Eric Kolaczyk
PIPP Phase I: Predicting and Preventing Epidemic to Pandemic Transitions
The COVID-19 pandemic and its effects, both in terms of the millions of lives lost and the trillions in estimated costs, are a recent example of the devastation pandemics can cause. Any discernible progress in the prediction, early detection, and rapid response would have significant impacts on human welfare. The overarching goal of this project […]
Understanding Social Dynamics Through Coevolving Latent Space Networks With Attractors
This research project will develop a general class of coevolving network models. In social systems, interactions frequently influence individual behavior and beliefs which can, in turn, impact interactions. Specific variants of this type of coevolutionary phenomenon include opinion dynamics, voter behavior, observational learning, herding or flocking, and polarization. Network-based models are natural for representing such […]
Edge Differentially Private Estimation in the β-model via Jittering and Method of Moments
A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off for parameter estimation in the β-model (Chatterjee, Diaconis and Sly, 2011) for edge differentially private network data released via jittering (Karwa, Krivitsky and Slavkovi´c, 2017). Unlike […]
CRCNS: Dynamic network analysis of human seizures for therapeutic intervention
Epilepsy is one of the most common neurological syndromes, affecting an estimated 3 million people in the United States. In one-third of these patients, seizures cannot be controlled despite maximal medication management. The complexity of the neuronal network dynamics that define the epileptogenic cortex and drive seizure initiation and spread makes understanding and treating epilepsy […]