Data powers the engine of artificial intelligence (AI), but not all data is created equal. While software capabilities are rapidly accelerating, the biopharma industry still largely lacks the intelligent hardware needed to generate clean, contextualized, and high-quality data that AI and machine learning (ML) models require to deliver on their promise.
Explore how today’s in silico process development tools for bioreactor scaling and mechanistic modeling of chromatography can help you get it right the first time now, and why the next generation of intelligent hardware will be critical to unlocking the full potential of AI/ML in biopharma.
Tobias Hahn, PhD
Tobias Hahn is R&D Director of chromatography mechanistic modeling activities at Cytiva. As former co-founder and CEO of GoSilico, now part of Cytiva, Tobias is responsible for delivering simulation software and workflows for in silico process development. He received his undergraduate education in computational mathematics and technical physics in Karlsruhe and Stockholm, earning his PhD in chemical engineering from Karlsruhe Institute of Technology (KIT). During his doctoral studies, he utilized his background in mathematics and software engineering to create the simulation software now known as GoSilico™ chromatography modeling software.
Cilon Li
Cilon Li is a digital and IT executive with over 15 years of experience in healthcare and biopharma. He is a strategic leader with a proven track record in driving digital transformation across supply chain management, product management, and R&D. At Cytiva, Cilon drives the company’s digital strategy and expanding product portfolio, encompassing Internet of Things (IoT), data analytics, AI/ML, software as a service (SaaS) and enterprise applications to help customers progress their digital biomanufacturing journeys.