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Machine Learning
Cornell team wins $50K in AI puzzle-solving challenge contest

Cornell team wins $50K in AI puzzle-solving challenge contest

A team from Cornell led by Kevin Ellis, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science, has developed a set of AI models that together, solve about 56% of the problems – scoring within 4 percentage points of the average human. Their paper, “Combining Induction and Transduction for Abstract Reasoning,” received a first-place-paper award at the 2024 ARC Prize competition. Lead authors on the paper were Wen-Ding Li, a doctoral student in the field of computer science, and Keya Hu, a visiting undergraduate student in Ellis’ group.

Choudhury wins Navy Young Investigator award to train robots

Choudhury wins Navy Young Investigator award to train robots

Sanjiban Choudhury, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science, just received a three-year, $750,000 Young Investigator Program award from the Office of Naval Research (ONR) to develop new ways to train robots to perform complex, multistep tasks, such as inspecting and repairing ship engines.

Rising star Ben Laufer: Improving accountability and trustworthiness in AI

Rising star Ben Laufer: Improving accountability and trustworthiness in AI

With artificial intelligence increasingly integrated into our daily lives, one of the most pressing concerns about this emerging technology is ensuring that the new innovations being developed consider their impact on individuals from different backgrounds and communities. The work of researchers like Cornell Tech PhD student Ben Laufer is critical for understanding the social and ethical implications of algorithmic decision-making.

Diagnostic tool identifies puzzling inflammatory diseases in kids

Diagnostic tool identifies puzzling inflammatory diseases in kids

A Cornell-led collaboration developed machine-learning models that use these cell-free molecular RNA dregs to diagnose pediatric inflammatory conditions that are difficult to differentiate. The diagnostic tool can accurately determine if a patient has Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), a viral infection or a bacterial infection, while simultaneously monitoring the patient’s organ health.