Demystifying Data Analytics
How to Accelerate AI Adoption for Industrial Processes
The WKU Center for Applied Data Analytics is delighted to launch the Data Science Awareness Webinar Series presented in collaboration with Altair.
The goal of the series is to promote awareness among faculty and staff on data analytics and artificial intelligence tools and applications. The three-session series covers topics including Demystifying Data Analytics, How to Accelerate AI Adoption for Industrial Processes, and Unlocking AI/ML Potential for Engineering Designs & Manufacturing Simulations.
The opportunity is at no charge for WKU faculty, staff, and graduate students. To take advantage of this complimentary series, register now and secure your place. Click here: https://web.altair.com/wku-data-analytics-awareness-series-with-altair
Demystifying Data Analytics
Presenter: Harrison Murphy - Director of Data Analytics Solutions, Specialists, Analytics & IoT
Abstract: Machine Learning, Artificial Intelligence, and Data Science have never been more accessible or as widely proliferated in day-to-day life as they currently are and will continue to be for the near future. I would also argue that it's never been as widely misunderstood as it is currently. From Generative AI and Large Language Models like ChatGPT to AI-generated art and self-driving cars, it's easy to think of AI and ML as this unobtainable, all-encompassing, futuristic wonder technology when the reality can be simpler, more practical, and more tangible than previously considered.
We want to pull back the curtain and demystify what a typical data science process can look like from a non-technical user's perspective to bridge the gap between theory and practical application.
In this talk we're addressing:
How to Accelerate AI Adoption for Industrial Processes
Presenter: Scott Genzer - Senior Data Scientist
Abstract: With AI being the buzzword across every company and every business executive’s desk in the USA and abroad, the proverbial “elephant in the room” remains the same: remarkably few AI projects ever make it to the factory floor. And even fewer produce real business value to the company’s bottom line. So how can we learn from the past and help companies, particularly in laggard verticals such as industrial manufacturing, beat the odds? The answer is not simple but in this talk, we attempt to answer it with the following guiding questions:
Presenter: Jaideep Bangal – Director of Simulation & Design
Abstract: As digital engineering advances, modeling, and simulation will converge with machine learning, AI, and high-performance computing (HPC) in solving the world’s most complex problems. PhysicsAI is a new tool designed to predict physics outcomes quickly. This technology leverages historical simulation data to deliver fully animated physics predictions in a fraction of the time it takes traditional solvers to do the same. Unlike previous machine learning technologies, PhysicsAI uses cutting-edge geometric deep learning to operate directly on meshes and CAD models, which generates even faster results.
During this presentation, we discuss:
Some of the links on this page may require additional software to view.