Cornell Bowers College of Computing and Information Science

Technology helps self-driving cars learn from own memories

By Tom Fleischman for the Cornell Chronicle

June 21, 2022

An autonomous vehicle is able to navigate city streets and other less-busy environments by recognizing pedestrians, other vehicles and potential obstacles through artificial intelligence. This is achieved with the help of artificial neural networks, which are trained to “see” the car’s surroundings, mimicking the human visual perception system.

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Harnessing machine learning to analyze quantum material

June 14, 2022

By Kate Blackwood for the Cornell Chronicle

Electrons and their behavior pose fascinating questions for quantum physicists, and recent innovations in sources, instruments and facilities allow researchers to potentially access even more of the information encoded in quantum materials.

However, these research innovations are producing unprecedented  – and until now, indecipherable – volumes of data.

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Machine learning tool sorts the nuances of quantum data

An interdisciplinary team of Cornell and Harvard University researchers developed a machine learning tool to parse quantum matter and make crucial distinctions in the data, an approach that will help scientists unravel the most confounding phenomena in the subatomic realm.

The Cornell-led project’s paper, “Correlator Convolutional Neural Networks as an Interpretable Architecture for Image-like Quantum Matter Data,” published June 23 in Nature Communications. The lead author is doctoral student Cole Miles.

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Kilian Weinberger Finalist for Blavatnik Foundation and New York Academy of Sciences National Award for Young Scientists

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