Mark Kon

DMS/NIGMS 1: Multilevel stochastic orthogonal subspace transformations for robust machine learning with applications to biomedical data and Alzheimer’s disease subtyping

Late-onset Alzheimer’s Disease (AD) is the most common form of dementia, with an estimated 6.5 million Americans aged 65 and older living with AD today – this number will double by 2050. AD occurs in more than 35% of individuals over the age of 85 and is the fifth leading cause of death among Americans […]

Stochastic Dynamic Modeling of Cellular Protein Interactions

Stochastic methods for modeling molecular-protein interactions form an entirely new set of ap-proaches to the important biological goal of simulating cellular biology in silico. Though great progress has been made in this direction by computational biologists over the past 15 years, the goal of “siliconizing” cellular molecular interactions still remains remote. More precisely, the current […]

AMPS: Uncertainty Quantification for Stochastic Analysis of Electrical Power Networks

The reliable functioning of the electric power grid forms a critical part of a modern industrial country. The failure to maintain the reliability of the grid leads to significant societal, national security problems and environmental costs. With the ever-increasing use of intermittent power sources such as wind, solar, battery, along with new types of disruptions […]