Jones’s doctoral work included the creation of QuESTlink, an open-source WSTP-powered package for simulating quantum computers, integrating the QuEST project’s hardware-accelerated numerics with Mathematica’s powerful symbolic engine. QuESTlink combines a plethora of Wolfram facilities, novel algorithms and high-performance computing techniques behind an intuitive API, enabling research-frontier computation through only a few lines of code.

Simon Benjamin , principal investigator (PI), is a professor of quantum technologies with the Materials Department at the University of Oxford. He leads a group of 17 applied theorists who look at diverse aspects of quantum computing, including architectures, fault tolerance and algorithms that are robust against hardware imperfections. His team created QuEST, a world-leading tool for classical emulation of quantum devices.

Financial data can be sliced cross-sectionally, through time or simultaneously by both curating and provisioning processed data in multidimensional matrix structures—“DataCubes.” Doing so has made it highly efficient to execute the desired types of data manipulations and visualizations in Mathematica.

The project pipeline began by writing custom APIs to extract data locked in silos; legacy procedures were then translated and separated into hundreds of “CustomMetrics” to clean and increase the information content of individual data segments. After the release of Mathematica 12, the project expanded to take advantage of the entity store data framework.

Additional projects have focused, within a Wolfram Language package, on automating the integration and enhancement of data and sequencing the workflow steps across multiple internal and external data sources and applications. Lastly, user experience was vastly improved with the custom development of a GUI to access, examine further and manipulate data while dynamically displaying the visual reports.

Bandyopadhyay’s research involves highly demanding symbolic computations, for which he finds a trusted partner in Mathematica. In at least 20 of his journal publications, the symbolic capabilities of Mathematica have played a significant role. Moreover, the flexibility of Wolfram Language has allowed him to develop algorithms and modules to explore deeper into algebraic geometry and kinematics and create customized tools for analyzing problems using hyper-complex numbers, such as dual numbers and dual quaternions. He uses the dynamic visualization capabilities of Mathematica to bring virtual robots to life, enabling his students to manipulate them and develop a better understanding of complicated motions of constrained multibody systems.

Using the Wolfram technology stack, Picket has conducted interesting work with insights on the points of failure and where inefficient markets exist in the supply chain. Of note, computation techniques used include projecting large amounts of healthcare supply and medication usage data into images. These images become the data fed to repurposed visual neural net training procedures that result in AI/machine learning models that are able to accurately recognize signals that predict future drug shortages.

Working with Wolfram’s Consulting Group, Picket has also verified a derived new class of economic measures, titled the Sutherland measures, made feasible by taking into account special economic qualities and situations of supplied medicines for the generic drug markets.

The text covers traditional topics, such as complex analysis, linear algebra and ordinary and partial differential equations, but explains and illustrates concepts using computer algebra, graphics and numerics. This text has been used for over a decade and includes many figures, animations and live code so students can perform computations while learning course concepts. Student learning requires numerous practice problems with grading and feedback. For a large undergraduate class, this is labor-intensive, so Taborek has developed a web-based platform to deliver homework problems, which are graded using calls to Wolfram Cloud APIs.

Patrick is a highly versatile professional with a wide range of expertise beyond academia. He has worked as a consultant using Mathematica on various projects focused on simulations, modeling and data analyses in diverse domains for companies like Daimler, Procter & Gamble and Dow Chemical. Patrick has been developing and maintaining the Wolfram Language integration for JetBrains IDEs since 2012. His exceptional skills and expertise have led him to join the IntelliJ Platform SDK team at JetBrains. In addition, Patrick has developed several syntax highlighters for Wolfram Language, one of which has been used on the official Mathematica Stack Exchange site, where he is an enthusiastic moderator and member.

In his research, Knill’s primary laboratory is Mathematica. Currently, he is delving deeper into discrete geometry, probability, spectral theory and linear algebra. He is thrilled about uncovering previously undiscovered relationships and enhancing proofs with code. This allows any curious individual to explore the underlying structure. Mathematica code is close to natural language, acting as a runnable pseudocode. While examples can elucidate a theorem, providing code that showcases it using random structures is not only thrilling but also validates the result’s efficacy.

MRI scans provide valuable data, but they need careful processing and analysis. That’s where Froeling’s QMRITools paclet comes in. The paclet is a handy toolkit for experimental design, data analysis and teaching. Since its launch in 2012, it has been used in over 50 scientific papers. Originally created to analyze muscle diffusion-weighted imaging data, QMRITools has expanded its scope. It now includes features like cardiac analysis (including tagging and T1 mapping), Dixon reconstruction, EPG modeling and fitting, J-coupling simulations and more.

The paclet currently offers over 450 custom functions, making it a valuable resource for researchers. Plus, there’s extensive documentation with more than 750 pages, and each toolbox comes with demonstrations. With these tools, Froeling aims to simplify quantitative MRI analysis, benefiting our understanding of muscle injury and disease.

Wolfram Language is foundational to his R&D work, which includes a road condition monitoring system that marries vision-based road defect detection and location with vehicle dynamics and vibration signal processing using edge computation to report road and safety conditions autonomously. Separate devices can communicate and accept instructions from each other for extended inspections.