- 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
Founder & Chairman
Walden International
Lip-Bu Tan is Founder and Chairman of Walden International (“WI”), and Founding Managing Partner of Celesta Capital and Walden Catalyst Ventures, with over $5 billion under management. He formerly served as Chief Executive Officer and Executive Chairman of Cadence Design Systems, Inc. He currently serves on the Board of Schneider Electric SE (SU: FP), Intel Corporation (NASDAQ: INTC), and Credo Semiconductor (NASDAQ: CRDO).
Lip-Bu focuses on semiconductor/components, cloud/edge infrastructure, data management and security, and AI/machine learning.Lip-Bu received his B.S. from Nanyang University in Singapore, his M.S. in Nuclear Engineering from the Massachusetts Institute of Technology, and his MBA from the University of San Francisco. He also received his honorary degree for Doctor of Humane Letters from the University of San Francisco. Lip-Bu currently serves on Carnegie Mellon University (CMU)’s Board of Trustees and the School of Engineering Dean’s Council, Massachusetts Institute of Technology (MIT)’s School of Engineering Dean’s Advisory Council, University of California Berkeley (UCB)’s College of Engineering Advisory Board and their Computing, Data Science, and Society Advisory Board, and University of California San Francisco (UCSF)’s Executive Council. He’s also a member of the Global Advisory Board of METI Japan, The Business Council, and Committee 100. He also served on the board of the Board of Global Semiconductor Alliance (GSA) from 2009 to 2021, and as a Trustee of Nanyang Technological University (NTU) in Singapore from 2006 to 2011. Lip-Bu has been named one of the Top 10 Venture Capitalists in China by Zero2ipo and was listed as one of the Top 50 Venture Capitalists on the Forbes Midas List. He’s the recipient of imec’s 2023 Lifetime of Innovation Award, the Semiconductor Industry Association (SIA) 2022 Robert N. Noyce Award, and GSA’s 2016 Dr. Morris Chang's Exemplary Leadership Award. In 2017, he was ranked #1 of the most well-connected executives in the technology industry by the analytics firm Relationship Science.
- 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