Dear Julia users in the Bay area, I am glad to announce a meetup session in Berkeley, California, USA on Feb 21. There are three invited speakers talking about their experiences on using Julia for optimization, statistics, parallel computing and quantum science applications. People from SQuInT (southwest quantum information network) workshop, developers & researchers from Stanford and universities nearby are also invited for discussions of developing related Julia packages during the free interaction session starting from 9:25pm.
Time: 7:30pm-10:00pm. Place: Room Berkeley, DoubleTree Hilton Hotel, 200 Marina Blvd. Berkeley, California 94710 USA Register: http://goo.gl/forms/T5qnGPndSE Talks: Talk 1: Predictive Analysis in Julia - An overview of the JuMP package for optimization Speaker: Philip Thomas from StaffJoy Content: This talk focuses on expressing problems including linear programming and integer programming in the JuMP metalanguage. Possibly with some introduction to general optimization problems. Talk 2: Convex.jl: Optimization for Everyone Speakers: David Deng and Karanveer, possibly also with Jenny Hong and Madeleine Udell from Stanford. Content: This talk will start with a brief overview of how the Convex.jl package works and the types of problems it can solve, and really showcase how convenient it is to use. It will be clear that Convex.jl is easily usable by just about anyone for their basic optimization needs. One or two more involved applications of using Convex.jl to solve real world problems will be demonstrated from a good pool of examples. Hopefully there will be an example on quantum tomography. Talk 3: Quantum Statistical Simulations with Julia Speaker: Katharine Hyatt from UCSB Content: Using computers to probe quantum systems is becoming more and more common in condensed matter physics research. Many of the commonly used languages and techniques in this space are either difficult to learn or not performant. Julia has allowed us to quickly develop and test codes for a variety of commonly used algorithms, including exact diagonalization and quantum Monte Carlo. Its parallel features, including some MPI and GPGPU integration, make it particularly attractive for many quantum simulation problems. I'll discuss what features of Julia have been most useful for us when working on these simulations and the developments we're most excited about. More details can be found here: https://github.com/JuliaQuantum/JuliaQuantum.github.io/issues/15 Feel free to forward this message to anyone who might be interested. Thanks. Contact: JuliaQuantum organization via quantumjulia AT gmail DoT com
