Center for Environmental Statistics

The Center for Environmental Statistics (CES) analyzes and models data sets describing traffic counts, trip generation, urban economics, seismicity, water supply, water quality, weather, and air quality of locations mostly in Southern California. The emphasis will be on studying spatial and temporal variation in the various indicators, and in impact studies of future developments.
Center for Image and Vision Sciences
Our research interest is to pursue a general unified computational theory underlying visual perception and learning, and to build highly intelligent computer systems which understand real world imagery and interact with people and the real environment.
Center for Statistical Computing
The modern advent of enormous repositories of digital information presents us with interesting new challenges. How can we represent and interpret such complex data? What are the best algorithms and computing strategies to address important scientific and social questions?
Center for the Teaching of Statistics
The Center for the Teaching of Statistics seeks to provide a model for Statistics education in the Southern California region by integrating research in Statistics and Pedagogy with technological innovations. We intend to serve as a resource for not just UCLA but the Southern California statistics community and, to the extent possible, to the Statistics community in general. We have formed some collaborative partnerships with AP Statistics educators, and plan to form future partnerships with educators in K-12, community colleges, and local colleges and universities. We will grow as resources and interest permits, but are already engaged in a number of activities concerning introductory Statistics teaching, AP Statistics, and technology in the classroom.
Laboratory of Statistical Genomics
Sequences of entire genomes, genotypes of individual variations in thousand of polymorphic loci and hundreds of individuals, gene expression measurements via cDNA chips on thousand of genes in a variety of conditions: these are some of the types of datasets are now available to genetic researchers. And they are examples of what are the challenges coming from genetics to the information sciences. The statistical genetics laboratory use tools from information theory, Bayesian statistics, Markov chain Monte Carlo to identify in these massive datasets scientifically valuable information.
Studio of Bio-data Refining and Dimension Reduction
The post-genome era has arrived with a torrent of high throughput genomic and proteomic data, useful for dissecting the complex genetic circuitry within cells of an organism. The goal of biodata-refining is to process such data in a way like a refinery processes crude oil. With an array of analysis tools, many of them yet to be invented, we hope to distil information of various kind to meet diverse needs such as pathway studies, disease gene searching and pharmacogenomic research. Our lab currently focuses on microarray gene expression data analysis. The aim is to build an integrated system for exploring multiple public-accessible gene expression databases. This system is based on the newly introduced concept of liquid association (LA). It also employs clustering and other statistical dimension reduction techniques to enhance the analysis. The system will integrate data from protein complex, transcription factor binding, genetic markers, drug sensitivity profiling and worldwide genomic knowledgebases to distil biological information from microarray data.