Microsoft Research Lab – Redmond
Groupes
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The Ability team conducts research at the intersection of HCI and AI, with the aim of developing innovative technologies that extend the capabilities of and enhance quality of life for people with disabilities (long-term, temporary or situational).
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The Adaptive Systems and Interaction team pursues the study and development of principles, applications, and tools for extending our understanding of principles of computational intelligence as well as developing and fielding trustworthy AI systems. Our work is motivated by the…
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The Augmented Learning and Reasoning group's mission is to augment AI models, technology, and people with new capabilities to transform what is possible. The group brings together expertise in Microsoft Research across different professional and academic disciplines to work together…
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All computer systems, be it a standalone device, a datacenter, the internet, an advertising platform, deep neural networks or the internet of things rely on sound algorithmic ideas at their foundations.
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Since its inception in 2012, the Microsoft Research Artist in Residence Program has hosted a number of artists in residence, including James George, Jason Salavon, Cristina Sirbu, Erin Smith, Aduen Darriba Frederiks, Helene Steiner, and Maja Petrić. The AIR program…
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The Audio and Acoustics group conducts research in audio processing and speech enhancement, 3D audio perception and technologies, devices for audio capture and rendering, array processing, information extraction from audio signals.
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The Autonomous Systems and Robotics Group works on the research and development of training and simulation technologies for robotics systems. We investigate multiple domains at the intersection of Robotics and Machine Learning such as Computer Vision, Imitation/Reinforcement Learning, Controls, and…
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Enabling the people closest to business challenges to resolve them using intelligent apps.
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At Microsoft Research, our causality research spans a broad array of topics, including: using causal insights to improve machine learning methods; adapting and scaling causal methods to leverage large-scale and high-dimensional datasets; and applying all these methods for data-driven decision…