Location and Times:
Dugan Hall 010
9:30 - 10:45am (Session 1)
11:00am - 12:15pm (Session 2)

Abstract:
Many data scientists rely on automated pipelines to streamline predictive modeling and machine learning workflows. But did you know how easy it is to incorporate Python models into these pipelines using SAS software?  This integration allows you to get the best of both worlds—leveraging the robustness and scalability of SAS Viya alongside the flexibility and innovation of open-source tools to seamless compare, review, and crown a champion model. Join us to explore how open-source models can fit into an end-to-end machine learning process, enabling efficiency, reproducibility, and scalability.

Speaker:
Rachel McLawhon, Global Academic Program Analytical Training Consultant, SAS Institute
Rachel holds a B.S. in Mathematics and both a B.S. and M.Ed. in Mathematics Education from North Carolina State University. She began her work with SAS in May 2016 as an intern, where she assisted with development of technical support documentation, marketing material, and K-12 learning software. During Fall 2019, Rachel completed SAS’ Technical Enablement Academy, where she spent 17 weeks learning numerous SAS technologies from development to deployment to administration. In her role with the SAS Global Academic Program, she assists in the creation of diverse learning materials engaging academics with SAS software and is an analytical trainer, specializing in Base SAS programming, Visual Analytics, and open-source integration.

Rachel holds a B.S. in Mathematics and both a B.S. and M.Ed. in Mathematics Education from North Carolina State University. She has been with SAS since 2016, and in her current role with the SAS Global Academic Program, she assists in the creation of diverse learning materials engaging academics with SAS software and is an analytical trainer, specializing in Base SAS programming, Visual Analytics, and open-source integration.

Location and Times:
Dugan Hall 346
4:00 - 5:00pm

Abstract:
Operations Research and Management Science (ORMS) may have begun as a highly technical field rooted in mathematics and engineering, but today it underpins what we call data analytics and data science—and it is no longer reserved for specialists. Modern tools, including generative AI, have lowered the barriers so that anyone, regardless of major or background, can use data to describe patterns, predict outcomes, and prescribe better decisions. In fact , employers across every industry now expect graduates to have this capability, whether they are in marketing, finance, supply chain, or even the arts. Today, we also live in a moment of unprecedented opportunity: at our fingertips are tools that not only help us succeed in our careers but also empower us to serve and love others, create data-driven solutions to social challenges, and make generational impacts that extend far beyond ourselves. This talk traces ORMS from its origins in World War II logistics to its modern role in business and society, showing how its core principles—descriptive, predictive, and prescriptive analytics—shape decisions that affect millions. Through historical stories and contemporary examples, students will see how ORMS has evolved into a practical skillset that enables both professional success and meaningful contributions to the world.

Speaker:
Matthew Lanham, Assistant Professor of Business Technology & Analytics, Butler University

https://matthewalanham.github.io/

Meet Professor Lanham in the first floor of Dugan Hall 4:00-:5:30pm at the LSB Clubs Fair. Our club is OPEN TO ALL MAJORS!