Join the conversation with William Thielicke, the developer of PIVlab, as he shares insights into the world of particle image velocimetery (PIV) and its applications. Discover how PIV accurately measures fluid velocities, non invasively revolutionising research across the industries. Delve into the development journey of PI lab, including collaborations, key features and future advancements for aerodynamic studies, explore the advanced hardware setups camera technologies, and educational prospects offered by PIVlab, for enhanced fluid velocity measurements. If you are interested in the hardware he speaks of check out the company: Optolution.

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In this episode, we dive into the fascinating world of scientific computing with Mark Kittisopikul, who has a background in computational biology. He shares insight into his career journey. And the critical role of scientific computing and modern research. Scientific computing involves leveraging computers to analyze interpret experimental data, bridging the gap between theoretical models and real world outcomes. Despite his non-computer science background, Mark helps his colleagues in biology. And other fields with computational tasks emphasizing the importance of data interpretation. He takes us through his journey, experimenting with various programming languages before discovering Julia. Which he now prefers for sufficiency in ease of use, especially in scientific applications.



Mark illustrates the benefits of Julia with the specific challenge he faced managing data from 10 cameras in a very specific microscope. Showcasing how Julia provided the solution by being able to parse gigabytes of data per second. We also explored the vibrant community around Julia and the upcoming JuliaCon conference highlighting the inclusive nature of the community and the opportunities for calibration and growth. So join us as we uncover the pivotal role of scientific computing in modern research and the advantages of using Julia for computational tasks in biology and beyond.

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In this episode, Albert recounts his journey from Nakhodka Russia to the CEO of a Dutch company Lazy Dynamics. He describes his academic trajectory from studying in St. Petersburg. To earning scholarship and master programs in Kyoto, Japan. There he focused on , developing driving aids for elderly drivers, but face challenges with system performances, leading him to pursue a PhD in Bayesian Inference. Albert explains Bayesian inference as a method for updating beliefs, about uncertain quantities based on new evidence. He discusses its applications and addressing uncertainty in complex systems like personalized. Just hearing it, the conversation touches on the differences between patient AI and reinforcement learning, I'll but also introduces RxInfer and for an open source toolbox programmed in Julia designed to automate Bayesian Inference through reactive message passing. He emphasizes RxInfer and its efficiency in handling computational resources by processing information only when necessary.

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