Instructor: Hua Zhou (University of California, Los Angeles)

AUGUST 18, 8:30—Noon

ROOM: LInC 268

Julia is a new open source programming language for technical computing. Its flexible design offers greater speed and power than the R+Python combination without radical change. Are statisticians and data scientists ready for Julia and is Julia ready for them? This short course illustrates the basic language features, numerical linear algebra, statistical functions (JuliaStat), optimization (JuliaOpt), and parallel and distributed computing in Julia using a variety of statistical applications.

Course Outline

• Getting started with Julia
• Comparison of Julia to R and Matlab
• Dynamic document using IJulia
• Statistical computing environment (JuliaStat)
• Numerical linear algebra
• Optimization
• Parallel computing
• Distributed computing

About Hua Zhou

Dr. Hua Zhou is associate professor in biostatistics at University of California, Los Angeles (UCLA). He specializes in numerical optimization, high-performance statistical computing, stochastic modeling, statistical genetics, and neuroimaging. His work on optimization has focused on design of efficient algorithms for large-scale problems, developing methods for accelerating them, and global optimization. His recent work on statistical genetics includes efficient methods for large-scale association studies. He also studies theoretical properties of certain Markov chains and applied them to the study of in vivo HIV dynamics and target cancer stem cell therapies.