Adjunct Faculty

The petitioner proposes to develop and analyze statistical and machine learning models to enhance privacy and accuracy in data-driven decision-making. Their work focuses on optimizing data mining techniques for applications in fraud detection, fair lending, and electronic health record privacy. The endeavor aims to create safer digital environments through innovations in differential privacy and cybersecurity.

The petitioner proposes to develop privacy-preserving technologies designed to compute data without compromising individual privacy. This work focuses on securing online transactions and protecting against identity theft or unauthorized access to personal information in an increasingly digitalized environment.

The petitioner proposes to develop state-of-the-art technological solutions for next-generation AI and deep learning algorithms, focusing on privacy preservation and cyber-threat detection architectures. This work aims to fortify digital defenses against cyberattacks and secure sensitive health data to reduce the overall healthcare burden in the U.S.

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.