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🎙 About The Episode

Justin Hodges’ passion for Artificial Intelligence (AI) was ignited in 2017 during an internship at Siemens Healthineers, where he used computational fluid dynamics (CFD) to model airway diseases within the lungs. His experiments fusing AI with CFD tools and workflows produced astonishing results, becoming the central focus of his graduate studies and his AI-focused career at Siemens. Online, Justin is well-known for his fluid dynamics videos on LinkedIn and his appearances as an AI expert on various podcasts. Yet, there is more to Justin than just offering insights on the latest technological trends. He is also an espresso enthusiast, enjoying the process of roasting his own beans and crafting the perfect cup.

Throughout his academic journey at the University of Central Florida, where he earned his bachelor’s, master’s, and Ph.D. degrees, Justin was immersed in a lab affiliated with Siemens Energy. His focus was on Industrial Turbomachinery, exploring fluid mechanics, aerodynamics, turbulence, and heat transfer. An internship with Siemens (formerly CD-adapco) had him working extensively with Simcenter STAR-CCM+ software, specializing in CFD engineering simulations.

Justin’s work with Siemens Healthineers began in 2017, where he worked on pioneering CFD/AI applications for modeling airway diseases, marking his introduction to AI. His enthusiasm for AI grew as he witnessed its efficiency compared to traditional numerical techniques. AI became a central component of his work, and he integrated it into his Ph.D. and various projects at Siemens. His role evolved into a Senior AI/Machine Learning (ML) Technical Specialist in Product Management, reflecting the increasing significance of AI in engineering simulations.

Justin’s work primarily involves CFD, a field that has seen remarkable advancements through the integration of AI and machine learning. These technologies enhance the capabilities of traditional statistical methods, revealing intricate relationships within complex data sets. Justin’s favorite simulation work involves optimization software where machine learning models streamline the process, significantly reducing time and cost.

Beyond his technical expertise, Justin emphasizes the importance of emotional intelligence in tech-heavy roles, advocating for effective collaboration and maintaining work-life balance to avoid burnout. He believes in the value of mentorship, setting ambitious goals, and the significance of networking and building meaningful relationships.

Justin recently published a book titled “Approaching Machine Learning Problems in Computational Fluid Dynamics and Computer Aided Engineering Applications: A Monograph for Beginners.” This practical guide aims to help CFD/CAE practitioners become more comfortable with machine learning projects, sharing his experiences and providing hands-on examples in Python.

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