Cambridge, United Kingdom

Jose Miguel Hernández Lobato

USPTO Granted Patents = 1 

Average Co-Inventor Count = 9.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: **Jose Miguel Hernández Lobato: Innovator in Machine Learning Models**

Introduction

Jose Miguel Hernández Lobato, an accomplished inventor based in Cambridge, GB, has made significant strides in the field of machine learning. With a keen focus on enhancing predictive capabilities within artificial intelligence, he stands out as a notable figure in technology innovation.

Latest Patents

Hernández Lobato holds a patent for an "Auxiliary model for predicting new model parameters." This invention describes a computer-implemented method that trains an auxiliary machine learning model to predict a set of new parameters for a primary machine learning model. The primary model is designed to transform an observed subset of real-world features into a predicted version of those same features, thereby advancing the efficiency and accuracy of machine learning applications.

Career Highlights

Jose Miguel is currently associated with Microsoft Technology Licensing, LLC, where he contributes to groundbreaking projects that push the boundaries of machine learning technology. His innovative approach and technical expertise have established him as a valuable asset to the company and the broader tech community.

Collaborations

Throughout his career, Hernández Lobato has worked alongside esteemed colleagues, including Cheng Zhang and Angus Lamb. Their collaboration fosters an environment of shared knowledge and creative problem-solving, further enhancing the potential impact of their collective work within the realm of artificial intelligence.

Conclusion

In summary, Jose Miguel Hernández Lobato exemplifies the innovative spirit prevalent in the tech industry today. His pioneering patent and ongoing collaborations illustrate the importance of research and development in machine learning. As he continues to push technological boundaries, his contributions are likely to influence the future of predictive modeling and artificial intelligence.

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