Dante Laudisa is a distinguished Staff Software Engineer at Google DeepMind, based in London. With over a decade of experience in the field of Artificial Intelligence and Machine Learning, he has become a prominent figure in the development and application of cutting-edge AI technologies. Dante's core expertise lies in Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP). He has made significant contributions to some of Google's most ambitious AI projects, including the Gemini family of models, where he focuses on model architecture, training efficiency, and robust evaluation. Dante is deeply passionate about responsible AI development, emphasizing safety, ethics, and the scalable deployment of AI systems to solve real-world problems. He holds a PhD in Computer Science from Imperial College London, where his research laid much of the groundwork for his current contributions to the AI landscape. He is known for his ability to bridge the gap between complex theoretical research and practical, impactful applications.
Dante Laudisa's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Played a pivotal role in the research, development, and scaling of Google's next-generation multimodal AI models, Gemini, contributing to its core capabilities, training methodologies, and safety protocols.
Successfully completed doctoral research at Imperial College London, one of the world's leading universities, focusing on advanced machine learning techniques and their applications, which has significantly informed his work in AI.
Actively contributes to research and implementation of state-of-the-art techniques for ensuring the safety, fairness, and ethical considerations in the deployment of large-scale AI models at Google.
Frequently shares insights and research findings through publications, technical talks, and articles, contributing to the broader AI community's understanding of LLMs and generative AI. (Note: Specific publications can be found on Google Scholar or his Google Research page).
Contributed to developing and implementing novel techniques for more efficient training and inference of large-scale neural networks, enabling the creation of more powerful and accessible AI models.
ITC "P. Verri" Milano - Year 1974
Università Cattolica del Sacro Cuore
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