News & Events


Neural Networks Model Audience Reactions to Movies


Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, and colleagues have created a new deep-learning software capable of assessing complex audience reactions to movies using the viewer's facial expressions. "Understanding human behavior is fundamental to developing AI [artificial intelligence] systems that exhibit greater behavioral and social intelligence. For example, developing AI systems to assist in monitoring and caring for the elderly relies on being able to pick up cues from their body language. After all, people don't always explicitly say that they are unhappy or have some problem," Professor Yue says. [Caltech story]

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MIT Sloan Sports Analytics Conference


Graduate student, Hoang M. Le, from Professor Yisong Yue’s group was runner-up for the best paper award at the MIT Sloan Sports Analytics Conference. He was recognized for his paper, Data-Driven Ghosting using Deep Imitation Learning. [Read the paper]

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Realtime Camera Planning


Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, is working with colleagues at Disney Research to develop machine-learning algorithms to make automated cameras more human-like.  Professor Yue's research group is generally interested in building AI systems that imitate demonstrated behavior, including laboratory animals, basketball players, humans playing video games, etc.  In this recent work with Disney Research, they are developing an automated camera system that learns how best to film sports matches by watching how human camera operators behave at particular moments. Early testing shows that its shots are far smoother than other automated cameras. [Learn more about the applications] [Learn more about the theory] [techradar story] [Sports Illustrated story]

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Professor Yue Receives Bloomberg Data Science Grant


Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, is a recipient of the Bloomberg Data Science Research Grant Program. The program aims to support cutting-edge research in the broad field of machine learning, including specific areas such as natural language processing, information retrieval, machine-translation and deep neural networks. Professor Yue has proposed to study an alternative notion of interpretability, which he calls “dynamic interpretability”. The goal of dynamically interpretable models is to make predictions that are interpretable, rather than have the model itself be explicitly interpretable. With this alternative goal, one can circumvent much of the inherent tension between accuracy and traditional “static” interpretability, and move one step closer to interpretable production-strength models.[Bloomberg release]

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Converting Data Into Knowledge


Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, has focused his research in machine learning. He explains, “machine learning is the study of how computers can take raw data or annotated data and convert that into knowledge and actionable items, ideally in a fully automated way—because it's one thing to just have a lot of data, but it's another thing to have knowledge that you can derive from that data.” [Interview with Professor Yue] [ENGenious article]

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