Applications in Biology and Health Sciences
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.
Berkeley faculty and students 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. We collaborate with biologistics and clinicians, in particular taking advantage of close connections across the bay at UCSF. A hallmark of the Berkeley approach is 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 also played an essential role in the creation and growth of the Center for Computational Biology and comprise its largest group.
Researchers
statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
causal inference, econometrics, experimental design, measurement error, missing data, natural experiments, applications in biomedical and social sciences
high-dimensional statistical learning, statistical computing, computational biology and genomics, precision medicine and health
large random combinatorial structures and probabilistic combinatorics, superprocesses and other measure-valued processes, probability on algebraic structures, applications of stochastic processes to biodemography, mathematical finance, population genetics, phylogenetics…
high-dimensional and integrative genomic data analysis, network modeling, hierarchical 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, sequential inference, causal inference, physical sciences, biology, control and reinforcement learning
ecological modeling, evolutionary biology and phylogenetics, genomics and genetics, population genetics, precision medicine and health
causal inference, public health, health services, biostatistics, discrete optimization
high-dimensional statistics, computational biology, bioinformatics, data analysis
AI and machine learning, applied probability, computational biology, computational genomics, evolutionary biology, human genetics
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, elections, geophysics, astrophysics, cosmology, litigation, health
Bayesian inference, inverse problems, stochastic processes, biological systems, empirical game theory, nonequilibrium thermodynamics, optimization, and computational topology
high-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, optimization, numerical methods, probabilistic forecasting, computational epidemiology
computational biology and genomics, censored data and survival analysis, semiparametric models, causal inference, multiple testing
veridical data science (PCS), trustworthy AI, interpretability, deep learning theory, tree-based methods, interdisciplinary research in medical AI, computational biology and genomics, neuroscience