Novel Statistical and Machine Learning Methods for Single Cell Data Analysis

Explore novel statistical and machine learning methods for single cell data analysis in this comprehensive lecture by Saurabh Sinha. Delve into cutting-edge techniques designed to extract meaningful insights from complex single-cell datasets. Learn about advanced algorithms and computational approaches that address the unique challenges posed by high-dimensional, sparse single-cell data. Discover how these innovative methods can be applied to uncover cellular heterogeneity, identify rare cell populations, and elucidate developmental trajectories. Gain valuable knowledge on integrating multiple data modalities and handling technical noise in single-cell experiments. This talk, part of the "Machine Learning for Health and Disease" program, bridges the gap between computational expertise and clinical applications, offering insights for both machine learning specialists and healthcare professionals interested in leveraging these powerful tools for biomedical research and personalized medicine.