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Thursday, Feb 29th | 10:00 AM ET (US)

Build Data-Centric Web Applications in Julia with Genie Builder

Calling all data scientists and R&D engineers! Join us for an exploration of Genie Builder 1.0, the ultimate low-code, drag & drop tool for building data apps in Julia.

Genie Builder streamlines the development of data-centric web applications around your Julia code, such as interactive dashboards, AI & simulation apps. With its drag & drop UI builder and low-code backend in pure Julia, Genie Builder helps you create web apps quickly - with no web development skills required. Powered by Genie, Julia’s premier Web framework, Genie Builder is both a user-friendly tool for rapid dashboard and prototype development and a reliable instrument for building and scaling production applications.

What You'll Learn:

  • How to build a data app with Genie Builder
  • How to deploy and share your Genie app on JuliaHub
  • Interactive Q&A Session with the Genie and JuliaHub teams to provide you with advice for your specific use cases
Register now!



Meet Your Speakers

Pere Giménez

Pere Giménez

Data Science Advocate at Genie
As a PhD in Electrical Engineering with a focus on machine learning and signal processing, Pere Giménez brings extensive experience in the development of scientific applications. His research deals with kernel methods, optimization, and power systems. Pere is also skilled in Julia open-source development, cloud deployments, and technical writing. In his current role as a Data Scientist & Developer Advocate at Genie, Pere is actively promoting the Web framework's adoption and assisting researchers and data scientists in transitioning their applications from theory to production.
Adrian Salceanu

Adrian Salceanu

Co-Founder at Genie, Creator of the Genie Framework
Adrian Salceanu is the technical co-founder of Genie, and the creator and lead maintainer of the open source Genie Framework. Adrian has over two decades of professional work experience as a Web developer and software architect, leading agile teams in developing, scaling, and maintaining business critical, data-intensive Web applications. Adrian is the author of Julia Programming Projects (published by Packt in 2018) and Web Development with Julia and Genie (published by Packt in 2022) and an enthusiastic JuliaLang open-source contributor. He has two master's degrees in Advanced Computer Sciences and Cybersecurity.

Model Discovery

Combine models with tools like DiffEqFlux and NeuralPDE to discover missing physics and generate digital twins.

Combine with Pre-Built Models and Digital Twins

Grab complete models from the JuliaSim Model Store and compose the pieces to accelerate the design process.

Specialized Numerical Environments

Use the latest numerical tools, like discontinuity-aware differential equation solvers, high-performance steady state solvers, and domain-specific environments.

Blending classical physical modeling with modern Scientific Machine Learning techniques.


JuliaSim is a next generation cloud-based simulation platform, combining the latest techniques in Scientific Machine Learning with equation-based digital twin modeling and simulation. Our modern ML-based techniques accelerate simulation by up to 500x, changing the paradigm of what is possible with computational design. The premise of the software is to facilitate the design and accelerate challenging real-life models of considerable complexity.


JuliaSim allows the user to import models directly from the Model Store (more information below) into the Julia environment, making it easy to build large complex simulations. The user-friendly GUI facilitates the process and makes simulation more accessible to a wider audience.


JuliaSim produces surrogates of blackbox (and regular) dynamical systems using Continuous Time Echo State Networks (CTESNs). This novel technique allows, amongst other features, for implicit training in parameter space to stabilize the ill-conditioning present in stiff systems.

Learn more about the JuliaSim Ecosystem

Julia Computing delivers JuliaSim as an answer to accelerating simulations through digital-twin (or surrogate) modeling. By blending classical, physical modeling with advanced scientific machine learning (SciML) techniques, JuliaSim provides a next-generation platform for building, accelerating, and analyzing models.