Machine Learning Seminar: "Surgical Machine Unlearning: Decoupling Targets, Diagnosing Fragility, and Latent Intervention."

Title: Surgical Machine Unlearning: Decoupling Targets, Diagnosing Fragility, and Latent Intervention Abstract: Machine unlearning seeks to remove specific data or behaviors from trained models to meet privacy, safety, and regulatory demands.

However, existing methods often treat unlearning as a blunt data-removal problem, overlooking how knowledge is structured inside modern models. In this talk, I will present a representation-aware view of unlearning, showing that what a model forgets—and what it unintentionally loses—depends on target definitions, evaluation protocols, and internal model structure.

By analyzing unlearning in classical deep learning tasks and large language models, I illustrate why forgetting is inherently non-uniform and how lightweight, targeted interventions can enable more precise and reliable unlearning.

Bio: Jianing Zhu is a Postdoctoral Fellow at the University of Texas at Austin (https://zfancy.github.io/), advised by Professor Atlas Wang. He received his PhD at Hong Kong Baptist University and was a visiting researcher at CMU and RIKEN AIP.

His research interests lie in trustworthy machine learning to advance model robustness, reliability, and transparency in modern AI systems, with a recent focus on machine unlearning and agentic memory.