|Mar. 11, 2022, 10:00 am (Taipei Time)
|Andrew J. Holbrook
|A quantum parallel Markov chain Monte Carlo
|Dr. Andrew J. Holbrook is Assistant Professor of Biostatistics at the UCLA Fielding School of Public Health and has research interests in computational statistics, Bayesian hierarchical modeling, viral epidemiology and brain imaging. Andrew received his Ph.D. in Statistics from UC Irvine in 2018, where he completed his dissertation, Geometric Bayes, an investigation into the intersections of differential geometry and applied Bayesian inference. For this work, Andrew won honorable mention for the 2019 Leonard J. Savage Award in Theory and Methods, awarded by the International Society for Bayesian Analysis.
After graduating, Andrew became a Postdoctoral Scholar at UCLA working with Prof. Marc A. Suchard, a renowned biostatistician and expert in statistical epidemiology. In 2020, Andrew joined the UCLA Department of Biostatistics and received an NIH (K) Career Development Award for developing high-performance computing methods to model the global spread of viruses in a Big Data context. His recent papers Massive parallelization boosts big Bayesian multidimensional scaling, Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data and From viral evolution to spatial contagion: a biologically modulated Hawkes model demonstrate Andrew’s extensive experience building large hierarchical models for complex data and developing high performance computing strategies to help scientists learn from such models.