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Systems that automatically learn and derive insight from data are already having a fundamental impact on our world — in areas from healthcare to self-driving cars — yet in many ways we are still in the early days. We are seeing the rapid development of new methods for modeling and predicting the world from data, and these methods are enabling new applications in automatically interpreting language and images.

Cornell Tech’s artificial intelligence group is at the forefront of these advances, and includes world experts in computer vision, natural language processing, and machine learning. Cornell Tech faculty work on the spectrum of AI methods, from fundamental algorithms to user-facing applications. We also believe that cross-disciplinary collaboration is key to having an impact on the real world.

 

 

 

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Yoav Artzi

Yoav Artzi is an Assistant Professor at Cornell Tech and the Department of Computer Science at Cornell University. Professor Artzi’s Language in Context group studies representations and learning algorithms for language understanding in context. Language understanding is a complex challenge that requires reasoning about linguistic meaning and its use in context, for example to resolve references to objects in the environment when instructing a robotic systems. The main focus of the group is developing methods for computer systems to learn to understand natural language through interaction with users and experimenting in the world. The goal is to create natural language systems that continuously learn, improve their language understanding, and acquire new language uses.

 

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Serge Belongie

Serge Belongie is a professor at Cornell Tech and the Cornell Computer Science Department. Professor Belongie's group specializes in human-in-the-loop computer vision and machine learning, in which humans and machines apply their complementary skills to solve challenging problems such as fine grained classification of plants and animals. Toward this end, his group explores the inner workings of deep convolutional neural networks, researches methods for capturing human perception of similarity between visual entities and develops algorithms for matching photos of 3D scenes captured from widely separated viewpoints. Applications of this work include assistive technology for the visually impaired, mobile field guides for naturalists (e.g., Merlin Photo ID) and recommendation engines to encourage healthy eating habits.

 

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Noah Snavely

Snavely is an associate professor in the Computer Science Department, working in the Cornell Graphics and Vision Group. Professor Noah Snavely’s group explores the use of massive, unstructured collections of online photos to understand our world. In the aggregate, the trillions of photos uploaded to the web each year reveal a rich portrait of the world – the cities and environments that surround us, and the patterns of activity that make up our everyday lives. Snavely’s research group develops new technology for tapping these unorganized image collections to model the world in 3D, to analyze images for computer graphics applications, and to detect trends in social media photos.

 

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Ramin Zabih

Ramin Zabih is a Professor of Computer Science at Cornell University and Cornell Tech and also holds a faculty appointment in the Radiology department at Weill Cornell Medical College. Professor Zabih's group focuses on algorithmic techniques for computer vision problems, with an emphasis on discrete optimization methods. Applications include traditional vision areas such as stereo, motion, image stitching and segmentation, as well as emerging topics in medical imaging. The group has ongoing collaborations with a wide range of colleagues, including theoretical computer scientists interested in algorithms, industrial researchers focused on applications, and clinical scientists addressing patient care.