Applications in Biology and Medicine
There is a long and fruitful history of joint development between Statistics and Biology and Medicine, with data at the core. For instance, Mendel’s fundamental laws of heredity were entirely based on statistical inference applied to data from carefully designed experiments. Most recently, the advent of novel high-throughput and high-resolution biological assays has allowed the exploration of biological processes on a genomic scale and at the resolution of single cells. Applications range from addressing fundamental science questions (e.g., how does the brain work?) to disease prevention, diagnosis, and treatment. Statistical methods are essential to make sense of the massive amounts of data generated by these biotechnologies.
Our faculty have been at the forefront of research at the interface of Statistics with Biology and Medicine, contributing statistical methods and software for genome sequencing, the study of stem cell differentiation, neuroscience, evolutionary biology, epidemiology, infectious disease modeling, clinical trials, and personalized medicine, among others. A hallmark of the Berkeley approach is our close collaboration with biologists and clinicians and our engagement throughout the data science pipeline, including the framing of questions, study design, exploratory data analysis, and the interpretation, validation, and translation of the results into domain insight.
Our faculty have played an essential role in the creation and growth of the Center for Computational Biology and comprise its largest group (10 of the Center’s 48 faculty).
Researchers
statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
causal inference in experiments and observational studies, with applications to biomedical and social sciences;
contaminated data including missing data, measurement error, and selection bias
statistics, applied statistics, data science, statistical computing, computational biology and genomics
large random combinatorial structures, random matrices, superprocesses & other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical…
high dimensional and integrative genomic data analysis, network modeling, hierarchical multi-label classification, translational bioinformatics
infectious diseases (specifically HIV), chronic disease epidemiology, environmental epidemiology, survival analysis, human rights statistics
computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
machine learning, statistical prediction, variational inference, statistical computing, optimization
evolution, molecular evolution, population genetics, human variation, human genetics, phylogenetics, applied statistics, genetics, evolutionary processes, evolutionary biology
causal inference, health services & policy analysis, biostatistics, discrete optimization
computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics
computational biology, machine learning, applied statistics, applied probability
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition
Stochastic Processes, Hierarchical Bayesian Inference, Random Graph Theory, Multi-Agent Training, Computational Biology, Solution Continuation, Optimization
high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics
statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies
statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and text summarization