10:30 am - 12:00 pm

Scholars working at the interface of statistics, machine learning, and finance, will offer a review of recent methods that promise to impact the practice of systematic investing.

• Panos Toulis, University of Chicago, Booth of School of Business - Watch
• Jun Liu, Harvard University - Watch
• Natesh Pillai, Harvard University - Watch

1:30 pm - 3:00 pm

In the past few years we have witnessed a push in industry and academia on improving predictions out-of-sample by leveraging causal mechanisms and causal relationships among key performance metrics, in the context of a variable/complex environment. This panel will explore new methods that generate robust predictions by borrowing strength from A/B testing, causal inference, and policy evaluation tools.

• Ed George, Wharton School of Business, University of Pennsylvania - Watch
• Hedibert Lopes, Arizona State University and INSPER - Watch
• Alexander Volfovsky, Duke University - Watch

3:15 pm - 4:45 pm

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. Despite several CEOs have been warning the public about AI taking over the world, there have been real results and substantial progress. This panel will discuss AI tools that have impacted the practice.

• Ruslan Salakhutdinov, Carnegie Mellon University - Watch
• Donald Rubin, Temple University and Harvard University - Watch
• Alan Karr, Fraunhofer USA Center Mid-Atlantic and National Institute of Statistical Sciences - Watch

Panos Toulis

• Toulis, P., & Bean, J. (2021, May 29). Randomization inference of periodicity in unequally spaced time series with application to Exoplanet Detection. arXiv.org.

Jun Liu

• Shin, M., Lee, Y., & Liu, J. S. (2019, June 02). Generative Parameter Sampler For Scalable Uncertainty Quantification. 
• Hu, Z. T., Ke, Z. S., & Liu, J. U. (2020, July 15). Measurement error models: From nonparametric methods to deep neural networks. 

Natesh Pillai

• Dai, C., Chan, D., Huybers, P., & Pillai, N. (2020, December 14). Late 19th-century navigational uncertainties and their influence on sea surface temperature estimates. arXiv.org.
• Rischard, M., Pillai, N., & McKinnon, K. A. (2018, May 29). Bias correction in daily maximum and minimum temperature measurements through gaussian process modeling. arXiv.org.

Ed George

• Chipman, H.A., George, E.I. and McCulloch, R.E., (2010). BART: Bayesian Additive Regression Trees, Annals of Applied Statistics.
• Chipman, H., George, E.I., McCulloch, R.E.  and T.S. Shively (2021). mBART: Multidimensional Monotone Bart, Bayesian Analysis.
• Hill, J., Linero, A. and Murray, J. (2020) Bayesian Additive Regression Trees: A Review and Look Forward.

Hedibert Lopes

• Bolfarine, H., Carvalho, C. M., Lopes, H. F., & Murray, J. S. (2021, July 24). Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis.

Alexander Volfovsky

• The Almost Matching Exactly Lab 
• Wang, T., Morucci, M., Awan, M. U., Liu, Y., Roy, S., Rudin, C., & Volfovsky, A. (1970, January 01). FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference.
• Parikh, H., Rudin, C., & Volfovsky, A. (2021, September 22). MALTS: Matching After Learning to Stretch.
• Morucci, M., Orlandi, V., Rudin, C., Roy, S., & Volfovsky, A. (2020, August 08). Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.

Ruslan Salakhutdinov

• Presentation

Donald Rubin

• No content available.

Alan Karr

• Karr, A. F., Hauzel, J., Menon, P., Porter, A. A., & Schaefer, M. (2021, September 28). Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly. 
•  Karr, A. F., Taylor, M. T., West, S. L., Setoguchi, S., Kou, T. D., Gerhard, T., & Horton, D. B. (n.d.). Comparing record linkage software programs and algorithms using real-world data. 
• Alan F Karr, William J Fulp, Francisco Vera, S. Stanley Young, Xiaodong Lin & Jerome P Reiter (2007) Secure, Privacy-Preserving Analysis of Distributed Databases, Technometrics, 49:3, 335-345, DOI: 10.1198/004017007000000209 
• Cox, L. H., Karr, A. F., & Kinney, S. K. (2011, June 09). Risk‐Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act.
• Karr, A. F. (n.d.). Data Sharing and Access. 
• Bollen, K. A., Beimer, P. B., Karr, A. F., Tueller, S., & Berzofsky, M. E. (n.d.). Are Survey Weights Needed? A Review of Diagnostic Tests in Regression Analysis. 
• Dwork, C., Karr, A., Nissim, K., & Vilhuber, L. (2021, February 3). On Privacy in the Age of COVID-19. 
• Journal of Official Statistics, Vol. 29, No. 1, 2013, pp. 157–163,DOI: 10.2478/jos-2013-0009
• Karr, A. F., Fulp, W. J., Vera, F., Young, S. S., Lin, X., & Reiter, J. P. (2012, January 1). Secure, privacy-preserving analysis of distributed databases.