Our ‘Unlearning’ Research for GenAI Featured in The Daily Texan Post

Excited to see our collaborative research with JPMorgan Chase on an AI unlearning algorithm spotlighted in The Daily Texan! Check out the article at OpenReview.


Key Points

  • Unlearning for AI: We have created an algorithm that allows generative AI models to “unlearn” information. This is especially important for trustworthy and reliable GenAI models.
  • Why it’s significant: This is a novel direction in AI research, which focuses on teaching AI systems to forget. This unlearning approach helps ensure AI doesn’t store potentially harmful or unnecessary data.
  • How it works: The algorithm teaches the AI model to forget unwanted details, allowing for alterations to images while still maintaining the image’s core identity.
  • Potential applications: This technology could be used to:
    • Remove sensitive information from images before sharing them.
    • Protect privacy
    • Combat the spread of misinformation or deepfakes
    • Avoid copyright infringement


Viral outbreaks spread throughout networks of people via transmission events. We aim to combine human mobility data, network science, and machine learning to inform and mitigate the disease dynamics for COVID-19. Furthermore, we aim to build an always-on social sensing system to improve a population’s resilience to a novel virus.

social networks

Social Networks

Objective social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. We are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. We aim to uncover new insights on understanding and engineering social media dynamics and their consequences on offline behaviors.

biological networks

Biological Networks

It is well established that bacteria engage in social behavior and form networked communities via molecular signaling. We analyze the network dynamics and biofilm metrics, showing that our method can effectively reveal the underlying intercellular communication process and community organization within the biofilm. We claim that the application of social and network sciences to understanding bacteria population dynamics can aid in developing better drugs to control the many pathogenic bacteria that use social interactions to cause infections.