Conference Agenda

Revisiting Grothendieck: Szemeredi regularity, random submatrices, and covariance loss

Dr. Chowdhury currently runs Parity Consulting, Parity Responsible Innovation Fund, and is a Responsible AI Fellow at the Berkman Klein Center for Internet & Society at Harvard University. She is also a Research Affiliate at the Minderoo Center for Democracy and Technology at Cambridge University and a visiting researcher at the NYU Tandon School of Engineering.

Spotlight on Structure: from Matrix to Tensor Algebra for Optimal Approximation of Non-random Data

Alex Townsend: Avoiding Discretization Issues for Nonlinear Eigenvalue Problems

Elimination and Factorization

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.

Graphs (aka networks) are a key part of the data science pipeline at many organizations. However, scalability is the most frequently reported limitation by graph analysts. I introduce QuasiStableColors.jl, a Julia library for approximate graph analysis. On tasks such as ranking node importance (centrality) it enables an over 10x speedup while introducing less than 5% error. In this talk, I will demonstrate how to use this novel graph compression for your own workloads.

We expose a problem of estimation of multivariate convolutions of gamma random variables, which has very bad numerical properties. This bad numerical behavior literally forced us to use Julia. We describe why Python, R or C++ were not capable of solving our problem and argue that the multiple dispatch paradigm in Julia was the reason we were able to reuse existing code.

Learn about the new productivity features and performance improvements in Genie 6, the new major Genie release of 2023!

The objective of this minisymposium is to gather people interested in working with tabular data to allow them to discuss various aspects of the Julia ecosystem in this area.

The Julia for HPC minisymposium gathers current and prospective Julia practitioners from various disciplines in the field of high-performance computing (HPC). Each year, we invite participation from science, industry, and government institutions interested in Julia’s capabilities for supercomputing. Our goal is to provide a venue for showing the state of the art, share best practices, discuss current limitations, and identify future developments in the Julia HPC community.

The Scientific Machine Learning (SciML) ecosystem is rapidly gaining momentum within the field of systems biology. With this birds of feather discussion we want to bring the international community of systems biology tool developers and users at one table to (a) brainstorm promising routes for future developments, and (b) facilitate collaborative projects.

JuMP is a modeling language and collection of supporting packages for mathematical optimization in Julia. JuMP makes it easy to formulate and solve linear programming, semidefinite programming, integer programming, convex optimization, constrained nonlinear optimization, and related classes of optimization problems.

In this talk, we discuss the state of JuMP, preview some recently added features, and discuss our plans for the future.

In field of aerodynamic is often required to test different geometries, most of them are standard airfoils. The aim is usually to identify promising geometry for a specific application. Many operations repeat mechanically, and are tedious and time consuming. In specific, the mesh creation is a fundamental but routine task, similar but not identical for all the airfoil and all the test cases. This is where GmshAirfoil.jl comes in handy.

This talk presents a new Julia package for efficient and generic Monte Carlo integration in high-dimensional and complex domains, featuring the Vegas algorithm for self-adaptive important sampling and an improved algorithm for increased robustness. The package demonstrates Julia's superiority over C/C++/Fortran and Python for high-dimensional Monte Carlo integration by enabling the easy creation of user-defined integrand evaluation functions with the speed of C and the flexibility of Python.

Extended Kalman filters are super useful in robotics and embedded systems, but require the derivation of large state derivative matrices. Julia's symbolic manipulation facilities can make this much easier! I will introduce TinyEKFGen.jl, a Julia package that converts nice Julia expressions to embeddable C-code that works with the TinyEKF library, and show some examples usages (including one that runs in space!).

Chris Rackauckas: The Special Math of Translating Theory To Software in Differential Equations

A new, high-order multi-physics code, Cygnus.jl, has recently been used to simulate portions of inertial confinement fusion experiments at the University of Rochester's Laboratory for Laser Energetics. Methods of parallelization, code design, performance characterizations, and lessons learned will be presented alongside simulation results.

In this presentation, we showcase using JuMP to formulate and solve a range of optimization problems related to the location and market bidding of energy management systems.

Koma is an MRI simulator utilizing CPU and GPU parallelization to solve the Bloch equations. Our simulator targets researchers and students, offering an easy-to-use GUI. The accuracy and speed of our simulator were compared against two open-source alternatives JEMRIS (C++, using an ODE solver) and MRiLab (C++ and CUDA). The results show that Koma can simulate with high accuracy (MAEs below 0.1% compared to JEMRIS) and better GPU performance than MRiLab.

In this talk we present feedback on our experience developing a large-scale energy system model, particularly concerning garbage collection pressure during model creation and methods for exploring parametric models.

This talk will present TimeStruct.jl, a package developed to support the modeling of optimization problems in JuMP involving time structurers with different resolution on an operational and strategic level. The package also supports multiple operational scenarios and strategic tree structures to model uncertainty at both operational and strategic level.

When starting a company built on mathematical concepts Julia seems like an obvious choice because of its capabilities for enabling rapid prototypes, rewrites, and pushing for performance early. This talk presents our journey to build a Julia production app. In particular, we present how we use a monorepo within a distributed team and how we set up our continuous-integration infrastructure. Finally, we introduce an open source project which aims to make it easier to write Julia serverless apps.

Graph alignment is the problem of recovering a bijection between vertex sets of two graphs, that minimizes the divergence between edges. This problem can be encountered in various fields where graphs are often very large, so an efficient algorithm is essential for many applications. In this talk we will discuss how to solve this problem approximately with the popular Fast Approximate Quadratic (FAQ) algorithm and its recent improved variants implemented in the Julia GraphsOptim.jl package.

The objective of this minisymposium is to gather people interested in working with tabular data to allow them to discuss various aspects of the Julia ecosystem in this area.

Planning trajectories for underactuated systems is a challenging problem in robotics. The dynamics governing such systems are quite complex, and mechanisms themselves have strict physical limits. In this talk, I will explain how we can use Julia (and packages from its robotics ecosystem) to frame motion planning problems as numerical optimisations. I will also share videos of robots solving practical tasks in the real world, tracking trajectories computed with this approach.

The Julia for HPC minisymposium gathers current and prospective Julia practitioners from various disciplines in the field of high-performance computing (HPC). Each year, we invite participation from science, industry, and government institutions interested in Julia’s capabilities for supercomputing. Our goal is to provide a venue for showing the state of the art, share best practices, discuss current limitations, and identify future developments in the Julia HPC community.

Energy system models are generally written in algebraic modelling languages like AMPL or GAMS. This both limits the extendibility of the models due to the structure of the languages and can be seen as a threshold due to the license costs for the languages. Julia with JuMP is an excellent alternative as it is both open source and allows for flexibility in model structure by using multiple dispatch. The model is a novel energy system model framework with easy extendibility and high flexibility.

Physiologically based pharmacokinetic (PBPK) models characterize a drug’s distribution in the body using prior knowledge. Bayesian tools are well suited to infer PBPK model parameters using the informative prior knowledge available while quantifying the parameter uncertainty. The presentation will review a full Bayesian hierarchical PBPK modeling framework in Julia, using the SciML ecosystem and Turing.jl, to accurately infer the posterior distributions of the parameters of interest.

Unapologetically Beyond Universality by Folkmar Bornemann

The talk focuses on the use of Julia in real-world aerospace applications in place of C++ for the purpose of simulating large, complex systems that interface with embedded hardware and software in real-time. Leveraging memory allocation management in Julia alongside an interface with C++ shared libraries is used to perform real-time hardware-in-the-loop simulations of models written in Julia.

MultilayerGraphs.jl is a Julia package for the creation, manipulation and analysis of multilayer graphs, which have been adopted to model a wide range of complex systems from bio-chemical to socio-technical networks.

We will synthetically introduce multilayer network science, illustrate some of the main features of the current version of the package and talk about its future developments.

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 detail recent work on linear analysis of ModelingToolkit models. We talk about the linearization itself, the subsequent simplification of models with algebraic equations into standard linear statespace models and about causal elements introduced to enable a more convenient workflow for analysis. We end with examples, illustrating mode shapes of a series of masses and springs, compute gain and phase margins of an electrical circuit, and determine the stability properties of a control system.

PRONTO.jl is a Julia implementation of the Projection-Operator-Based Newton’s Method for Trajectory Optimization (PRONTO). PRONTO is a direct method for trajectory optimization which solves the optimal control problem directly in infinite-dimensional function space. It is capable of achieving quadratic convergence and has potential applications ranging from aerospace to quantum sensing.

Get an up-to-date overview of the modelling capabilities of JuMP through a number of worked examples. I'll cover the types of optimization problems you can solve effectively, give lots of unsolicited advice and briefly look at some extensions.

This talk presents a package to analyse long-range dependence (LRD) in time series data. LRD is shown by the fact that the effects from previous disturbances take longer to dissipate than what standard models can capture. Failing to account for LRD dynamics can perversely affect forecasting performance: a model that does not account for LRD misrepresents the true prediction confidence intervals. LRD has been found in climate, political affiliation and finance data, to name a few examples.

MINDFul.jl is a tool to research coordination mechanisms and intent-driven algorithms for multi-domain IP-Optical networks. It combines modern paradigms like Software Defined Networking (SDN) and Intent-Based Networking (IBN) to build a novel flexible architecture, which is appropriate but not limited to decentralized control. It provides a stateful representation of common metro/core network equipment and facilitates event-based simulations with a hackable interface and visualization support.

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! 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! 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! 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! 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! 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! 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!

The Julia for HPC minisymposium gathers current and prospective Julia practitioners from various disciplines in the field of high-performance computing (HPC). Each year, we invite participation from science, industry, and government institutions interested in Julia’s capabilities for supercomputing. Our goal is to provide a venue for showing the state of the art, share best practices, discuss current limitations, and identify future developments in the Julia HPC community.

This talk explores the capabilities of the Julia ecosystem for topological physics, specifically the calculation of topological invariants for periodically driven non-equilibrium Quantum systems. Often, these quantities are related to topological properties of complicated geometric objects. Julia's ability to streamline these calculations and easily integrate mathematical libraries via multiple dispatch offers a powerful toolbox that simplify the exploration of new phenomena considerably.

Through the use of GPUCompiler.jl and LLVM.jl, it is possible to cross-compile julia code to backends not officially supported by julia itself. One of these is the AVR backend, the architecture used by the arduino family of microcontrollers. This talk explores some experiments in compiling julia code to AVR, running it baremetal on an arduino as well as looking into challenges with making julia more suited to cross compilation.

In this talk, we will discuss the use of surrogates in scientific simulations, and introduce JuliaSim, a commercial offering built on top of the SciML ecosystem, and introduce some of the surrogates available in JuliaSim.

The objective of this minisymposium is to gather people interested in working with tabular data to allow them to discuss various aspects of the Julia ecosystem in this area.

Laplacians.jl by Daniel Spielman

Measure Transport, "moving" from one measure to another, has been gaining momentum to perform generative sampling, conditional density estimation, and other statistical methods on a computer. However, transport software is primarily bespoke, is not portable, and can be slow. The Monotone Parameterization Toolkit (MParT) package provides a fast, tested base in C++ to train and use complicated maps for transport easily, and we highlight the Julia bindings for the package in this talk.

Julia is primarily considered to be a "scientific programming language". Quite often, what starts out as "scientific programming" ends up in products meant to be consumed by others. When this happens, these pieces of software end up in "applications", typically with a UI of some sort. This BoF is for those that build these applications, to learn from others, and to discuss what can be done to move the ecosystem forward.

GreenFunc.jl is a powerful package that offers a solution to the complex computational challenges of quantum many-body systems. The package is developed using native Julia language which offers both speed and flexibility. GreenFunc.jl implements state-of-the-art algorithms for solving quantum many-body problems using a Green's function approach, making it an invaluable tool for researchers in fields such as material design, high-temperature superconductivity, and quantum information technology.

BPGates.jl is a tool for extremely fast simulation of quantum circuits, as long as the circuit is limited to only performing entanglement purification over Bell pairs.

ASE60 Poster Session

SciML provides tools for a wide problem space. It can be confusing for new users to decide between the packages and the kind of questions that can be answered using each of them. This talk will walk through various ecosystem components for tasks such as inverse problems, model augmentation, and equation discovery and showcase workflows for using these packages with examples based on real-world data.

In this talk, we introduce CurricularAnalytics.jl, a package for studying and analyzing academic program curricula. By representing curricula as graphs, we utilize various graph-theoretic measures to quantify the complexity of curricula. In addition to analyzing curricular complexity, the toolbox supports the ability to visualize curricula, create optimal degree plans for completing curricula, and simulate the impact of various events on student progression through a curriculum.

The Julia release process is important since it's the process by which all the work that goes into the Julia language gets delivered to users.
This talk details the various steps that are made to ensure that Julia releases are of high quality with few regressions, both in correctness and performance.

Quantum spin systems are a central topic in condensed matter physics. Their simplicity enables systematic study of the quantum properties, which has notable applications in, e.g., quantum computing. With ReducedBasis.jl we provide a Julia package, which uses reduced-basis methods to accelerate the modeling of parametrised eigenvalue problems, such as typical for quantum spin Hamiltonians. The package integrates with ITensors enabling a treatment using state-of-the-art tensor network methods.

The computational evaluation of the electronic properties of atoms and moleculesentails the use of quantum mechanics. The computational cost of some routines, in most cases, does not scale linearly and becomes very dependent on the size of the system. Thus, over time, the development of effective tools to accelerate such computations can benefit of new programming languages, like Julia, focused on numerical, scientific and faster programming.

Radio interferometry produces the sharpest images in astronomy through the concept of a computational telescope, with software and algorithms integral to their success. In the coming decade, new facilities, including the ngEHT, ngVLA, and SKA, will revolutionize astronomy, leveraging breakthroughs in digital technology to generate Petabyte-scale datasets. This minisymposium will explore Julia's rapidly growing role in radio astronomy and highlight crucially needed developments.

Geometric Algebra is a high-level mathematical framework which expresses a variety of geometric computations with an intuitive language. While its rich structure unlocks deeper insight and an elegant simplicity, it often comes at a cost to numerical implementations. After giving an overview of geometric algebra and its applications, a Julia implementation is presented which uses metaprogramming to shift the work to compile-time, enabling a fast and expressive approach to computational geometry.

Hydro-Quebec is a public electricity utility for the province of Quebec in Canada. Its demand forecast team has been working for decades with Fortran models used daily in a critical system. Julia was selected as the replacement for Fortran. An optimisation library with call-backs was converted to Julia. The Julia version of the library is exempt of Fortran interface, and we present how the Fortran applications are modified to call either version of the library as to compare their behaviours.

ModelingToolkitStandardLibrary is a library of components to model the world and beyond. We will demonstrate how various Mechanical, Electrical, Magnetic, and Thermal components can be used as basic building blocks to simulate complex models. We will talk about design choices, vision, and future plans for the package.

The Copulas.jl package brings standard dependence modeling routines to native Julia. Copulas are distributions functions on the unit hypercube that are widely used (from theoretical probabilities and Bayesian statistics, to applied finance or actuarial sciences) to model the dependence structure of random vectors apart from their marginals. This native implementation leverages the Distributions.jl framework and is therefore conveniently directly compatible with the broader ecosystem.

Joint Chance Constraints achieve a good trade-off between cost and robustness for optimization under uncertainty. This talk proposes a Julia implementation of Joint Chance Constraints and algorithms to solve the programming problems for different types of multivariate probabilities. The library focuses on unit commitment problems for decentralized, renewable-powered microgrids, connected to an unreliable higher-level, grid-balancing unit, but can be applied to similarly-structured problems.

We are introducing Piccolo.jl, an integrated quantum optimal control stack. In our recent paper, "Direct Collocation for Quantum Optimal Control", we demonstrated -- in simulation and on hardware -- that our direct collocation based pulse optimization method (PICO) is a powerful alternative to existing quantum optimal control (QOC) methods. Piccolo.jl is designed to be a simple and powerful interface for utilizing this method for pulse optimization and hardware-in-the-loop control.

In this talk, we introduce the BSTModelKit.jl package, which enables the construction and analysis of biochemical systems theory (BST) models of metabolic and single transduction networks. We demonstrate the capabilities of BSTModelKit.jl by analyzing the thrombin generation dynamics of a population of synthetic patients before and during pregnancy developed from an ongoing study at the University of Vermont supported by the National Institute of Health (NIH).

The proposal is about which Covid vaccine would be selected by target group of patients in a cross cultural comparison of US and India as features related to the vaccines vary. The data is analyzed using packages from Julia, Python, R, and STATA. The objective is to present a comparison of tools while analyzing an extremely relevant and time critical data related to Covid vaccinations

Geometric algebra (GA) is a powerful language for formulating and solving problems in geometry, physics, engineering and graphics. SimpleGA is designed as a straightforward implementation of the most useful geometric algebras, with the key focus on performance. In this talk we use the library to explain some key properties of GA, and explain the motivation behind the design and how it utilises Julia's unique features.

We describe and demonstrate a method to use Julia to generate music on a computer. While electronic music generation has had a long and distinguished history, the use of the Julia programming language provides benefits that are not available using traditional tools in this area.

Neural universal differential equations (UDEs) are one of the comparatively new concepts combining aspects of purely knowledge-based mechanistic models with black-box and data-based approaches. We explore the applicability of UDEs for parameter estimation in the context of systems biology. For this, we consider different levels of noise, data sparsity, and prior knowledge about the underlying system on a diverse set of problems.

This talk will present a simulation of geometric control on a quadrotor model built using ModellingToolkit.j. The resulting trajectories will be visualized using Makie.jl. The talk will be a detailed overview of how to use these Julia packages to simulate and visualize the control of quadrotors

Dr. Stephen Wolfram is the creator of Mathematica, Wolfram|Alpha and the Wolfram Language; the author of A New Kind of Science; the originator of the Wolfram Physics Project; and the founder and CEO of Wolfram Research.

Radio interferometry produces the sharpest images in astronomy through the concept of a computational telescope, with software and algorithms integral to their success. In the coming decade, new facilities, including the ngEHT, ngVLA, and SKA, will revolutionize astronomy, leveraging breakthroughs in digital technology to generate Petabyte-scale datasets. This minisymposium will explore Julia's rapidly growing role in radio astronomy and highlight crucially needed developments.

Radio interferometry produces the sharpest images in astronomy through the concept of a computational telescope, with software and algorithms integral to their success. In the coming decade, new facilities, including the ngEHT, ngVLA, and SKA, will revolutionize astronomy, leveraging breakthroughs in digital technology to generate Petabyte-scale datasets. This minisymposium will explore Julia's rapidly growing role in radio astronomy and highlight crucially needed developments.

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