11:00 am – 12:30 pm EDT

In these unprecedented times quantitative investors are asking what data are available for tracking the spread and impact of COVID-19, from different sources, issues about data curation and reliability, and issues that arise when developing meta-analysis of data from different sources for the purposes of forecasting and of identifying causal relations.

• Nicholas Christakis, Yale University
• David Madigan, Northeastern University
• Bin Yu, UC Berkeley

12:30 pm – 2:00 pm EDT

In Tech, Finance, and Big Pharma alike, techniques from machine learning, to artificial intelligence, to data science, promise to revolutionize how operations are run, forecasts are made, and vaccines discovered. Several CEOs have been warning the public about AI taking over the world. Despite all the hype, real results have been lagging. This panel will discuss a realistic role for techniques from machine learning to data science, and what we can expect they will bring in the near and distant future.

• David Donoho, Stanford University
• Michael Jordan, UC Berkeley
• Donald Rubin, Temple University and Harvard University

2:00 pm – 3:30 pm EDT

A review of statistical and machine learning ideas and methodologies that promise to impact the practice of systematic investing.

• Emmanuel Candes, Stanford University
• Jun Liu, Harvard University
• Panos Toulis, University of Chicago Booth

• Jayson S. Jia, Xin Lu, Yun Yuan, Ge Xu, Jianmin Jia, and Nicholas A. Christakis. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, vol. 582, pp. 389-394, 2020.
• Eric M. Feltham, Laura Forastiere, Marcus Alexander, and Nicholas A. Christakis. No increase in COVID-19 mortality after the 2020 primary elections in the USA. arXiv no. 2010.02896, 2020.
• Nicholas A. Christakis. Apollo's Arrow. Little, Brown Spark, 2020.
• Hunala
• Nick Altieri, Rebecca L. Barter, James Duncan, Raaz Dwivedi, Karl Kumbier, Xiao Li, Robert Netzorg, Briton Park, Chandan Singh, Yan Shuo Tan, Tiffany Tang, Yu Wang, Chao Zhang, and Bin Yu. Curating a COVID-19 data repository and forecasting county-level death counts in the United States. Harvard Data Science Review, Special Issue on COVID-19, 2020.
• Raaz Dwivedi, Yan Shuo Tan, Briton Park, Mian Wei, Kevin Horgan, David Madigan, and Bin Yu. Stable discovery of interpretable subgroups via calibration in causal studies. arXiv no. 2008.10109, 2020.
• Martijn J Schuemie, Patrick B Ryan, Nicole Pratt, RuiJun Chen, Seng Chan You, Harlan M Krumholz, David Madigan, George Hripcsak, and Marc A Suchard. Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND). Journal of the American Medical Informatics Association, vol. 27, pp. 1331–1337, 2020.
• Martijn J Schuemie, Patrick B Ryan, Nicole Pratt, RuiJun Chen, Seng Chan You, Harlan M Krumholz, David Madigan, George Hripcsak, and Marc A Suchard. Large-scale evidence generation and evaluation across a network of databases (LEGEND): assessing validity using hypertension as a case study. Journal of the American Medical Informatics Association, vol. 27, pp. 1268–1277, 2020.
• Martijn J. Schuemie, Patrick B. Ryan, George Hripcsak, David Madigan, and Marc A. Suchard. A systematic approach to improving the reliability and scale of evidence from health care data. arXiv no. 1803.10791, 2018.
• The Observational Health Data Sciences and Informatics.

• David Donoho. 50 Years of Data Science. Journal of Computational and Graphical Statistics, vol. 26, pp. 745–766, 2017.
• Michael I. Jordan. Artificial Intelligence—The Revolution Hasn’t Happened Yet. Harvard Data Science Review, vol. 1, 2019.
• Benedikt Bauer and Michael Kohler. On deep learning as a remedy for the curse of dimensionality in nonparametric regression. Annals of Statistics, vol. 47, pp. 2261–2285, 2019.
• Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal. Reconciling modern machine-learning practice and the classical bias–variance tradeoff. Proceedings of the National Academy of Sciences, vol. 116, pp. 15849–15854, 2019.
• Leo Breiman. Statistical Modeling: The Two Cultures. Statistical Science, vol. 16, pp. 199-231, 2001. (with discussion)
• Bradley Efron. Prediction, Estimation, and Attribution. Journal of the American Statistical Association, vol. 115, pp. 636–677, 2020. (with discussion)
• Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, vol. 521, pp. 436–444, 2015.
• Latanya Sweeney. That’s AI?: A History and Critique of the Field. Carnegie Mellon Technical Report CMU-CS-06-1063, 2003.

• Variable Selection with Knockoffs.
• John Cherian and Lenny Bronner. How The Washington Post Estimates Outstanding Votes for the 2020 Presidential Election. Washington Post Tech Report, 2020. (PDF) Cherian and Bronner WP TechRep 2020
• Chenguang Dai, Buyu Lin, Xin Xing, and Jun S. Liu. False Discovery Rate Control via Data Splitting. arXiv no. 2002.08542, 2020.
• Roger Koenker and Gilbert Bassett Jr. Regression Quantiles. Econometrica, vol. 46, pp. 33-50, 1978.
• Yaniv Romano, Evan Patterson, and Emmanuel J. Candès. Conformalized Quantile Regression. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019. (Code: https://sites.google.com/view/cqr)
• Yaniv Romano, Matteo Sesia, and Emmanuel J. Candès. Classification with Valid and Adaptive Coverage. arXiv no. 2006.02544, 2020.
• Azeem Shaikh and Panos Toulis. Randomization Tests in Observational Studies with Staggered Adoption of Treatment. arXiv no. 1912.10610, 2019.
• Panos Toulis. Life After Bootstrap: Residual Randomization Inference in Regression Models. arXiv no. 1908.04218, 2019.
• Vladimir Vovk, Alexander Gammerman, and Glenn Shafer. Algorithmic Learning in a Random World. Springer, 2005.
• Bin Yu and Karl Kumbier. Veridical data science. Proceedings of the National Academy of Sciences, vol. 117, pp. 3920-3929, 2020.
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