Manjunath Mahendra

Integrative Machine Learning for Enhanced Clinical and Biomedical Data Science: Addressing Diversity and Scarcity Challenges

My research revolves around advancing the field of clinical and biomedical data science, specifically focusing on overcoming challenges related to the generation of tabular clinical data. I'm particularly interested in the significant role synthetic data generation plays in addressing prevalent issues in clinical data science, such as data scarcity, skewness, and privacy concerns. I am dedicated to progressing in this field by developing, benchmarking, and deploying state-of-the-art algorithms tailored for task-specific synthetic sample generation. My primary goal is to create synthetic tabular data as a viable alternative to real data, facilitating the implementation of innovative approaches like convex space learning using deep learning.