- Understanding the value of combing of real-world data and historic clinical trial data
- How can AI be used to analyse post approval data?
Eric Sarpong
Director, Real-World Data Analytics
Merck
The true potential of AI rests on super-human learning capacity, and on the ability to selectively draw on that learning. Both of these properties – scale and selectivity – challenge the design of AI computers and the tools used to program them. A rich pool of new ideas is emerging, driven by a new breed of computing company, according to Graphcore co-founder Simon Knowles. At the AI Hardware Summit, Phil Brown, VP Scaled Systems Product discusses the creation of the Intelligence Processing Unit (IPU) – a new type of processor, specifically designed for AI computation. He looks ahead, towards the development of AIs with super-human cognition, and explores the nature of computation systems needed to make powerful AI an economic everyday reality.
Developer Efficiency
Enterprise AI
ML at Scale
Novel AI Hardware
Systems Design
Data Science
Hardware Engineering
Software Engineering
Strategy
Systems Engineering
Phil Brown
VP, Scaled Systems Product
Graphcore
Phil leads Graphcore’s efforts to build large scale AI/ML processing capability using Graphcore unique Intelligence Processing Units (IPUs) and IPU-Fabric and Streaming Memory technology. Previously he has held a number of different roles at Graphcore including Director of Applications, leading development of Graphcore’s flagship AL/ML models, and Director of Field Engineering, which acts as the focal point for technical engagements with our customers. Prior to joining Graphcore, Phil worked for Cray Inc. in a number of roles, including leading their engagement with the weather forecasting and climate research customers worldwide and as a technical architect. Phil holds a PhD in Computational Chemistry from the University of Bristol.
Graphcore
Website: https://www.graphcore.ai/
We’re optimistic for a future where people live healthier, more informed, more creative lives. We see a world where technology enhances human potential, and takes us into a new era of intelligence and progress that everyone can benefit from.
We let innovators create the next breakthroughs in machine intelligence.
We believe our Intelligence Processing Unit (IPU) technology will become the worldwide standard for machine intelligence compute. The Graphcore IPU is going to be transformative across all industries whether you are a medical researcher, roboticist or building autonomous cars.
We have created a completely new processor, the IPU, specifically designed for AI compute. The IPU’s unique architecture lets AI researchers undertake entirely new types of work, not possible using current technologies, to drive the next advances in machine intelligence.
- Highlighting the ethical and practical challenges of accessing patient data and how best to overcome shared challenges
- Developing trust across those involved in the formation of real-world data
- De-identify personal information using a risk-based approach and putting valuable data in the hands of data scientists
Aaron Mann
Co-Founder and CEO
Clinical Research Data Sharing Alliance
Peter Mesenbrink
Executive Director Statistician
Novartis
Rebecca Li
Executive Director
Vivli
Julie Holtzople
Senior Director of Clinical Transparency & Data Sharing
AstraZeneca
- Predictive approaches to treatment effect heterogeneity (PATH), and offer the potential to identify predictive biomarkers, and to understand which treatment a patient may be more likely to benefit from
- The two primary classes of PATH models include risk models and more flexible effect models, with a proliferation of newly published approaches in recent years
- Limitations of these approaches such as how to appropriately evaluate their accuracy are an area of active research
David Paulucci
Director of Data Science
Bristol-Myers Squibb
Chip Design
Edge AI
Enterprise AI
ML at Scale
NLP
Novel AI Hardware
Data Science
Hardware Engineering
Software Engineering
Strategy
Systems Engineering
Industry & Investment
Lip-Bu Tan
CEO
Intel
Lip-Bu Tan is chief executive officer of Intel Corporation and serves on the company’s board of directors. He was appointed to his position in March 2025.
Tan is an accomplished executive with more than two decades of semiconductor and software experience and deep relationships across the technology ecosystem. He has received several accolades for his significant contributions to the industry, including the 2022 Robert N. Noyce Award, the Semiconductor Industry Association’s highest honor, and was named one of Forbes’ Top 50 Venture Capitalists.
Tan previously served as chief executive officer of Cadence Design Systems Inc. and was also a member of its board of directors. During his 12 years as Cadence’s chief executive officer, he led a reinvention of the company and drove a cultural transformation centered on customer-centric innovation that enabled Cadence to more than double its revenue, expand operating margins and significantly outperform the market.
Tan is a founding managing partner of Walden Catalyst Ventures and chairman of Walden International, a leading venture capital firm. He has also served on the boards of public companies Credo Technology Group and Schneider Electric.
Tan holds a Bachelor of Science in physics from Nanyang Technological University in Singapore, a Master of Science in nuclear engineering from the Massachusetts Institute of Technology and an MBA from the University of San Francisco.
- Exploring the digital endpoint development process with a case-study
- Measuring what matters; identifying and addressing needs. Dealing with concerns around development, validation, regulatory concerns
- Putting endpoints to use via deployment in clinical trials
Yiorgos Christakis
Senior Data Scientist, Early Clinical Development
Pfizer
- Using AI to evaluate the demographic population and trial operational performance trade offs of exclusion of non-safety required chronic conditions in the eligibility criteria
- Benchmarking and differentiating between Schedule of Assessment(SoA) procedures relative to different time periods and peer groups to drive greater operational efficiency
Michael Dandrea
Principal Data Scientist
Genentech
- Assessing representativeness of randomized clinical trials using ML fairness metrics and surveillance data
- Using these metrics to assess and monitor representativeness of clinical trials
- New tools for designing representative and more efficient single and multi-site trials
Kristin Bennett
Professor of Mathematical Sciences
Rensselaer Polytechnic Institute
- Assessing representativeness of randomized clinical trials using ML fairness metrics and surveillance data
- Using these metrics to assess and monitor representativeness of clinical trials
- New tools for designing representative and more efficient single and multi-site trials
Kristin Bennett
Professor of Mathematical Sciences
Rensselaer Polytechnic Institute