Events Calendar

PhD Thesis Defense - Tiantian Feng

PhD Thesis Defense - Tiantian Feng 
 
Committee Members: Professor Shrikanth Narayanan, Professor Aiichiro Nakano, Professor Kristina Lerman, and Professor Morteza Dehghani (external)  
 
Title: Foundation Model Assisted Privacy-Enhancing Computing in Human-centered Machine Intelligence  
 
Abstract: Human-centered machine intelligence has revolutionized many leading domains, ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other. These systems utilize state-of-the-art machine learning (ML) algorithms to achieve a deeper understanding of human conditions, such as state, trait, and interactions, which provide possibilities to create technologies that increasingly support and enhance human experiences. Despite promises human-centric ML systems deliver, they create critical risks in potentially leaking sensitive information that people might want to keep private. The sensitive information can be individual attributes (e.g., age, gender), states (e.g., health, emotions), or biometric fingerprints. In this thesis, I explore privacy-enhancing computation associated with human-centered ML. My thesis investigates established approaches to preserve privacy in diverse human-centered applications. However, we identify that these approaches are frequently ineffective when encountering low-resource data due to privacy restrictions in sensing, storing, and using such data. Concurrently, the foundation model is a rapidly evolving research field, leading to the success of modern generative AI capable of creating realistic and high-fidelity digital content. These advances in foundation models and generative AI also present opportunities for privacy-enhancing computing as high-quality generated content can serve as training data. This leads us to explore using the foundation model to generate training data to assist low-resource training encountered with sensitive data in human-centered applications. Our extensive experiments demonstrate the potential of the foundation model in assisting low-resource training caused by privacy constraints in obtaining human-centered signals.

Location: Ronald Tutor Hall of Engineering (RTH) - 320

Audiences: Everyone Is Invited

Contact: CS Events