Location History:
- Seattle, WA (US) (2010 - 2011)
- San Francisco, CA (US) (2010 - 2020)
- Sunnyvale, CA (US) (2022 - 2023)
Company Filing History:
Years Active: 2010-2023
Title: Kunal Talwar: Innovator in Differentially Private Machine Learning
Introduction
Kunal Talwar is a prominent inventor based in San Francisco, CA, known for his significant contributions to the field of machine learning and privacy. With a total of 16 patents to his name, he has made remarkable strides in developing systems that enhance user privacy while leveraging machine learning technologies.
Latest Patents
One of Kunal's latest patents focuses on training user-level differentially private machine-learned models. This innovation provides systems and methods for learning differentially private machine-learned models. The computing system includes one or more server computing devices that comprise processors and non-transitory computer-readable media. These systems store instructions that, when executed, enable the server computing devices to perform various operations. These operations include selecting a subset of client computing devices from a pool, providing a machine-learned model to these devices, and receiving local updates for the model. The system then determines a differentially private aggregate of these updates and updates the machine-learned model based on a data-weighted average of the local updates.
Career Highlights
Kunal has worked with notable companies such as Microsoft Technology Licensing, LLC and Google Inc. His experience in these leading technology firms has allowed him to refine his skills and contribute to groundbreaking innovations in the tech industry.
Collaborations
Kunal has collaborated with talented individuals in his field, including Li Zhang and Kamal Jain. These partnerships have fostered an environment of creativity and innovation, leading to the development of advanced technologies.
Conclusion
Kunal Talwar's work in differentially private machine learning exemplifies the intersection of technology and user privacy. His contributions continue to shape the future of machine learning, making significant impacts in the industry.