Symposium Schedule | June 5-8

7:30 a.m.

East Foyer
Stewart Center

8:30 - 9:30 a.m.

Fowler Hall, Stewart Center

James Berger
(Duke University)

Abstract:  There is a Society of Imprecise Probability (SIPTA). At their annual meeting last July, I gave this talk to illustrate some of the methods Bayesians use to deal with imprecise probability. The illustrations considered include dealing with interval valued probabilities, the p-value problem, optimal normal hierarchical Bayesian analysis, and uncertainty quantification of complex computer models. Many of the ideas behind Bayesian methods for dealing with imprecise probability arose from Herman Rubin, so this talk is also given in honor of his enduring legacy.

9:30 - 10:00 a.m.

STEW 218

Break / Coffee

10:00 a.m. - 12:00 p.m.

Morning Technical Sessions

Session 1: In Honor of John Deely (co-organizers: Bruce Craig and Wesley O. Johnson) - STEW 214 AB

• Ronald Christensen (University of New Mexico)
• Purushottam (Prakash) Laud (Medical College of Wisconsin)
• Wesley O. Johnson (University of California, Irvine)
• James O'Malley (Dartmouth College)

Session 2: Statistical Developments in Analysis of Genomics Data for Disease Phenotype and Drug Response (organizer: Jun Xie) - STEW 214 CD

• Ching-Ti Liu (Boston University)
• Wei Sun (Fred Hutchinson Cancer Center)
• Yaowu Liu (Harvard University)

12:00 - 1:30 p.m.

Lunch Break (on your own)

1:30 - 3:30 p.m.

Afternoon Technical Sessions

Session 3: Statistical Challenges for National Security (co-organizers: Dan DeLaurentis and Justin Newcomer) - STEW 214 AB

• Adam Cardinal-Stakenas (National Security Agency)
• Katherine Simonson (Sandia National Laboratories)
• Kelly Avery (Institute for Defense Analyses)
• Suresh Jagannathan (Purdue University)

Session 4: Big Data Theory and Computation (organizer: Guang Cheng) - STEW 214 CD

• Shih-Kang Chao (Purdue University)
• Zijian Guo (Rutgers)
• Xiao Han (USC)
• Mingao Yuan (IUPUI)

3:30 - 4:00 p.m.

STEW 218

Break / Coffee

4:00 - 5:00 p.m.

Fowler Hall, Stewart Center

Tony Cai
(The Wharton School at the University of Pennsylvania)

Abstract:  In the conventional statistical framework, the goal is developing optimal inference procedures, where optimality is understood with respect to the sample size and parameter space. When the dimensionality of the data becomes large as in many contemporary applications, the computational concerns associated with the statistical procedures come to the forefront. A fundamental question is: Is there a price to pay for statistical performance if one only considers computable (polynomial-time) procedures? After all, statistical methods are useful in practice only if they can be computed within a reasonable amount of time. 

In this talk, we discuss the interplay between statistical accuracy and computational efficiency in two specific problems: submatrix localization and sparse matrix detection based on a noisy observation of a large matrix. The results show some interesting phenomena that are quite different from other high-dimensional problems studied in the literature.

5:00 - 5:15 p.m.

5:15 - 6:30 p.m.

STEW 218

8:00 a.m.

8:30 - 9:30 a.m.

Peter Bühlmann
(ETH Zürich)

Abstract:  Heterogeneity in potentially large-scale data can be beneficially exploited for causal inference and novel robustness. The key idea relies on invariance and stability across different heterogeneous regimes or sub-populations. What we term as "anchor regression" opens up new insights and connections between causality and protection (robustness) against worst case perturbations in prediction problems. We will discuss the methodology and some applications.

9:30 - 10:00 a.m.

STEW 218

Break / Coffee

10:00 a.m. - 12:00 p.m.

Morning Technical Sessions

Session 5: Big Data in Plant Science I (co-organizers: Min Zhang and Jianming Yu) - STEW 214 AB

• Patrick Schnable (Iowa State)
• Alexander Lipka (University of Illinois)
• Tingting Guo (Iowa State)
• Mitchell Tuinstra (Purdue)

Session 6: Nonparametric Bayes: Big Models for Big Data (organizer: Vinayak Rao) - STEW 214 CD

• Peter Mueller (University of Texas at Austin)
• Long Nguyen (University of Michigan)
• Sinead Williamson (University of Texas at Austin)
• Steve MacEachern (Ohio State)

12:00 - 1:30 p.m.

Lunch Break (on your own)

1:30 - 3:30 p.m.

Afternoon Technical Sessions

Session 7: Scalable Bayesian Methods for Large and Complex Data (organizer: Anindya Bhadra) - STEW 214 CD

• Jeffrey Morris (MD Anderson)
• Naveen Narisetty (University of Illinois)
• Veronika Rockova (Chicago Booth)

Session 8: Big Data in Plant Science II (co-organizers: Min Zhang and Jianming Yu) - STEW 214 AB

• Rebecca W. Doerge (Carnegie Mellon)
• Zhen Zhang (Dow AgroSciences LLC)
• Min Zhang (Purdue University)
• Karl Broman (University of Wisconsin-Madison)

Session 9: In Memory of Herman Rubin and His Contributions (organizer: Anirban DasGupta) - STEW 202

• Rodrigo Bañuelos (Purdue University)
• Andrew L. Rukhin (National Institute of Standards and Technology)
• Rick Vitale (University of Connecticut)

3:30 - 4:00 p.m.

STEW 218

Break / Coffee

4:00 - 5:00 p.m.

Fowler Hall, Stewart Center

Xihong Lin
(Harvard University)

Abstract:  Massive ‘ome data, including genome, exposome, and phenome data, are becoming available at an increasing rate with no apparent end in sight. Examples include Whole Genome Sequencing data, large-scale remote-sensing satellite air pollution data, digital phenotyping, and Electronic Medical Records. The emerging field of Health Data Science presents statisticians with many exciting research and training opportunities and challenges. Success in health data science requires strong statistical inference, integrated with computer science and information science. Examples include signal detection, network analysis, integrative analysis of different types and sources of data, and incorporation of domain knowledge in health data science method development. In this talk, I discuss some of the challenges and opportunities, and illustrate them using high-dimensional testing of dense and sparse signals for whole genome sequencing analysis, integrative analysis of different types and sources of data, and analysis of pleiotropy using biobanks and Electronic Medical Records (EMRs).  

8:00 a.m.

East Foyer
Stewart Center

Registration table opens

8:30 - 9:30 a.m.

Donald Rubin
(Harvard University)

Morning Plenary

Essential concepts of causal inference — a remarkable history

Abstract: I believe that a deep understanding of cause and effect, and how to estimate causal effects from data, complete with the associated mathematical notation and expressions, only evolved in the twentieth century. The crucial idea of randomized experiments was apparently first proposed in 1925 in the context of agricultural field trails but quickly moved to be applied also in studies of animal breeding and then in industrial manufacturing. The conceptual understanding seemed to be tied to ideas that were developing in quantum mechanics. The key ideas of randomized experiments evidently were not applied to studies of human beings until the 1950s, when such experiments began to be used in controlled medical trials, and then in social science — in education and economics. Humans are more complex than plants and animals, however, and with such trials came the attendant complexities of non-compliance with assigned treatment and the occurrence of “Hawthorne" and placebo effects. The formal application of the insights from earlier simpler experimental settings to more complex ones dealing with people, started in the 1970s and continue to this day, and include the bridging of classical mathematical ideas of experimentation, including fractional replication and geometrical formulations from the early twentieth century, with modern ideas that rely on powerful computing to implement aspects of design and analysis

9:30 - 10:00 a.m.

STEW 218

Break / Coffee

10:00 a.m. - 12:00 p.m.

Morning Technical Sessions

Session 10: Jayanta K. Ghosh Memorial Session on Model Uncertainty (organizer: Jim Berger) - STEW 214 AB

• Malgorzata Bogdan (Wroclaw University of Science and Technology)
• Bertrand Clarke (University of Nebraska-Lincoln)
• Malay Ghosh (University of Florida)

Session 11: Deep Neural Nets, Scalable Computing and Finance (organizer: Kiseop Lee) - STEW 202

• Colm O'Cinneide (QS Investors LLC)
• Xiao Wang (Purdue)
• Faming Liang (Purdue)
• Kylie Bemis (Northeastern University)

Session 12: Precision Medicine (organizer: Lingsong Zhang) - STEW 214 CD

• Guanhua Chen (University of Wisconsin)
• Bruce Craig (Purdue University)
• Haoda Fu (Eli Lilly and Co.)

12:00 - 1:30 p.m.

Lunch Break (on your own)

1:30 - 3:30 p.m.

Afternoon Technical Sessions

Session 13: Divide & Recombine with DeltaRho R & Hadoop for Big Data Analysis (organizer: William S. Cleveland) - STEW 202

• William S. Cleveland (Purdue University)
• Wen-wen Tung (Purdue)
• Aritra Chakravorty (Purdue)

Session 14: Jayanta K. Ghosh Memorial Session on Bayesian Nonparametrics, Empirical Processes, and Convexity (organizer: Jim Berger) - STEW 214 AB

• Anirban DasGupta (Purdue University)
• Subhashis Ghosal (North Carolina State University)
• Surya Tokdar (Duke University)

Session 15: Probabilistic Machine Learning and Modern Statistics (organizer: Vinayak Rao) - STEW 214 CD

• Babak Shahbaba (UC Irvine)
• Jean Honorio (Purdue)
• Qiang Liu (University of Texas at Austin)
• Bharath Sriperumbudur (Penn State)

3:30 - 4:00 p.m.

STEW 218

Break / Coffee

4:00 - 5:00 p.m.

Fowler Hall, Stewart Center

Guy Lebanon
(Amazon Inc.)

Abstract:  I will discuss several challenges that are of critical importance to the tech industry and currently receive little attention in the research community. Among these challenges are selection and composition of box-art images, producer-consumer marketplaces in newsfeeds, and modeling long-term effects in e-commerce. After presenting the challenges I will discuss several possible solutions, and ways in which the research community can help out.

6:00 - 11:00 p.m.

PMU South Ballroom