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James Zou's indexed work centers on developing and applying machine learning techniques, particularly deep learning, to address challenges in biology, medicine, and social equity. His research includes quantifying gender and ethnic stereotypes using word embeddings, as evidenced by a highly cited 2018 PNAS paper. He has also significantly contributed to video-based AI for assessing cardiac function, highlighted by a 2020 Nature publication, and has provided a foundational primer on deep learning in genomics through a 2018 Nature Genetics paper. A consistent theme in his work is the critical examination of AI's societal impact, including explorations of AI fairness and the fragility of neural network interpretations. Beyond these core areas, Zou has contributed to broader discussions on sex and gender analysis in science and engineering. His earlier work, such as a 2013 Cell paper, explored genome-wide chromatin state transitions associated with developmental and environmental cues. The arc of his contributions demonstrates a consistent focus on developing robust and interpretable AI methods and applying them to complex biological and societal problems, often with an emphasis on ethical considerations and real-world impact.
David Ouyang, Bryan He, Amirata Ghorbani, Neal Yuan, Joseph Ebinger, Curtis P Langlotz, Paul A Heidenreich, Robert A Harrington, David H Liang, Euan A Ashley, James Y Zou
James Zou, Mikael Huss, Abubakar Abid, Pejman Mohammadi, Ali Torkamani, Amalio Telenti
Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas J Montine, James Zou
Kyle Swanson, Eric Wu, Angela Zhang, Ash A Alizadeh, James Zou
Bryan He, Ludvig Bergenstråhle, Linnea Stenbeck, Abubakar Abid, Alma Andersson, Åke Borg, Jonas Maaskola, Joakim Lundeberg, James Zou
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