Pfizer/ASA/Columbia University Symposium on AI in Clinical Drug Development

The Pfizer/ASA/Columbia University Symposium on Risks and Opportunities of AI in Clinical Drug Development, which is jointly sponsored by Pfizer Inc., the American Statistical Association and the Statistics Department and Data Science Institute at Columbia University, has been postponed until 2021. Details forthcoming. 

The symposium is intended to serve as a platform for distinguished statisticians, data scientists, regulators, and other professionals to address the challenges and opportunities of AI in pharmaceutical medicine; to foster collaboration among industry, academia, regulatory agencies, and professional associations; and to propose recommendations with policy implications for proper implementation of AI in promoting public health.

Featured speakers include Dr. Amy P. Abernethy, Principal Deputy Commissioner of the US FDA, and Professor van der Schaar, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge and Turing Faculty Fellow at The Alan Turing Institute in London.

Details of the symposium, including registration, the program and speakers’ profiles, can be found in the following link: http://aipm.stat.columbia.edu/.

Space is limited, so please register early to get a spot! We look forward to seeing you at the symposium.

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Demissie Alemayehu, Pfizer Inc, and David Madigan, Columbia University

Co-Chairs

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Before data analysis: Additional recommendations for designing experiments to learn about the world.
Andrew Gelman (2023).
Pathfinder: Parallel quasi-Newton variational inference.
Andrew Gelman (2022).
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Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery. In International Conference on Machine Learning (pp. 9311-9323). PMLR.
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Multivariate rank-based distribution-free nonparametric testing using measure transportation. Journal of the American Statistical Association, (just-accepted), 1-45.
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Rush, C. (2020, June).
Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.
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Li, H., Aue, A., Paul, D., Peng, J., & Wang, P. (2020).
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A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis. Journal of the American Statistical Association, (just-accepted), 1-39.
Gu, Y., & Xu, G. (2021).
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The continuous categorical: a novel simplex-valued exponential family. In International Conference on Machine Learning (pp. 3637-3647). PMLR.
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