The Center for Complexity and Self-management of Chronic Disease (CSCD) CSCD Colloquium Series welcomes Dr. Saeid Amiri, University of Nebraska. Dr. Amiri is an expert on machine learning, clustering methods and statistical genetics. Working with CSCD investigators, Dr. Amiri is developing a new foundation for modeling, analysis and interpretation of complex, high-dimensional and incongruent Big Data.
Presentation details: Extraction of valuable information from Big data (n>>p) in high dimensions (p>>n) and the subsequent scientific inference using such derived information present considerable challenges in many medical, biological, social and data-driven sciences.

Dr. Amiri will:
1) present statistical learning and unsupervised machine learning techniques for the low dimension data and discuss a new sub-space alternative approach.
2) illustrate an extension method for higher-dimensions and big data based on random subspaces.
3) provide a series of arguments to justify the new technique and will provide examples involving real and simulated data to compare our method with other related techniques.​

This event is co-sponsored by the UMSN Fogarty International Training Program for Strengthening Non-Communicable Disease Research and Training Capacity in Thailand