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  • Integrating AI and decision science to design faster, smarter, lower-risk trials
  • Using adaptive methods to boost efficiency and responsiveness in trial design
  • Turning complex data into insights that accelerate timelines and improve outcomes

Author:

Tom Oliver

Head of Product
Faculty AI

Tom Oliver is Head of Product at Faculty, where he leads the strategy and roadmap for Faculty Frontier™, the company’s decision-intelligence platform that helps enterprises operationalize AI in day-to-day decisions. Tom partners with executives and cross functional teams, including product managers, engineers, data scientists, and designers, to discover, build, and scale products that solve real problems for technical and business users. He collaborates closely with Faculty’s leadership and with key stakeholders and clients across pharma, retail, consumer goods, and healthcare, turning complex challenges into measurable outcomes. Before joining Faculty, Tom held leadership positions at PwC, focusing on technology enabled transformation, operating model change, and enterprise technology selection and deployment. His public writing explores the future of work, AI ethics, and human centric design, asking how powerful systems can advance human flourishing, not only efficiency. He holds a degree from the University of Oxford.

Tom Oliver

Head of Product
Faculty AI

Tom Oliver is Head of Product at Faculty, where he leads the strategy and roadmap for Faculty Frontier™, the company’s decision-intelligence platform that helps enterprises operationalize AI in day-to-day decisions. Tom partners with executives and cross functional teams, including product managers, engineers, data scientists, and designers, to discover, build, and scale products that solve real problems for technical and business users. He collaborates closely with Faculty’s leadership and with key stakeholders and clients across pharma, retail, consumer goods, and healthcare, turning complex challenges into measurable outcomes. Before joining Faculty, Tom held leadership positions at PwC, focusing on technology enabled transformation, operating model change, and enterprise technology selection and deployment. His public writing explores the future of work, AI ethics, and human centric design, asking how powerful systems can advance human flourishing, not only efficiency. He holds a degree from the University of Oxford.

Author:

Tarun Walia

Senior Director, Decision Science
Novo Nordisk

Tarun Walia

Senior Director, Decision Science
Novo Nordisk

Author:

Stephanie Vakaljan

Director of Trial Analytics and Decision Support
GSK

Stephanie Vakaljan

Director of Trial Analytics and Decision Support
GSK

Learn how AI models enhance physics-based simulations to predict molecular interactions and optimize drug design.
Discover the synergy between machine learning and classical methods to accelerate screening and improve the accuracy of drug discovery.

Author:

Sreyoshi Sur

Former Scientist, Molecular Engineering & Modeling
Moderna

Sreyoshi Sur

Former Scientist, Molecular Engineering & Modeling
Moderna

Explore how AI enhances biomarker discovery by analyzing large datasets to uncover novel biomarkers for disease diagnosis and therapeutic efficacy.
Learn how integrating digital biomarkers with AI improves the interpretation of data from wearable devices and traditional lab-based biomarkers for better patient stratification and treatment personalization.

Author:

Satarupa Mukherjee

R&D Leader, AI/ML (Digital Pathology)
Roche

Satarupa Mukherjee

R&D Leader, AI/ML (Digital Pathology)
Roche

Author:

Jack Geremia

CEO
Matterworks

Jack Geremia

CEO
Matterworks

Author:

Virginia Savova

Senior Director, Head Cell-Targeted Precision Medicine
AstraZeneca

Virginia Savova

Senior Director, Head Cell-Targeted Precision Medicine
AstraZeneca

Examine how AI models are being developed, validated, and governed to meet regulatory expectations, with practical insights into documentation, auditability, and lifecycle management to ensure safe, transparent, and compliant deployment in GxP environments.

Author:

Benjamin Stevens

Director, CMC Policy
GSK

Benjamin Stevens

Director, CMC Policy
GSK

Explore h ow AI models predict protein 3D structures from sequences, enabling insights into folding pathways and functional conformations
Examine emerging co-folding models that reveal protein–protein interactions and guide multimeric complex design.

Author:

Miles Congreve

Chief Scientific Officer
Isomorphic Labs

Miles Congreve

Chief Scientific Officer
Isomorphic Labs

Learn how AI-driven approaches integrate multiomics data, including genomics, proteomics, and transcriptomics, to identify potential drug targets and disease biomarkers for complex diseases.
Explore how AI models synthesize cross-omic data and real-time multiomic information to uncover novel biological mechanisms, identify potential biomarkers and enable precision medicine.

Moderator

Author:

Eva Fast

Senior Principal Computational Biologist
Pfizer

Eva Fast

Senior Principal Computational Biologist
Pfizer

Author:

Kiran Nistala

Head, Functional Genomics
Alkermes

Kiran Nistala

Head, Functional Genomics
Alkermes

Author:

Harris Bell-Temin

Director, Proteomics
Johnson & Johnson Innovative Medicine

Harris Bell-Temin

Director, Proteomics
Johnson & Johnson Innovative Medicine
Moderator

Author:

David Champagne

Senior Partner
McKinsey & Company

David Champagne

Senior Partner
McKinsey & Company

Author:

Melissa Landon

Head, Commercial & Business Development, AI & Automation
MilliporeSigma

Melissa Landon

Head, Commercial & Business Development, AI & Automation
MilliporeSigma

Author:

David Hallett

Chief Scientific Officer
Recursion

David Hallett

Chief Scientific Officer
Recursion

Author:

Morten Sogaard

Senior Vice President & Head, Astellas Innovation Lab
Astellas Pharma

Morten Sogaard

Senior Vice President & Head, Astellas Innovation Lab
Astellas Pharma

Author:

Peter Clark

Vice President, Digital Chemistry & Design
Novo Nordisk RDUS

Peter Clark

Vice President, Digital Chemistry & Design
Novo Nordisk RDUS

Demonstrate how AI-driven initiatives - like predictive modelling and automated inspection -translate into measurable outcomes (e.g., defect reduction, shorter batch release cycles) that justify capital investment and cross-functional prioritization.