MACSYS & MIG Workshop: Introduction to Julia

Lead instructor: Dr. Chris Rackauckas

Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and received a top presentation award at every ACoP from 2019-2021 for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.

The Julia programming language has been designed from the ground up for high-performance scientific computing. In this hands-on workshop we will explore how to use Julia and the SciML ecosystem to build performant scientific models that can be deployed on high-performance computing systems. We will start with the basics of the Julia language, covering its syntax, data structures, and key features that make it suitable for scientific computing. Then we will dive into the SciML ecosystem, exploring packages such as DifferentialEquations.jl for solving differential equations, Catalyst.jl / JumpProcesses.jl for chemical kinetics (Gillespie simulations), and hybrid models which combine ODEs/PDEs with stochastic models (SDEs, jump diffusions, etc.). We will also cover methods for scaling PDE simulations to large-scale stiff systems, using implicit-explicit solvers (IMEX) and preconditioning to ensure efficient computation. Additionally, we will explore how to solve inverse problems (parameter estimation) and integrate machine learning via adjoint automatic differentiation in order to give stable and performant fitting of models to data.

Please note, $25 booking fee is fully refunded upon workshop attendance.

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To secure your spot in a workshop, we require a nominal fee, which will be fully refunded upon your attendance at the workshop. This policy helps ensure commitment and allows us to manage resources effectively.

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