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  • 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

Author:

Michael Dandrea

Principal Data Scientist
Genentech

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

Author:

Kristin Bennett

Professor of Mathematical Sciences
Rensselaer Polytechnic Institute

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

Author:

Kristin Bennett

Professor of Mathematical Sciences
Rensselaer Polytechnic Institute

Kristin Bennett

Professor of Mathematical Sciences
Rensselaer Polytechnic Institute
  • Opportunities for improving efficiency of clinical research and development
  • Combining information technology, statistics, and data science to address challenging problems in drug development
  • Integrated scientific learning approach

Author:

Alex Sverdlo

Senior Director, Statistical Scientist
Novartis

Alex Sverdlo

Senior Director, Statistical Scientist
Novartis
  • Addressing the technical, cultural and policy hurdles that traditionally impede the broad access and usage of clinical trial data
  • Layout a blueprint for companies to accelerate their progress in a patient-first manner
  • Developing a framework to automatically approval clinical trial data requests
  • Exploring the impact this has had on Novartis and the broader industry

Author:

Gabriel Eichler

Vice President of Data, Data 42
Novartis

Ech

Gabriel Eichler

Vice President of Data, Data 42
Novartis

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Banu Nagasundaram

AI Product Leader
Amazon Web Services (AWS)

Banu Nagasundaram

AI Product Leader
Amazon Web Services (AWS)

Banu Nagasundaram

AI Product Leader
Amazon Web Services (AWS)
Neil Thompson Interview
 

Kunle Olukotun

Chief Technologist & Co-Founder
SambaNova Systems

Kunle Olukotun is the Cadence Design Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is a renowned pioneer in multi-core processor design and the leader of the Stanford Hydra chip multiprocessor (CMP) research project.

Prior to SambaNova Systems, Olukotun founded Afara Websystems to develop high-throughput, low-power multi-core processors for server systems. The Afara multi-core processor, called Niagara, was acquired by Sun Microsystems and now powers Oracle’s SPARC-based servers.

Kunle Olukotun

Chief Technologist & Co-Founder
SambaNova Systems

Kunle Olukotun

Chief Technologist & Co-Founder
SambaNova Systems

Kunle Olukotun is the Cadence Design Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is a renowned pioneer in multi-core processor design and the leader of the Stanford Hydra chip multiprocessor (CMP) research project.

Prior to SambaNova Systems, Olukotun founded Afara Websystems to develop high-throughput, low-power multi-core processors for server systems. The Afara multi-core processor, called Niagara, was acquired by Sun Microsystems and now powers Oracle’s SPARC-based servers.

Olukotun is the Director of the Pervasive Parallel Lab and a member of the Data Analytics for What’s Next (DAWN) Lab, developing infrastructure for usable machine learning.

Olukotun is an ACM Fellow and IEEE Fellow for contributions to multiprocessors on a chip and multi-threaded processor design. Olukotun recently won the prestigious IEEE Computer Society’s Harry H. Goode Memorial Award and was also elected to the National Academy of Engineering—one of the highest professional distinctions accorded to an engineer.

Kunle received his Ph.D. in Computer Engineering from The University of Michigan.

Q&A with Opaque