Title: Structured Knowledge as a Lens for Trustworthy AI: Privacy, Unlearning, and Safety

Date: Wednesday, November 18, 2026

Time: 10:00 AM – 12:00 PM (ET)

Location: Virtual via Microsoft Teams (link below). An in-person viewing room is also available at Coda C1215 Midtown (12th floor, Coda Building).

Meeting link: https://teams.microsoft.com/meet/2781943465376823?p=4kQ6wkrTY2G5qNmJSg 

Meeting ID: 278 194 346 537 682 3

Passcode: ma9KF2We

 

Rongzhe Wei

Machine Learning PhD Candidate

School of Electrical and Computer Engineering

Georgia Institute of Technology

 

Committee

1. Dr. Pan Li (Advisor), Electrical and Computer Engineering, Georgia Institute of Technology

2. Dr. Justin Romberg, Electrical and Computer Engineering, Georgia Institute of Technology

3. Dr. Juba Ziani, Industrial and Systems Engineering, Georgia Institute of Technology

4. Dr. Duen Horng (Polo) Chau, Computational Science and Engineering, Georgia Institute of Technology

5. Dr. Pin-Yu Chen, IBM Research

 

Abstract

Privacy, unlearning, and safety are often evaluated at the level of individual records, facts, or requests. This dissertation uses structured knowledge as a lens to examine how relationships among these units shape what AI systems reveal, retain, and enable. For privacy, it develops graph diffusion mechanisms and distribution-dependent analyses that account for data structure. For unlearning, it reveals overlooked privacy risks for minority data and shows how seemingly forgotten facts remain recoverable through related knowledge, motivating evaluations and methods that address these dependencies. For safety, it demonstrates how restricted knowledge can be assembled across seemingly benign queries and develops a defender that learns when to intervene as information accumulates. Together, these contributions show how accounting for relationships among data, knowledge, and interactions leads to more faithful evaluations and more effective methods for trustworthy AI.