LinkedIn and Cornell Fund AI Research

LinkedIn is deepening its academic ties with the Cornell Ann S. Bowers College of Computing and Information Science (Cornell Bowers CIS) through its annual grant program. The initiative, now in its third year, focuses on foundational research addressing critical industry and academic challenges. This year, eight recipients—four faculty members and four doctoral students—will receive funding for projects spanning AI, data science, and privacy.

This latest round of LinkedIn Cornell 2024 Grant Recipients reflects a growing emphasis on complex technological and societal issues. The funded research aims to foster fairness, inclusion, and long-term positive impacts in technology, aligning with broader trends in AI research grants.

Faculty Research

Sarah Dean, assistant professor of computer science, will investigate how social network algorithms optimize for short-term engagement over long-term user impact. Her project seeks to develop models that anticipate and optimize for sustained user value.

Michael P. Kim, also an assistant professor of computer science, is tackling fairness in machine learning. His work, "Prediction as Intervention: Promoting Fairness when Predictions have Consequences," examines how predictive algorithms can inadvertently harm marginalized groups and explores ways to create new opportunities through intentional prediction.

Jennifer J. Sun, assistant professor of computer science, plans to leverage large language models (LLMs) to extract actionable insights from knowledge graph text data. Her goal is to build scalable systems for applications like skills matching and career recommendations.

Daniel Susser will focus on privacy-enhancing technologies (PETs). His project aims to develop shared frameworks to improve communication and deliberation around PET usage among data subjects, researchers, companies, and regulators.

Doctoral Student Research

Sophie Greenwood, a doctoral student, will examine the effectiveness of "multi-sided fairness" in recommendation algorithms, assessing their impact on platform engagement and individual users.

Kowe Kadoma, a doctoral student, is studying how personalized language in LLMs affects user trust and feelings of inclusion. Her research aims to make LLMs more responsive to users' individual communication styles.

Abhishek Vijaya Kumar, a doctoral student, is developing systems for efficient resource sharing on multi-GPU clusters to accelerate generative AI inference.

Kaiwen Wang, a doctoral student, will create adaptive, safe, and steerable reinforcement learning algorithms, with potential applications in healthcare and conversational agents, building on work related to AI research grants.