Conference Agenda

Welcome to JuliaCon 2023 Keynotes! We're excited to feature leading experts in the Julia community, who will share their latest insights and developments. Stay tuned for inspiring talks and lively discussions!

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

Full title: Optimizations with orthogonality constraints and eigenvector-dependent nonlinear eigenvalue problems. Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Morning break for coffee and snacks, and transit time from the keynote to the rest of the day's talks.

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday

This talk is designed to give data/insights/decision intelligence team leads a better understanding of the potential of Julia and how it can be effectively adopted in their teams. I'll be discussing the advantages and disadvantages of adopting Julia, drawing on my own experience and sharing some of the lessons I've learned along the way. I'll also be sharing several examples of Julia's unreasonable effectiveness that have supercharged our small team.

Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour. We present RobustNeuralNetworks.jl: a Julia package for neural network models that are constructed to naturally satisfy robustness constraints. We discuss the theory behind our model parameterisation, give an overview of the package, and demonstrate its use in image classification, reinforcement learning, and nonlinear robotic control.

In this talk, I introduce ElasticSurfaceEmbedding.jl, a package for creating holdable surfaces by weaving paper strips. I'll discuss the process of embedding pieces of a target surface into a plane and minimizing their elastic strain energy. The presentation aims to engage a diverse audience such as mathematicians, physicists, and handicraftsmen, and explore interdisciplinary implications. Physical examples will be showcased onsite.

Google Optimization Tools (a.k.a., OR-Tools) is an open-source, fast and portable software suite for solving combinatorial optimization problems. In this talk, we present our work on a new interface to OR-Tools, and we describe the lessons that we learnt along the way.

Agent-based modeling of the whole cell has emerged as a frontier of modern research. To address this challenge, we developed MEDYAN, a mechano-chemical forcefield and simulation software, in C++. Our new Julia package is 10x faster. To achieve this result, the overall architecture was redesigned and various Julia packages were leveraged. I will describe our strategy for combining stochastic reaction diffusion dynamics with movable membrane and filament mechanics. See medyan.org for more details.

Dynamic optimization problems include optimal control, state estimation, and system identification. Our newly developed integrated residual methods generalize the state-of-the-art direct collocation method. Interesso.jl implements a selection of Lagrange polynomial and Gauss quadrature node distributions. The iterative Progradio.jl optimizer allows for efficient mesh refinement. We include an example of optimizing the trajectory of a space-shuttle landing.

In this talk, I present a collection of Julia packages developed for Tensor Network simulation experiments (Tenet.jl and EinExprs.jl). We examine which Julia features and design choices enabled us to offer an intuitive interface for users, increasing the tunability and flexibility without loss of performance.

Pigeons.jl enables users to leverage distributed computation to obtain samples from complicated probability distributions, such as multimodal posteriors arising in Bayesian inference and high-dimensional distributions in statistical mechanics. Pigeons is easy to use single-threaded, multi-threaded and/or distributed over thousands of MPI-communicating machines. We demo Pigeons.jl and offer advice to Julia developers who wish to implement correct distributed and randomized algorithms.

Este plática consta de compartir la experiencia de haber presentado un curso llamado Julia Para Gente Con Prisa en el IIMAS de la UNAM en el verano de 2022. Es un curso diseñado para primerizos pero con aplicaciones para gente que quiere explotar lo más posible de sus cores en ciencias de datos o proyectos de interés social

We have developed BiosimMD.jl, a package for performing molecular dynamics (MD) simulations significantly faster than state-of-the-art engines. We present performance benchmarks of the package and its versatility. The package allows scientists to implement novel methods for MD without compromising the speed of simulation. We also discuss aspects of Julia critical in BiosimMD’s development, including access to many levels of computational abstraction, metaprogramming, and ease of multi-threading.

We present our package and book that we’ve developed to write Julia for Data Science Book. Unlike many other books, our book considers functions are first class citizens and is fully (re)built with CI. We discuss how to develop a code of conduct for communication guidelines and a workflow for coauthoring together using GitHub features such as pull request and issues as project management tools.

We present the key ideas for finding first-order critical points of multi-objective optimization problems with nonlinear objectives and constraints. A gradient-based trust-region algorithm is modified to employ local, derivative-free surrogate models instead, and a so-called Filter ensures convergence towards feasibility. We show results of a prototype implementation in Julia, relying heavily on JuMP and suitable LP or QP solvers, that confirm the use of surrogates to reduce function calls.

We report on the progress of Julia for statistics

Julia has had the most developed ecosystem for differential equation modeling in simulation through the SciML organization for a while. Here we present a collection of talks by computational systems biologists in the community. The focus of the symposium will be to look at how SciML tools are being used in systems biology, how they can improve, and how we can take steps to increase collaboration throughout industry and academia.

We propose ConformalPrediction.jl: a Julia package for Predictive Uncertainty Quantification in Machine Learning (ML) through Conformal Prediction. It works with supervised models trained in MLJ.jl, a popular comprehensive ML framework for Julia. Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions.

In this talk, I'll share my experience with Julia in a new (to me) context: in a classroom of first-year undergraduate students who have no coding experience! I plan to use Pluto notebooks to make a series of interactive computational (and low- or no-code) demonstrations of concepts from the introductory materials science course at Carnegie Mellon which I am teaching in the Spring 2023 semester. I look forward to sharing the resources I create as well as my reflections on the experience!

We present QUBO.jl, an end-to-end Julia package for working with QUBO (Quadratic Unconstrained Binary Optimization) instances.

The Bruno.jl package allows for pricing financial derivative assets under different theoretical models over varying time frames. This enables technical traders to formulate and test trading strategies within the package based on the derivatives themselves, rather than relying solely on the underlying assets. Using multiple dispatch, the simulating environment is left generic allowing for a wide range of uses from finance practitioners to academics.

In many fields of optimization, there is often a tradeoff between efficiency and the simplicity of the model. ConstraintLearning.jl is an interface to several tools designed to smooth that tradeoff.

• CompositionalNetworks.jl: a scaling glass-box method to learn highly combinatorial functions [JuliaCon 2021]
• QUBOConstraints.jl: a package to automatically learn QUBO matrices from optimization constraints.

Applications are not limited to Constraint Programming, but are focused on it.

We present LotteryTickets.jl, a library for finding lottery tickets in deep neural networks: pruned, sparse sub-networks that retain much of the performance of their fully parameterized architectures. LotteryTickets.jl provides prunable wrappers for all Flux.jl defined layers as well as an easy macro for making a predefined Flux model prunable.

A handful of Julians get nerdsniped into a ridiculous benchmarking spat with all the other languages.

After a 1000+ slack thread that defied numerical stability, common sense and message quotas, our intrepid Julians won nothing but a few lost days of productive heckling.

It was beautiful.

The talk is about the creation of a GitHub repository that teaches users Julia by forking the repository and using CI/CD to autonomously teach best Julia practices.

ENLSIP algorithm is designed to solve nonlinear least squares problems under nonlinear constraints. Implemented in Fortran77, it has been successfully used for decades by Hydro-Québec, the main electricity supplier for the province of Quebec in Canada, to calibrate its short-term electricity demand forecast models. A conversion into Julia has been developed to improve reliability and readability of the original code. We now present it as a Julia numerical optimization open-source package

We introduce GeNIOS.jl, a package for large-scale data-driven convex optimization. This package leverages randomized numerical linear algebra and inexact subproblem solves to dramatically speed up the alternating direction method of multipliers (ADMM). We showcase performance on a logistic regression problem and a constrained quadratic program. Finally, we show how this package can be extended to almost any convex optimization problem that appears in practice.

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

This talk will demonstrate the use of the MLJ.jl package in building a machine-learning pipeline for a dataset of property loans. The goal is to predict which loans might default and build a strategy to minimize losses. Several machine learning models such as ElasticNet, XGBoost, and KNN will be explored, and then combined into a stacked model. I will also show how the output of these models can be used to drive investment decisions and the final results of the strategy. This talk will provide a

The Julia community aims to be welcoming, diverse, inclusive towards people from all backgrounds. However, there is still room for improvement in terms of engagement and representation of people identifying from underrepresented backgrounds. In this talk, we will present different initiatives aimed at supporting underrepresented individuals and groups within the Julia community such as Julia Gender Inclusive, JuliaCN, and the Development and Diversity Fund.

In this talk, I’ll introduce a suite of software packages built end-to-end in Julia to represent the modeling and analysis of complex financial systems. This package ecosystem leverages agent-based modeling and machine learning to produce a flexible simulation environment for studying market phenomena, generating scenarios, and building real-time trading applications.

We present the package FastOPInterpolation.jl. It provides interpolation on arbitrary tensor product domains. The main goal is fast, repeated evaluations at fixed order with forward recursion and fast reinterpolation of updated or new functions. The implemented domains include lines, disks, and triangles. The extension to other domains is easily possible. It was originally developed with integral equations on product spaces in mind.

Polynomial Optimization can be solved in a variety of ways. JuMP provides an unified interface for modelling these problems. In this talk, we show how to interface each type of polynomial optimization solver to this model and how they compare on a variety of benchmark problems.

This is a light hearted talk that looks at some interesting statistics and tidbits of the Julia repo from the almost 15 years it has existed.

Developers have been using multithreading to obtain increased performance. However, developers find it difficult to write multithreaded code. To help them, it is important to understand the difficulties they face. The goal of the study is to evaluate the challenges faced by developers with multithreading in Julia using Julia Discourse and Stack Overflow. Conversation between developers on these online discussion forums were analyzed using inductive qualitative content analysis.

In the context of the famous quote “It ain’t what you don’t know that gets you in trouble. It’s what you know for sure that just ain’t so” attributed to Mark Twain, we explore the magnitude of the trouble you get into when you introduce pathological assumptions in scientific machine modeling. Considering Universal Differential Equations we ask “what happens if our domain’s knowledge is incorrectly specified?” and answer showcasing the high interoperability of Julia.

Computable General Equilibrium (CGE) models are large systems of non-linear equations that combine economic theory with real economic data to describe impacts of policies or shocks in the economy. Currently these problems are predominately solved using GAMS which is an expensive, closed source solution. We have created a package called GamsStructure.jl to emulate GAMS data manipulation in Julia. We will discuss several models created in both GAMS and Julia to highlight key similarities.

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

We report on progress of Julia for statistics

In this talk, we present a differentiable Julia implementation of eigenvalue algorithms based on isospectral flows, i.e., matrix systems of ordinary differential equations (ODEs) that continuously drive Hermitian matrices toward a diagonal steady state. We discuss different options for suitable ODE solvers as well as methods for computing sensitivities, and showcase applications in quantum many-body physics.

Julia has had the most developed ecosystem for differential equation modeling in simulation through the SciML organization for a while. Here we present a collection of talks by computational systems biologists in the community. The focus of the symposium will be to look at how SciML tools are being used in systems biology, how they can improve, and how we can take steps to increase collaboration throughout industry and academia.

Julia Gender Inclusive is an initiative that supports gender diversity in the Julia community. We are a group of people whose gender is underrepresented in the community and aim to provide a supportive space for all gender minorities in the Julia community. Over the last year, we have worked toward doing so with our Learn Julia with Us workshops, regular coffee meetings, and our inaugural hackathon. With the BoF session, we hope to discuss current and future initiatives with other people with un

In this talk proposal, we will discuss the chain of fraudulent transactions and help the investigation agencies to fight money laundering with the help of Julia programming language and packages.

We present an efficient and scalable approach to inverse PDE-based modelling with the adjoint method. We use automatic differentiation (AD) with Enzyme to automaticaly generate the buidling blocks for the inverse solver. We utilize the efficient pseudo-transient iterative method to achieve performance that is close to the hardware limit for both forward and adjont problems. We demonstrate close to optimal parallel efficiency on GPUs in series of benchmarks.

We present the Branch-and-Bound Performance Estimation Programming (BnB-PEP), a unified methodology for constructing optimal first-order methods for convex and nonconvex optimization. BnB-PEP poses the problem of finding the optimal optimization method as a nonconvex but practically tractable quadratically constrained quadratic optimization problem and solves it to certifiable global optimality using a customized branch-and-bound algorithm.

When we set out to build the modelling language for StateSpaceEcon.jl, our package for macroeconomic models, we did not yet know that Julia was the perfect language for the task. When faced with hundreds of variables, shocks, equations, parameters, lags and expectation terms, what the economist needs most is an intuitive and expressive domain-specific language to help keep all that complexity under control. Join us as we share our experience – it might help you enhance your own packages.

In this talk, we present continuous-adjoint sensitivity methods for hybrid differential equations (i.e., ordinary or stochastic differential equations with callbacks) modeling explicit and implicit events. The methods are implemented in the SciMLSensitivity.jl package. As a concrete example, we consider the sensitivity analysis of dosing times in pharmacokinetic models. We discuss different options for the automatic differentiation backend.

I would like to share with the JuMP community some of my experience building stochastic programming models in production, this talk will discuss design patterns used for an LNGC logistics problem developed in an R&D project.

This talk will explore using Julia to simulate the Request for Quote (RFQ) trading method. RFQ is a trading method that puts counterparties in competition by asking banks for prices to buy or sell an asset. I will simulate the Executing in an Aggregator model (Oomen 2017) and demonstrate why Julia's high performance and ease of use make it a perfect choice for simulating this type of trading. I'll finally show how we can learn from these simulations, educate clients and guide pricing strategies.

As physicists build ever more advanced particle accelerators, corresponding simulation softwares demand more computational resources. Our experiment, IsoDAR, is no exception to this. To reduce computational overhead of high-fidelity simulations, we used Julia to develop machine learning models that can, with reasonable accuracy, predict the behavior of a beam traversing our accelerator. These surrogate models have the potential to transform the way physicists design and optimize accelerators.

Julia provides a vibrant automatic differentiation (AD) ecosystem, with numerous AD libraries. All these AD solutions are unique, and take diverse approaches to the various fundamental AD design choices for code transformations available in Julia. The recent refactoring of the JuMP nonlinear interface is giving us an opportunity to integrate some of these AD libraries into JuMP. However, how far can we go in the integration?

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

Mathematics is a science and one of the most important discoveries of the human race on earth. In our daily life, we use mathematics knowingly and unknowingly. Many of us are unaware that forensic experts use mathematics to solve crime mysteries. In this talk, we will explore how Sherlock Holmes, the famous fictional detective character created by Sir Arthur Conan Doyle uses Mathematics and Julia programming language to solve crime mysteries.

Economic research strongly depends upon the economist’s ability to identify relevant information for causal inference and forecast accuracy efficiently. We address this goal in our Julia’s ParallelGSReg project, developing different econometric-machine learning packages. In JuliaCon 2023, we will present an improved version of our dimensionality reduction package (including non-linear algorithms) and a new "research acceleration package" with automatized Latex code and AI-bibliographic features.

We present a tool for user-friendly generation of machine learning surrogates that allows for fast and optimizer-free Model Predictive Control. Using a variety of practically-relevant examples, we demonstrate its utility to the workflow of a control engineer in the context of Julia simulation tools (such as ModelingToolkit.jl).

In recent years, it has been extensively demonstrated that phase transitions can be detected from data by analyzing the output of neural networks (NNs) trained to solve specific classification problems. In this talk, we present a framework for the autonomous detection of phase transitions based on analytical solutions to these problems. We discuss the conditions that enable such approaches and showcase their computational advantage compared to NNs based on our Julia implementation.

We present our research efforts in creating a performance portable programming model, JACC, in Julia targeting heterogeneous hardware on the US Department of Energy Leadership Computational Facilities. JACC leverages the high-productivity aspect of Julia and the CUDA.jl and AMDGPU.jl vendor specific GPU implementations and expands to many core CPUs (Arm, x86) and automatic memory management. The goal is to allow Julia applications to write performant code once leveraging existing infrastructure.

Falra.jl in Julia provides a straightforward approach to implementing distributed computing, equipped with an AI-assisted feature for generating source code. This addition facilitates more efficient big data transformations. Tasks such as preprocessing 16TB of IoT data can be done in 1/100 of the original time. Developers are now able to generate Julia source code more easily with the aid of AI, further aiding in distributed computing tasks.

Many complex networks present a temporal nature, e.g. Social Networks, and their modelling is still an challenge. In this talk we’ll show how, in our research group, we use the Julia's SciML ecosystem to understand and predict the temporal evolution of networks. In particular, we'll present a new package DotProductGraphs.jl, that implements tools from the statistical theory of graph embedding (RDPG) to model temporal networks as dynamical systems.

We hope you're enjoying JuliaCon 2023 so far! Please find our food trucks waiting right outside venue with food available for purchase.

We hope you're enjoying JuliaCon 2023 so far! Please find our food trucks waiting right outside venue with food available for purchase.

We hope you're enjoying JuliaCon 2023 so far! Please find our food trucks waiting right outside venue with food available for purchase.

We hope you're enjoying JuliaCon 2023 so far! Please find our food trucks waiting right outside venue with food available for purchase.

We hope you're enjoying JuliaCon 2023 so far! Take a break and grab some lunch to recharge for the afternoon sessions. We have a delicious spread waiting for you in the dining hall. Bon appétit!

We hope you're enjoying JuliaCon 2023 so far! Please find our food trucks waiting right outside venue with food available for purchase.

We hope you're enjoying JuliaCon 2023 so far! Take a break and grab some lunch to recharge for the afternoon sessions. We have a delicious spread waiting for you in the dining hall. Bon appétit!

Julia and Rust both punch above their weight, but how well do they gel together? We'll invite community members to speak on their experiences interfacing the languages in both directions, future possibilities of collaboration and leveraging the best of both worlds.

Julia has had the most developed ecosystem for differential equation modeling in simulation through the SciML organization for a while. Here we present a collection of talks by computational systems biologists in the community. The focus of the symposium will be to look at how SciML tools are being used in systems biology, how they can improve, and how we can take steps to increase collaboration throughout industry and academia.

We present an efficient approach for the development of 3-D partial differential equation solvers that are able to tackle the hardware limit of modern GPUs and scale to the world's largest supercomputers. The approach relies on the accelerated pseudo-transient method and on the automatic generation of on-chip memory usage-optimized computation kernels for the implementation. We report performance and scaling results on LUMI and Piz Daint, an AMD-GPU and a NVIDIA-GPU supercomputer.

WorldDynamics.jl is a Julia package which aims to provide a modern framework to investigate Integrated Assessment Models (IAMs) of sustainable development benefiting from Julia's ecosystem for scientific computing. Its goal is to allow users to easyly use and adapt different IAMs, from World3 to recent proposals. In this talk, we are going to present the motivations behind the package, its major goals and current functionalities, and prospect our expectations for the following releases.

In this presentation, we give an overview of the recent progress regarding the continuous nonlinear nonconvex optimization solvers implemented in the JuliaSmoothOptimizers (JSO) organization. We introduce the new package JSOSuite.jl, a unique interface between users and JSO solvers.

We report on progress of Julia for statistics

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

You want to build a complex macro? ExprParsers.jl gives you many prebuilt expression parsers - for functions, calls, args, wheres, macros, ... - so that you don't need to care about the different ways these high-level Expr-types can be represented in Julia syntax. Everything is well typed, so that you can use familiar julia multiple dispatch to extract the needed information from your input Expr.

The Earth’s crustal magnetic field is a powerful tool for navigation as an alternative to GPS. MagNav.jl is an open-source Julia package containing algorithms for both aeromagnetic compensation and navigation. Alongside baseline algorithms, such as Tolles-Lawson, this package enables multiple scientific machine learning approaches for compensation. This talk will cover some of these techniques and advanced use cases for MagNav.jl in navigation.

VimBindings.jl is a Julia package that emulates vim, the popular text editor, directly in the Julia REPL. This talk will illuminate the context in which a REPL-hacking package runs by taking a deep dive into the Julia REPL code, and articulate the modifications VimBindings.jl makes to introduce novel functionality. The talk will also describe design problems that emerge at the intersection of the REPL and vim paradigms, and the choices made to attempt a coherent fusion of the two.

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

In JuMP 1.0, support for nonlinear programming is a second-class citizen. In talk, we discuss our efforts to build a first-class NonlinearExpression object in JuMP, banishing the need for the @NL macros.

Julia Accelerator Interfaces(JAI, github.com/grnydawn/AccelInterfaces.jl) tries to solve the issues in code migration from Fortran to Julia GPU by using shared libraries. JAI consists of 1) Julia GPU programming interface using Julia macros whose syntax is similar to OpenACC. 2) Automated shared library generation that implements kernels and vendor API interfaces using vendor-provided compilers. 3) Automated call to functions implemented in the shared libraries using Julia ccall interface.

During the BoF we want to discuss future directions of development of JuliaData ecosystem. The objective is to identify priorities of development of current packages in the ecosystem and discuss what functionalities are missing and should be added in new packages.

This talk explores the use of Julia in a novel observational health research study that explores health equity and mental health in ~100 million patients in an international collaborative effort across more than 4 countries. Contributions and efforts within the JuliaHealth and adjacent communities have made working with this data possible. The approaches and results shared will be valuable for potential researchers and will open new frontiers for high performance computing and health analytics.

We were able to successfully synthesize simple compact high-level neural machines via a novel algorithm for neural architecture search using flexible differentiable programming capabilities of Zygote.jl.

OpenTelemetry.jl is a pure Julia implementation of the OpenTelemetry specification. It enables developers to collect logs, traces, and metrics in a unified approach to improve the observability of complex systems. With OpenTelemetry.jl, users can not only analyze the telemetry data in Julia but also across many other different languages or services.

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

The Julia HPC community has been growing over the last years with monthly meetings to coordinate development and to solve problems arising in the use of Julia for high-performance computing.

The Julia in HPC Birds of a Feather is an ideal opportunity to join the community and to discuss your experiences with using Julia in an HPC context.

vOptGeneric.jl is a package based on JuMP and MOI for modeling and solving multi-objective linear optimization (MOO) problems. vOptGeneric will soon be discontinued, and replaced by a new package, fully based on the syntax of JuMP and offering a convenient interface entirely based on MOI to extend the pool of MOO algorithms. In this talk, we present our feedback on our experiences from vOptGeneric.

We describe a project-based assignment in the EE4375 master course held at the TU Delft. The assignment asks to perform finite element simulations of the magnetic and thermal field of power transformer in distribution grids. Students can choose to employ both codes developed earlier in the course or to resort to existing finite element packages. See github.com/ziolai/finite_element_electrical_engineering . The assignment is developed in collaboration with the local system administrator.

Writing a large optimization model is a time-consuming and error-prone task. If and when a modeling error is suspected, the developer often must re-consider every constraint in the model for faulty assumptions causing singularities in the optimization model, which impede convergence of solvers. We introduce a package that computes the Dulmage-Mendelsohn and block triangular partitions of incidence graphs of JuMP models, which can be used to detect sources of structural and numeric singularities.

Many optimization problems can be represented as a graph to provide useful visualization and to allow for manipulating and exploiting problem structure. We use Plasmo.jl (which extends JuMP.jl) to build optimization problems within a graph structure (where optimization model components are stored within the nodes and edges of the graph), and we use the nonlinear interior-point solver, MadNLP.jl, to solve these models and exploit some of these graph structures.

This talk will go into how to use julia's Logging standard library, in particular into how to configure it using LoggingExtras.jl. LoggingExtras.jl is a suite of extra functionality on top of the Logging standard library to make configuring log handling simpler. Primarily it works by separating all the ways you can configure the logger into a series of composable objects with only one function: filtering, splitting, transforming. This allow for easy and comprehensive control of logging.

The numerical solution of optimal control of dynamical systems is a rich process that typically involves modelling, optimisation, differential equations (most notably Hamiltonian ones), nonlinear equations (e. g. for shooting), pathfollowing methods… and, at every step, automatic differentiation. We report on recent experiments with Julia on a variety of applied or more theoretical problems in optimal control and try to survey what the practitioner will find, and would like to find.

Julia is a high-performance programming language that has gained traction in the machine-learning community due to its simplicity and speed. The talk looks at how Julia can potentially be used to build machine learning models on the server using WebAssembly (WASM) and the WebAssembly System Interface in this talk (WASI) but also look at some of the major hurdles along the way. The talk will go over the benefits of using WASM and WASI for building such as improved performance and security.

We present an easy to use and powerful package that enables analysis of Single Cell Expression data in Julia. It is faster and uses less memory than existing solutions since the data is internally represented as expressions of sparse and low rank matrices, instead of storing huge dense matrices. In particular, it efficiently performs PCA (Principal Component Analysis), a natural starting point for downstream analysis, and supports both standard workflows and projections onto a base data set.

As an alternative code-hosting/project management platform, GitLab is not as well supported within the Julia community. This talk presents a GitLab CI process for Julia aiming to change this, making it easier for those using GitLab as their preferred platform to build and ship Julia-based software.

An ad-hoc approach to acquiring and using data can seem simple at first, but leaves one ill-prepared to deal with questions like: "where did the data come from?", "how was the data processed?", or "am I looking at the same data?". Generic tools for managing data (including some in Julia) exist, but suffer from limitations that reduce their broad utility. DataToolkit.jl provides a highly extensible and integrated approach, making robust and reproducible treatment of data and results convenient.

Feedback control policies for quantum systems often lack performance targets and certificates of optimality. Here, we will show how bounds on the best possible control performance are readily computable for a wide range of quantum control problems by means of convex optimization using Julia's optimization ecosystem. We discuss how these bounds provide targets and certificates to improve the design of quantum feedback controllers.

As Julia usage continues to grow within regulated biomedical environments, it is vital to ensure analyses are traceable and reproducible. Conducting analyses in an open-science manner is also critical to expand the adoption of Julia and to facilitate the infrastructure growth of Julia as an accessible ecosystem. A step-by-step model-building example of a classic monoclonal antibody-drug conjugate PBPK/tumor dynamics system illustrates how to develop such a reproducible open-science framework.

A casual, open-ended discussion for anyone interested in using Julia in health and medicine. In particular, we'll discuss strategies for growing and strengthening the Julia health and medicine community.

We present a new Julia library, TDAOpt.jl, which provides a unified framework for automatic differentiation and gradient-based optimization of statistical and topological losses using persistent homology. TDAOpt.jl is designed to be efficient and easy to use as well as highly flexible and modular. This allows users to easily incorporate topological regularization into machine learning models in order to optimize shapes, encode domain-specific knowledge, and improve model interpretability

Type instabilities are not always bad! Using non-concrete types, and avoiding method specialization and type inference can help with improving latency and, in specific cases, runtime performance. The latter is observed in inherently dynamic contexts with no way to compile all possible method signatures upfront, because code needs to be compiled at points of dynamic dispatch by design. We present a concrete case we face in our production environment, additional examples, and related trade-offs.

Scaling up atomistic simulation models is hampered by expensive calculations of interatomic forces. Machine learning potentials address this challenge and promise the accuracy of first-principles methods at a lower computational cost. This talk presents, as part of the research activities of the CESMIX project, how Julia is used to facilitate automating the composition of a novel neural potential based on the Atomic Cluster Expansion.

ValueConstraints.jl provides a framework for declaratively expressing constraints on values, e.g. "minimum", "maximum", "needs to be within this set", etc. along with a small standard library of common constraints. It is fast, i.e. on par with using regular function calls, provides friendly error messages and warnings, and, perhaps most importantly, can serve as a foundation for the creation of schemas.

The electronic structure theory is critical for understanding materials, but it has been challenging to develop readable yet efficient electronic structure packages. WTP.jl identifies a layer of abstractions that simplifies such development through an interface resembling the mathematical notation of the electronic structure theory. Using WTP.jl, we built another package SCDM.jl for electron localization with code far more readable than the widely used alternative Wannier90.

In this talk various Julia packages for processing spatial data coming from the Open Street Map (OSM) project will be presented. OSM is an excellent source of information about road system that can be explored by tools such as OpenStreetMapX.jl However, the OSM files also contain useful information points interests (restaurants, hotels, schools, stores etc.) that can be extracted and analyzed in Julia.

NEOs.jl is an open source Near Earth Object orbit determination software package in the Julia programming language. NEOs.jl features exploitation of high-order automatic differentiation techniques, known as jet transport, in order to quantify orbital uncertainties in a versatile, semi-analytical manner. Using NEOs.jl we have estimated the Yarkovsky acceleration acting on the potentially hazardous asteroid Apophis and have ruled out potential impacts on 2036 and 2068.

This talk introduces InverseStatMech.jl, a Julia package that provides various efficient and robust algorithms to infer geometric structures of ordered and disordered materials from their spectra and other structural descriptors, which is a crucial inverse problem in statistical mechanics, crystallography and soft materials sciences. (Package currently under development.)

We present a novel transparent data persistence architecture as an extension of the SimJulia package. We integrated PostgresORM into the ResumableFunctions library by using Julia's metaprogramming support. As such, we were able to remove the dependency on a user's knowledge on architectures for persistence. Our contribution aims to improve the usability, whilst demonstrating the power of macro expansion to move towards a dynamic object-relational mapping configuration.

Join us for ASE-60, where we celebrate the life and the career of Professor Alan Stuart Edelman, on the occasion of his 60th birthday: https://math.mit.edu/events/ase60celebration/

At Beacon Biosignals we don't want to have to re-invent the wheel about data loading and batch randomization every time we stand up a new machine learning project. So we've collected a set of patterns that have proven useful across multiple projects into OndaBatches.jl, which serves as a foundation for building the specific batch randomization, featurization, and data movement systems that each machine learning project requires.

Using MixedModels.jl and MixedModelsMakie.jl, I will show several different ways to visualize different aspects of the model fit as well as the model fitting process. I will focus especially on shrinkage (downward bias of the random effects relative to similar estimates from a classical OLS model) and how MixedModels.jl uses BOBYQA and a profiled log likelihood to efficiently explore the parameter space.

This talk introduces cadCAD.jl, a high performance open source Julia library for modeling and simulating dynamical systems with generic attributes. Our goal with this talk is to show the main ideas behind the library, how it promotes open science, how we used Julia to achieve higher performance in comparison to it's Python implementation, and how it would fit in a data science workflow, by running an example simulation.

As JuliaCon 2023 comes to a close, join us for a memorable farewell ceremony to celebrate a week of learning, collaboration, and innovation. We'll recap the highlights of the conference, thank our sponsors and volunteers, and recognize outstanding contributions to the Julia community. Don't miss this opportunity to say goodbye to old and new friends, and leave with inspiration for your next Julia project. Safe travels!

Come hang out in the evening after talks for some friendly hacking and social time!