"Sasi Kumar Kolla is a Healthcare AI & Machine Learning Systems Engineer with a distinguished research trajectory spanning 2021 to 2026. His expertise lies at the intersection of clinical data platforms, deep learning, and AI-driven analytics, with a focus on transforming healthcare through intelligent systems. Over the years, Sasi has evolved from architecting large-scale EHR data platforms and secure clinical data exchange models to pioneering cutting-edge research in federated learning, generative AI for rare disease research, and graph neural networks for drug interaction prediction. His most recent work encompasses large language models for EHR knowledge extraction, foundation models for precision medicine, and autonomous clinical monitoring using reinforcement learning reflecting his commitment to advancing equitable, explainable, and real-time AI solutions in medicine. Sasi stands at the forefront of healthcare AI, bridging engineering rigor with clinical impact to shape the future of intelligent, data-driven patient care."

Healthcare Data Engineering AbstractGenerative AI Models for Synthetic Healthcare Data Augmentation in Rare Disease Research "Shallow Learning agents require abundant labelled data to excel. In rare diseases, where patient population is often limited to a few hundred patients, such a data source is not readily available. Generative AI models can synthesize new, similar data by learning an approximate distribution of the original data. Their application in rare disease cohorts and clinical data is detailed, with supervisory signals derived from the data labelling mechanisms. The response from Generative AI models is further validated by comparing model predictions with real-world outcomes. Rare diseases are conditions that affect a small percentage of the population — definitions suggest a prevalence of below 1:2000 but this varies among nations. Globally, a collection of more than 7000 rare diseases, affecting 25 million patients, has been enumerated, with the rapid emergence of new entities identified. Early diagnosis, clinical management, and developing novel therapeutics for rare diseases offer challenging problems with limited labelled data-sets available to Deep Learning agents. Synthetic Data Generation, by augmentation or providing an alternative data source, enables Shallow Learning agents to attempt these tasks across phenotype, genotype, and narrative data."