12 People Results for the Tag: Markov Chain

All A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

David Hughes

Associate Professor of Entomology and Biology
Parasite manipulation of host behavior

Center for Infectious Disease Dynamics

Dezhe Jin

Associate Professor of Physics
Computational models of neural basis of motor control and learning; theoretical analysis of biological neural networks.

Tanya Renner

Assistant Professor of Entomology
Evolution of chemical and structural defense. Molecular evolution, evolutionary genomics, and transcriptomics. Origins and evolution of carnivorous plants.

David R Hunter

Professor of Statistics, Department Head
I work on statistical models for networks, mixture models, and certain optimization algorithms called MM algorithms. A full list of papers and related work, such as software, may be found at http://sites.stat.psu.edu/~dhunter/.

Center for Infectious Disease Dynamics

Ephraim M. Hanks

Associate Professor of Statistics
Spatio-Temporal Statistics. Bayesian Hierarchical Modeling. Optimal Sampling. Animal Movement. Landscape Genetics.

Center for Infectious Disease Dynamics

Murali Haran

Associate Professor of Statistics
Statistical computing (Markov chain Monte Carlo algorithms); spatial models (Gaussian random field models); methods for complex computer models; interdisciplinary collaborations in environmental sciences, climate science, disease modeling, ecology

Center for Infectious Disease Dynamics

Alberto Bressan

Advisor for the Center for Mathematics of Living and Mimetic Matter

Tom Baker

Distinguished Professor of Entomology and Chemical Ecology

David Kennedy

Assistant Professor of Biology

Center for Infectious Disease Dynamics

Duane Diefenbach

Adjunct Asst. Professor of Wildlife Ecology; Leader, PA Coop. Fish and Wildlife Research Unit
Wildlife ecology, estimation of population parameters, and harvest management of game populations.

David Koslicki

Associate Professor of Computer Science and Engineering
Developing efficient algorithms to extract insight from high-throughput sequencing data.