Sameer Wagh

Title: Sameer Wagh: Innovator in Private Deep Neural Networks

Introduction

Sameer Wagh is a prominent inventor based in Princeton, NJ (US). He has made significant contributions to the field of deep learning and privacy in computing. With a total of 2 patents, his work focuses on enhancing privacy and efficiency in neural network training.

Latest Patents

Sameer Wagh's latest patents include innovative systems and methods for private deep neural network training. This method involves storing private values across multiple machines and computing values based on shared data, ensuring privacy during the training process. Another notable patent is his work on tunable oblivious RAM, which allows users to adjust the tradeoff between privacy and computational resources. This approach provides a unique solution that balances privacy needs with efficiency, a feature not commonly found in other implementations.

Career Highlights

Throughout his career, Sameer has worked with esteemed organizations such as Princeton University and Microsoft Technology Licensing, LLC. His experience in these institutions has allowed him to develop cutting-edge technologies that address critical challenges in data privacy and machine learning.

Collaborations

Sameer has collaborated with notable individuals in his field, including Paul Cuff and Prateek Mittal. These partnerships have contributed to the advancement of his research and the successful development of his patents.

Conclusion

Sameer Wagh is a distinguished inventor whose work in private deep neural networks and tunable oblivious RAM has made a significant impact on the field of computing. His innovative approaches continue to shape the future of privacy in technology.