Research Assistant

The petitioner proposes to continue developing privacy-preserving technologies for machine learning and cloud-based applications, specifically focusing on federated learning, differential privacy, and data anonymization. His work aims to build scalable, privacy-compliant AI systems that allow for secure data handling without compromising performance in sectors like healthcare and finance. This includes the development of privacy-conscious attribution models for advertising and secure natural language processing applications.

The petitioner proposes to advance the robustness, safety, and data efficiency of machine learning models when used outside their training conditions. This work focuses on enabling the reliable deployment of AI systems in real-world applications across critical domains such as healthcare, transportation, finance, and national security.

The petitioner proposes to continue developing robust, fast, computationally efficient, and privacy-protected algorithms to solve large-scale problems in machine learning and data analysis. This work specifically focuses on processing data applied to the social media industry to improve functionality and services for American users.

The petitioner proposes to advance machine learning-based techniques to improve consumer data privacy and enhance the efficacy of predictive machine-learning models within the pharmaceutical industry. This work focuses on strengthening AI security protocols and ensuring the safe, secure, and trustworthy application of AI in healthcare settings.