shadow-bank growth, while technology accounts for roughly 30 percent.

Sehwa Kim

  • Outing Insider Trading
  • Professor Sehwa Kim is exposing the vulnerabilities in fragmented securities regulation. His data sets suggest that higher levels of insider trading can result when bank regulators, rather than the Securities and Exchange Commission, oversee disclosure regulation.

Healthcare

Carri Chan

  • Using Data to Save Lives
  • Professor Carri Chan is improving hospital care with data analytics and mathematical models that help hospitals better manage healthcare delivery in resource-constrained environments, including during the COVID-19 pandemic. Her recommendations on nurse staffing were piloted in the Weill-Cornell emergency department in New York City.

Assaf Zeevi

  • Using AI to Target Healthcare Interventions
  • Professor Assaf Zeevi is developing a machine learning tool to aid healthcare providers in identifying malnourished patients who would benefit from nutrition support while hospitalized. His tool aims to improve patient outcomes, as a diagnosis of malnutrition is commonly linked with unfavorable hospital outcomes.

Real Estate

Stijn Van Nieuwerburgh

  • Using Big Data to Understand Real Estate
  • Professor Stijn Van Nieuwerburgh is studying the impact of the pandemic on real estate markets. His research using large data sets, which enable him to predict behavior, suggests the dramatic increase in remote work during the pandemic will continue, with long-lasting implications for residential and commercial real estate.

Workplace

Bo Cowgill

  • Forecasting the Future of AI
  • Professor Bo Cowgill’s work is showcasing the potential of AI in the workplace, particularly in hiring decisions. His research demonstrates how AI algorithms can outperform humans in such tasks, highlighting the benefits of incorporating these tools into traditional office settings.

Bruce Kogut

  • Determining Value in AI
  • Professor Bruce Kogut’s research sheds light on the circumstances where AI may not be as useful in the workplace. Specifically, his findings indicate that replacing a human team member with an AI counterpart can lead to a decrease in team performance, an increase in coordination failures, and a reduction in both team trust and individual effort. Kogut argues that human-machine interaction is critical for realizing the positive impact that AI can have on teams, organizations, and work practices in general.

Wei Cai

  • Having Fun at Work
  • Professor Wei Cai is improving the workplace experience with research that finds, on average, gamified training platforms can have a highly positive effect on workplace performance.