Daniel Holtmann-Rice

Title: Innovations of Daniel Holtmann-Rice

Introduction

Daniel Holtmann-Rice is a prominent inventor based in New York, NY. He has made significant contributions to the field of machine learning, holding a total of six patents. His work focuses on developing advanced techniques that enhance the capabilities of kernel-based machine learning systems.

Latest Patents

One of his latest patents is titled "Structured Orthogonal Random Features for Kernel-Based Machine Learning." This invention involves generating unbiased estimators for Gaussian kernels through a new framework called Structured Orthogonal Random Features (SORF). The method utilizes a linear transformation matrix computed from pairs of orthogonal and diagonal matrices, typically employing Walsh-Hadamard matrices and Rademacher distributions. Another notable patent is the "Sparse Recovery Autoencoder," which encodes sparse datasets using a learned sensing matrix. This method includes initializing an encoding matrix, selecting sparse vectors, and updating the matrix through machine learning techniques, ultimately returning embeddings for the dataset.

Career Highlights

Daniel Holtmann-Rice is currently employed at Google Inc., where he continues to innovate in the field of machine learning. His work has been instrumental in advancing the understanding and application of kernel methods in various data-driven processes.

Collaborations

He has collaborated with notable colleagues such as Sanjiv Kumar and Xinnan Yu, contributing to a dynamic research environment that fosters innovation and creativity.

Conclusion

Daniel Holtmann-Rice's contributions to machine learning through his patents and collaborations highlight his role as a leading inventor in the field. His innovative approaches continue to shape the future of technology and data analysis.