Newsletter December 2022 - JuliaHub Newsletter

JuliaCon 2023 Call for Proposals - Deadline Extended: The JuliaCon 2023 Call for Proposals deadline has been extended until January 15. Do you have an exciting new Julia use case? Have you solved a difficult problem using Julia? Any topic that would be of interest to the Julia community is eligible. Formats include talks (30 minutes), lightning talks (10 minutes), minisymposia (2-3 hours), workshops (3 hours), posters, birds-of-a-feather/interest groups (one hour breakout sessions), experiences (3 minutes) and online only talks. Click here to learn more about proposal criteria and to submit your proposal.

XKDR Mumbai Julia Meetup with JuliaHub CEO Dr. Viral Shah: Dr. Viral Shah joins XKDR’s Ajay Shah and Ayush Patnaik for a Julia meetup at XKDR’s office in Mumbai. The meetup takes place on Tuesday, January 3 from 6-8 pm. Dr. Shah will present the Evolution of Julia, followed by Ajay Shah’s presentation on Statistics in Julia and Ayush Patnaik’s presentation on Geospatial Analysis in Julia. The event is free but you must register online to attend. Snacks and beverages will be served. Click here to register.

Bangalore Meetup with JuliaHub’s Avik Sengupta and Panagiotis Georgakopoulos: JuliaHub’s Avik Sengupta (VP of Engineering) and Panagiotis Georgakopoulos (Software Engineer) will present Pluto.jl and JavaCall.jl at JuliaHub’s Bangalore office on Tuesday, January 10 from 6-7:30 pm. The event is free but you must sign up online to attend. Snacks and beverages will be served. Click here to sign up.

Why Julia? - JuliaHub Chief Scientist Alan Edelman: MIT Professor Alan Edelman (JuliaHub Chief Scientist and co-founder, Julia co-creator) will present Why Julia? at the University of Florida Herbert Wertheim College of Engineering in Gainesville, Florida. The presentation takes place on Tuesday, January 31 from 12-1 pm at Computer Sciences and Engineering (CSE) Room 404.

Free Webinar from JuliaHub: Using Large Datasets in JuliaHub is a new free Webinar from JuliaHub presented by Dr. Matt Bauman, Director of Applications Engineering. Click here to watch.

Julia for Drones: Year of Autonomy in Alaskan Glaciers, Flight, Earth Orbit, Cislunar Space and Mars in the Aerospace America Year in Review describes how Julia is used to guide an autonomous flight vehicle. “The flight test team was able to demonstrate … a vertical takeoff and landing vehicle with both electric and conventional fuel propulsion systems onboard. The [uncrewed aerial system] was able to plan and execute these missions autonomously using onboard hardware. It was the first time the Julia programming language was flown on the embedded hardware - algorithms were precompiled ahead of time. The algorithms used to perform the various missions involved feedback control, mixed-integer linear programming and optimal control.”

Julia for Robots and Autonomous Systems: Microsoft Autonomous Systems and Robotics researchers Jayesh Gupta, Sai Vemprala and Ashish Kapoor published Learning Modular Simulations for Homogeneous Systems in which they describe using Julia and the SciML ecosystem to develop “a modular simulation framework for modeling homogeneous multibody dynamical systems, which combines ideas from graph neural networks and neural differential equations… [M]essage passing allows coordination between multiple modules over time for accurate predictions and in certain cases, enables zero-shot generalization to new system configurations. Furthermore, [these] models can be transferred to new system configurations with lower data requirement and training effort, compared to those trained from scratch.”

Julia for Quantum Computing: QuEra’s Quest - Build a Flexible Neutral Atom-Based Quantum Computer describes how QuEra uses Julia for quantum computing. “Last month, QuEra Computing began providing access to its 256-qubit, neutral atom-based quantum system, Aquila, from Amazon Braket. Founded in 2018, and built on technology developed at Harvard and MIT, QuEra, is one of a growing number of quantum companies betting on neutral atom technology. Advocates, including QuEra, say neutral atom approaches feature significant advantages over other approaches such as semiconductor-based superconducting systems, which require frigid operating temperatures and pose significant control and scaling challenges … The company has developed software, Bloqade – “Bloqade enables benchmarking and algorithmic design, incorporating the core strengths of neutral-atom arrays for quantum computing. Accounting for features such as Julia, flexible processor geometry, multi-qubit connectivity, and analog operation modes, Bloqade is an open-sourced community tool.” Bloqade can be downloaded from GitHub or run on Amazon Braket.”

Converting from Proprietary Software to Julia: Are you looking to leverage Julia’s superior speed and ease of use, but limited due to legacy software and code? JuliaHub and our partners can help accelerate replacing your existing proprietary applications, improve performance, reduce development time, augment or replace existing systems and provide an extended trusted team to deliver Julia solutions. Leverage experienced resources from JuliaHub and our partners to get your team up and running quickly. For more information, please contact us.

Careers at JuliaHub: JuliaHub is a fast-growing tech company with fully remote employees in 12 countries on 5 continents. Click here to learn more about exciting careers and internships with JuliaHub.

Julia and JuliaHub in the News

• Aerospace America Year in Review: Year of Autonomy in Alaskan Glaciers, Flight, Earth Orbit, Cislunar Space and Mars

• Nature: Changes in the Hydro-Climatic Regime of the Hunza Basin in the Upper Indus under CMIP6 Climate Change Projections

• HackerNoon: The Best Programming Languages for Working with AI

• MIT News: Ushering in a New Era of Computing

• Analytics Insight: Julia on the Upswing: Why Data Scientists Are Choosing Julia

• MarkTechPost: High-Performance Computing (HPC) And Artificial Intelligence (AI)

• Analytics Insight: Top 10 Programming Languages that Are Loved by Stack Overflow Users

• SitePoint: Best Programming Language for AI

• Dataconomy: How to Choose a Programming Language for Your Machine Learning Project

• HPCWire: QuEra’s Quest: Build a Flexible Neutral Atom-Based Quantum Computer

• HPCWire: Why Developers Should Let oneAPI ‘Dramatically Simplify’ Their Work

• Analytics Insight: Top 10 Programming Languages for Data Scientists to Learn in 2023

• MarketScreener: Intel - Why Developers Should Let oneAPI 'Dramatically Simplify' Their Work

• MarketScreener: Intel Corporation - Special Call

• Analytics India: Top 9 Python Libraries for Machine Learning in 2022

• IndiaAI: Eight Best IDEs for Data Scientists

• ZDNet: Top Programming Languages List: An Unexpected Change at the Top

• Dice: Rust Programming Language on the Rise: Should You Learn It?

• Analytics Insight: Top 10 DataOps Programming Languages for Developers to Learn in 2023

• TheNextWeb: Forget about Algorithms and Models — Learn How to Solve Problems First

• Forbes India: Nandan Nilekani Is Overhauling the Way the World Sees India

• KDNuggets: Black Friday Deal – Master Machine Learning for Less with DataCamp

• 24/7 Wall Street: 22 Jobs With the Fastest-Changing Skill Requirements

• PC Market: 【AWS re:Invent 22】量子電腦發展還早　企業開始試用

• Developpez: C++ se Classe Mieux que Java pour la Première Fois dans l'Histoire de l'Indice de TIOBE

• DataDrivenInvestor: State of Data Science for 2023

• FlyXG: 人工智能机器学习技术的迅速发展中获益良多

Julia Blog Posts

• Why Were Computers Invented? (Erik Engheim)

• The Importance of Monitoring Machine Learning Models (Nikhil Adithyan)

• Automatically Generate Comments for Your Julia Code with OpenAI.jl and GPT-3 (Logan Kilpatrick)

• 10 Simple Reasons to Learn the Julia Programming Language (in 2023) (Logan Kilpatrick)

• No, Julia Is Not 34,000,000,000 Times Faster than Python (Logan Kilpatrick)

• Case Study: Documenting Machine Learning Models in a Julia ML Framework (Logan Kilpatrick)

• Here’s Why Quantum Computing Could Be the Big Break for the Julia Language (Logan Kilpatrick)

• Here’s Why Startups Are Building with the Julia Programming Language (Logan Kilpatrick)

• Metadata in DataFrames.jl: Why and How? (Bogumił Kamiński)

• The Mighty Trio: Project Euler, Simpson's Paradox, and DataFrames.jl (Bogumił Kamiński)

• Lessons in the Fundamentals of Julia from Master Toshiro Kageyama (Bogumił Kamiński)

• Handling Summary Statistics of Empty Collections (Bogumił Kamiński)

• Pivot Tables in DataFrames.jl 1.4 (Bogumił Kamiński)

• Integrating Equation Solvers with Probabilistic Programming through Differentiable Programming (Christopher Rackauckas)

• Building A Syntax Highlighter In Julia (Emmett Boudreau)

• The Coolest Technique Ever For Loading Extensions (Emmett Boudreau)

• Building An RPC Chat App With Julia In 10 Minutes (Emmett Boudreau)

• What’s Next For Toolips? (Emmett Boudreau)

• Every Scaler and Its Application in Data Science (Emmett Boudreau)

• Trend Following with ETFs (Dean Markwick)

• Bayesian Logistic Regression (Patrick Altmeyer)

• How to Conformalize a Deep Image Classifier (Patrick Altmeyer)

• A Year of Using Quarto with Julia (Patrick Altmeyer)

• Literate Programming Using Documenter.jl and Entangled (Johannes Hidding)

• Computing Polynomials: Outperforming C++ (Johannes Hidding)

• QRCoders.jl: Efficient Generator for Especial QR Codes in Julia (Rex Wang)

• Genie Builder v0.2: Speed Up Your Julia App Development (Pere Giménez)

• World Happiness Report - EDA & Clustering with Julia (Navi)

• My Experience Working as a Technical Writer for MLJ (David Josephs)

• Asking Questions of Kaggle Survey with Julia (Anup Joseph)

• Handling Strings and GadFly-Plotting while Learning about Zipf's Law (Alex Tantos)

Upcoming Julia and JuliaHub Events

• Mumbai: Julia Meetup with Dr. Viral Shah (JuliaHub), Ajay Shah (XKDR) and Ayush Patnaik (XKDR) at XKDR Mumbai Jan 3

• Bangalore: Pluto.jl and JavaCall.jl Julia Meetup with Avik Sengupta (JuliaHub VP of Engineering) and Panagiotis Georgakopoulos (JuliaHub Software Engineer) at JuliaHub Bangalore Jan 10

• Gainesville: Why Julia? with MIT Professor Alan Edelman (JuliaHub Chief Scientist and co-founder, Julia co-creator) at University of Florida College of Engineering Jan 31

Recent Julia and JuliaHub Events

• Brisbane: Julia as a Data Science Research Tool with Yoni Nazarathy (University of Queensland) at the Australian Data Science Network (ADSN) Nov 21

• Orlando, FL: Interservice/Industry Training, Simulation and Education Conference (I/ITSEC) with JuliaHub Nov 28-Dec 2

• New Orleans, LA: Conference on Neural Information Processing Systems (NeurIPS) with JuliaHub Nov 28-Dec 9

• Webinar: Using Large Datasets in JuliaHub with Dr. Matt Bauman (JuliaHub) Dec 1

• Online Workshop: Computational Secrets of Julia Revealed with MIT Professor Alan Edelman (JuliaHub co-founder and Chief Scientist) and Intel Machine Learning Architect Rahul Unnikrishnan Nair Dec 2

• Bangalore: Julia Meetup with Dr. Viral Shah (JuliaHub CEO and Julia co-creator) Dec 13

Contact Us: Please contact us if you want to:

• Learn more about JuliaHub, JuliaSim, Pumas, PumasQSP or CedarEDA

• Obtain pricing for Julia consulting projects for your organization

• Schedule online Julia training for your organization

• Share information about exciting new Julia case studies or use cases

• Spread the word about an upcoming online event involving Julia

• Partner with JuliaHub to organize a Julia event online

• Submit a Julia internship, fellowship or job posting

About JuliaHub and Julia

JuliaHub is a fast and easy-to-use code-to-cloud platform that accelerates the development and deployment of Julia programs. JuliaHub users include some of the most innovative companies in a range of industries including pharmaceuticals, automotive, energy, manufacturing, and semiconductor design and manufacture.

Julia is a high performance open source programming language that powers computationally demanding applications in modeling and simulation, drug development, design of multi-physical systems, electronic design automation, big data analytics, scientific machine learning and artificial intelligence. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort. Julia has been downloaded by users at more than 10,000 companies and is used at more than 1,500 universities. Julia co-creators are the winners of the prestigious James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.