Friday, March 7th
12:00pm - 1:00pm, ARB 627 or Zoom
Zoom ID: 957 7525 9475
Passcode: 834911
Assistant Professor of Industrial Engineering and Operations Research
Columbia University
Conditional Diffusion Models for Missing Data Imputation: Part II
Abstract:
Diffusion models have emerged as a powerful generative framework, achieving state-of-the-art performance across diverse applications. They have shown remarkable potential for missing data imputation, offering a flexible and effective solution to this fundamental challenge. In this talk, Professor Wang will begin with an overview of diffusion models for unconditional data generation, explaining the core concepts of the forward (noise-adding) and reverse (denoising) processes. Building on this foundation, he will then introduce conditional diffusion models tailored for missing data imputation, covering both continuous and discrete data scenarios. Key topics include modeling techniques, conditioning mechanisms, and training strategies.
Why Diffusion Models?
As data grows increasingly complex, diffusion models offer a cutting-edge approach to handling missing entries, generating realistic synthetic data, and improving the robustness of predictive tasks. These methods stand at the forefront of generative AI techniques, making them a valuable tool for both methodological research and practical applications.
Speaker Bio:
Kaizheng Wang is an assistant professor of Industrial Engineering and Operations Research and a member of the Data Science Institute at Columbia University. He works at the intersection of statistics, machine learning, and optimization.
About TRAIL4Health & the Brown Bag Learning Series
TRAIL4Health is a Translational AI Laboratory committed to advancing public health through innovative applications of artificial intelligence and data science. At TRAIL4Health, we host a variety of events to foster learning, collaboration, and the exchange of ideas at the intersection of AI, public health, and medicine.
The TRAIL Brown Bag Learning Series is a weekly informal gathering held on Fridays at noon in Room 627 of the Allen Rosenfield Building. These sessions are an opportunity for faculty, students, and researchers to come together and learn something new—whether it be a dataset, software, new article, or simply to exchange ideas. This informal setting provides a platform for sharing knowledge, stimulating discussions, and fostering collaboration in a relaxed environment.