# LinkedIn and Cornell Fund AI Research _LinkedIn and Cornell University announce the 2024 grant recipients, funding eight projects focused on AI, data science, and privacy._ **Updated:** 2026-08-22 **Published:** 2026-05-22 **Source:** https://www.startuphub.ai/ai-news/technology/2026/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. LinkedIn & Cornell PartnershipCoreannual grant program now in its third yearFrom the article 2 mentionsLinkedIn is deepening its academic ties with the Cornell Ann S.Funding AI ResearchDriverFrom the article 5 mentionsThis year, eight recipients, four faculty members and four doctoral students, will receive funding for projects spanning AI, data science, and privacy.Grant RecipientsContextfour faculty members and four doctoral students fundedFrom the article 5 mentionsThis latest round of LinkedIn Cornell 2024 Grant Recipients reflects a growing emphasis on complex technological and societal issues.Faculty Research FocusContextoptimizing social network algorithms for user impactDoctoral Student ResearchContextprojects spanning AI, data science, and privacyFrom the article 5 mentionsKaiwen 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.Foster Positive ImpactEffectFrom the articleThe funded research aims to foster fairness, inclusion, and long-term positive impacts in technology, aligning with broader trends in AI research grants.Advance Tech & SocietyOutcomeaddressing critical industry and academic challenges This latest round of [LinkedIn Cornell 2024 Grant Recipients](https://www.linkedin.com/blog/engineering/culture/announcing-our-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](/ai-news/artificial-intelligence/2026/georgia-tech-president-ai-is-reshaping-higher-education). ## 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](/ai-news/ai-research/2026/multicalibration-solves-llm-bias-under-shift). 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](/ai-news/ai-research/2026/agentic-ai-fails-loops-planning-unsafe-tool-use). --- Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory. © StartupHub.ai. All rights reserved. You may not republish this article in full without a license. Search engines and AI research tools may crawl and summarize for reference. Bulk reproduction or model training on this content requires a license. See https://www.startuphub.ai/terms.