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Roderick van den Berg is a Staff Research Scientist at Google AI, based in Amsterdam. His research primarily focuses on deep generative models, with significant contributions to areas such as audio synthesis (like WaveNet), image generation, video understanding, and developing efficient neural network architectures. He is passionate about advancing the capabilities of artificial intelligence to understand and generate complex data, often publishing his work in top-tier machine learning conferences.
Roderick van den Berg's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Played a significant role in the development of WaveNet, a deep generative model for raw audio that revolutionized text-to-speech and music synthesis quality by generating more natural-sounding audio.
Contributed extensively to research in generative adversarial networks (GANs) and other deep learning models for high-fidelity image synthesis, image super-resolution, and video generation.
Developed and researched methods for creating more computationally efficient deep learning models, enabling powerful AI on devices with limited resources or reducing the environmental impact of large-scale models.
Authored and co-authored numerous influential papers presented at leading artificial intelligence and machine learning conferences such as NeurIPS, ICML, ICLR, and CVPR, contributing significantly to the academic and research community.
Contributed to Google's large language models for video generation, such as VideoPoet, which can generate and edit video from text, image, and video prompts, and Phenaki, capable of generating minutes-long videos from text.
Technische Universiteit Delft - Year 1997
Stedelijk Gymnasium Haarlem - Year 1990
Highperformr Signals uncover buying intent and give you clear insights to target the right people at the right time — helping your sales, marketing, and GTM teams close more deals, faster.
Eco-Movement is a leading global data platform for electric vehicle (EV) charging stations. They specialize in collecting, enriching, and distributing high-quality, real-time data on EV charge points. This information is crucial for navigation systems, mobility service providers, fleet operators, and EV drivers, enabling them to easily locate available and compatible charging stations, thereby supporting the transition to electric mobility. Their focus is on data accuracy, completeness, and providing a comprehensive view of the charging infrastructure worldwide.
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