Check out this select sampling of some of the phenomenal SAS and open source presentations, demos, and workshops that will be offered at WUSS 2025 by technical experts and industry leaders!
More to come as the conference nears, with the complete list of presentations showcased on the WUSS website in August.
SAS® Data-Driven Software Design: How to Develop More Modular, Maintainable, Fixable, Flexible, Configurable, Compatible, Reusable, Readable Software through Independent Control Tables and Other Control Data
Troy Martin Hughes, Data Llama Analytics
Data-driven design describes software design in which the control logic, program flow, business rules, data models, data mappings, and other dynamic and configurable elements are abstracted to control data that are interpreted by (rather than contained within) code. Thus, data-driven design leverages parameterization and external data structures (including configuration files, control tables, decision tables, data dictionaries, business rules repositories, and other control files) to produce dynamic software functionality. This hands-on workshop introduces real-world scenarios in which the flexibility, configurability, reusability, and maintainability of SAS® software are improved through data-driven design methods, as introduced in the author’s 2019 book: SAS Data-Driven Development: From Abstract Design to Dynamic Functionality. This white paper highlights one of these scenarios, in which malleable comma-separated values (CSV) files—where the variables and their order can vary—are ingested with the aid of a data dictionary control table.
Charting Your AI Journey: A Roadmap for Supervised, Unsupervised, and Generative Learning through Machine Learning and Deep Learning
Ryan Paul Lafler, Premier Analytics Consulting, LLC
Miguel Angel Bravo Martinez del Valle, Premier Analytics LLC
Machine learning (ML) continues to reshape business, technology, science, and research across all industries, with its adoption enabling systems to learn from data, automate decisions, and generate insights. This paper presents a structured roadmap through three core domains of machine learning that are increasingly adopted by organizations: supervised, unsupervised, and generative learning. Along this roadmap, readers will identify key algorithms and architectures within each domain and understand the role of parameters and hyperparameters in mitigating overfitting and underfitting. The discussion includes examples of predictive modeling on labeled data using supervised algorithms, knowledge discovery from unlabeled data using unsupervised algorithms, and the extension of these capabilities through generative learning, which enables systems to extract insights and produce new content or data representations. The paper concludes by introducing three generative architectures that define the state of AI in 2025: encoder models (BERT), decoder models (LLMs), and encoder-decoder models (T5), and describes how each supports advanced AI tasks including representation learning, language generation, natural language processing (NLP), text summarization, and translation.
Getting Started with SAS® Visual Analytics
Danny R Modlin, SAS
This workshop shows you how to explore and visualize data with SAS® Visual Analytics. You learn how to access and prepare data, how to create interactive charts, reports, and maps to learn about your data, and how to build a preliminary predictive model.
You’ve Got Options: Five-Star SAS® System Option Hacks
Louise S Hadden, Cormac Corporation
SAS® provides myriad opportunities for customizing programs and processes, including a wide variety of system options that can control and enhance SAS code from start to finish. This paper and presentation demonstrates methods of obtaining information on SAS system options, and moves on to fully explicate ten SAS system option hacks, from COMPRESS to VALIDVARFMT. System options are highly dependent on platforms, security concerns, SAS versions and products: dependencies and defaults will be discussed. SAS practitioners will gain a deeper understanding of the powerful SAS system options they’ve seen, used, and automatically included in their code. This presentation is suitable for all skill and experience levels; platform differences are part of the discussion.
From Muggles to Macros: Transfiguring Your SAS Programs with Dynamic, Data-Driven Wizardry
Josh Horstman, PharmaStat LLC
Richann Jean Watson, DataRich Consulting
The SAS® macro facility is an amazing tool for creating dynamic, flexible, reusable programs that can automatically adapt to change. This presentation uses a series of examples to demonstrate how to transform static “muggle” code full of hardcodes and data dependencies by adding macro language magic to create data-driven programming logic. Don’t hardcode data values into your programs. Cast a vanishing spell on data dependencies and let the macro facility write your SAS code for you!
More Muggles, More Macros: Adding Advanced Data-Driven Wizardry to Your SAS® Programs
Josh Horstman, PharmaStat LLC
Richann Jean Watson, DataRich Consulting
In their popular 2024 presentation “From Muggles to Macros”, Horstman and Watson delivered a spell book full of macro magic to enhance SAS programs with data-driven wizardry. This exciting sequel to that enchanting performance adds to the list of incantations for creating dynamic, flexible, reusable programs that can automatically adapt to change. New charms include the use of control tables, the CALL EXECUTE routine, and of course, more macro language techniques. Don’t hardcode data values into your programs. Cast a vanishing spell on data dependencies and let the macro facility write your SAS code for you!
Prompt, Program, Submit: Generative AI for Faster SDTM, ADaM, and TLFs
Matt Becker, SAS
The life sciences industry is seeing more requests for quick, compliant clinical trial submissions, which makes it even more important to improve programming operations. Generative artificial intelligence, especially large language models (LLMs), could change the way SDTM, ADaM, and TLF are created in a big way.
This session will investigate the practical applications of generative AI to automate and enhance critical clinical programming duties. From the mapping of raw data to SDTM domains to the crafting of ADaM specifications and the generation of boilerplate code or statistical summaries, we will analyze real-world use cases. These examples will demonstrate how to reduce manual effort while ensuring traceability and compliance. Additionally, we will illustrate the integration of these AI-driven procedures into SAS environments to improve productivity without compromising regulatory compliance.
Whether you’re a statistical programmer, data manager, or biostatistician, this session will help you reimagine what’s possible when human expertise meets machine intelligence.