Announcing Our LinkedIn-Cornell 2023 Grant Recipients
August 2, 2023
LinkedIn and Cornell Ann S. Bowers College of Computing and Information Science (Bowers CIS) embarked on a partnership, bringing together our collective research power to make technological advances that will further our goal to connect professionals with opportunities at scale. Through this partnership, we support Ph.D. students and faculty members on their research in areas in Computer Science, AI, Information Science including Diversity and Equity. Our 2022 recipients showcased the strong value of our partnership through incredible research in diverse topics including Allison Koenecke’s research on early stoppage of randomized controlled trials on heterogeneous populations, David S. Matteson’s research on deep generative models to enable fairer and more responsible AI, and Ruihan Wu’s research in privacy in machine learning. Riding on the back of such tremendous success, we are excited to announce the 2023 grant recipients.
Our 2023 grant recipients’ research includes high-impact projects on topics ranging from large-language models and recommender systems to dynamic information retrieval and algorithmic fairness:
Princewill Okoroafor | Computer Science, advised by Robert Kleinberg: Princewill’s project aims to advance our understanding of limitations and trade-offs in predictive models as well as develop new algorithms that can achieve more accurate and calibrated predictions – all of which can have a significant impact on large scale recommender systems.
Sarah Dean | Computer Science: Sarah intends to develop algorithms that can make decisions in the short term while ensuring long-term platform health.
Mor Naaman | Information Science: Mor will build on responsible AI research by providing a deeper understanding of how autocomplete functions, powered by new large language models (LLMs), can transform how we express ourselves.
Cristian Danescu-Niculescu-Mizil | Information Science: Cristian will develop methods for endowing artificial systems with intuition and explore how this can be used to improve how we communicate.
Shira Mingelgrin | Statistics & Data Science, advised by Sam Wang: Shira will apply causal discovery methods to investigate potential differences in the factors that drive hiring behavior between genders.
Benjamin Laufer | Information Science, advised by Jon Kleinberg and Helen Nissenbaum: Benjamin will be looking to develop new methods for identifying, measuring, and mitigating the harmful dynamics that arise in algorithmic recommendations.
Jonathan Chang | Computer Science, advised by Wen Sun: Jonathan will investigate algorithms that can help reduce the data needed to train reinforcement learning systems, further improving responses in chatbot recommendation systems.
Teaming up with the Cornell Bowers CIS to is an important step in bringing industry and academia together to collaborate on the advancements in technology. We look forward to learning from one another through this research partnership and thinking about how these learnings will help us forge the economy of the future.