That's a wrap on Rev Philadelphia
From statistical computing to enterprise AI, learn what the organizations getting it right have in common.
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Three themes defined Philadelphia
Governance built in, not bolted on
A new SCE requires new ways of working
Scale means reuse, not just growth
What was covered
Drawing on his tenure as FDA Commissioner and his current work leading Nucleus RadioPharma, Dr. Hahn will share his perspective on how AI is reshaping drug development across clinical trials and regulatory submissions, and what sponsors need to understand as they incorporate AI into regulated workflows.
Nucleus RadioPharma
Enterprises have the models, the talent, and the budgets. What's missing is the operational foundation to turn AI investment into measurable business impact. Hear Domino's view on what's holding regulated enterprises back and what the path forward looks like.
AstraZeneca shares how it's navigating the shift to multi-agent systems, what becomes genuinely hard when agents start talking to each other, and what even mature organizations are working to resolve at scale.
Few executives have a clearer view of where pharma data science is heading than Eric Gibson of Novartis. In this closing fireside chat, he shares a candid perspective on the FDA's shift toward R-based submissions, multimodal research, and why synthetic data may be the key to unlocking AI at scale.
GSK's STAR program tackled open source adoption with an unconventional approach: a team of R experts who said yes to every inquiry. This session traces the journey from legacy constraints to measurable progress, including the governance, training, and support structures that made the shift stick.
Modernizing a statistical computing environment requires more than a technology upgrade. UCB shares its approach to evolving a legacy SCE within a regulated context, including what it took to pack up technical debt, contain scope, and put the right foundations in place across people, process, and IT.
At BMS, data science and AI are embedded end-to-end, from early discovery to clinical development and commercialization. Learn how Domino enables teams to move from Python notebooks to production AI, scaling collaboration and impact across the enterprise.
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A top 10 pharma shares how it's moving from manual, fragmented clinical analytics to a unified statistical computing environment on Domino, reducing human error, maintaining auditability across SAS, R, and Python, and supporting regulated, day-to-day statistical programming.
In this joint session, Domino and Posit will show how Positron integrates with Domino's governed environment, covering the full code lifecycle for both R and Python, package management with Posit Package Manager, and how Positron's native AI tools augment statistical programmers and data scientists in the ways they already work.
Thomas Mock, PhD | Senior Product Manager | Posit
Modernizing a statistical computing environment requires navigating roadmap decisions to fit new capabilities into a complex clinical ecosystem. Priya Subramaniam will share practical lessons from Novartis’ SCE journey on extending platforms to meet organization’s needs, scaling adoption, and addressing validation and QC - delivering speed and flexibility without compromising regulatory rigor.
Mike Harnish | President & Managing Partner | KSM Technology Partners
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