40 citations · 69 across the 8 of their papers we have counts for
8 papers
Scaling Hawkes Processes
Seyoon Ko, Jasen Zhang, Andrew J. Holbrook
Hawkes processes (HP) are a large class of stochastic point process models scientists have used to analyze contagion phenomena ranging from earthquakes, infectious diseases and bio…
Scaling Hawkes processes to one million COVID-19 cases
Seyoon Ko, Marc A. Suchard, Andrew J. Holbrook
Hawkes stochastic point process models have emerged as valuable statistical tools for analyzing viral contagion. The spatiotemporal Hawkes process characterizes the speeds at which…
DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia
Seyoon Ko, Hua Zhou, Jin Zhou +1
The demand for high-performance computing (HPC) is ever-increasing for everyday statistical computing purposes. The downside is that we need to write specialized code for each HPC…
High-Performance Statistical Computing in the Computing Environments of the 2020s
Seyoon Ko, Hua Zhou, Jin J. Zhou +1
Technological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever. We review these advances from a stati…
OPENMENDEL: A Cooperative Programming Project for Statistical Genetics
Hua Zhou, Janet S. Sinsheimer, Christopher A. German +12
Statistical methods for genomewide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed a…
Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET
Ernest K. Ryu, Seyoon Ko, Joong-Ho Won
Many imaging problems, such as total variation reconstruction of X-ray computed tomography (CT) and positron-emission tomography (PET), are solved via a convex optimization problem…
Easily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration
Seyoon Ko, Donghyeon Yu, Joong-Ho Won
Many statistical learning problems can be posed as minimization of a sum of two convex functions, one typically a composition of non-smooth and linear functions. Examples include r…
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Evan Racah, Seyoon Ko, Peter Sadowski +5
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientis…