Keynote Speakers

Keynote: Xiao-Li Meng
We are excited to announce that Professor Xiao-Li Meng will present the MSSISS 2018 keynote address. Professor Meng is the Dean of the Harvard University Graduate School of Arts and Sciences (GSAS), Whipple V. N. Jones Professor and former chair of Statistics at Harvard.

Professor Meng: Tuesday at Rackham

Abstract:

A Trio of Inference Problems That Could Win You a Nobel Prize in Statistics (if you help fund it)

This talk presents and discusses a host of challenging problems as listed in Meng (2014). Statistical inference is a field full of problems whose solutions need the same intellectual force as for winning a Nobel Prize in other scientific fields.  Multi-resolution inference is the oldest of the trio, but emerging applications such as individualized medicines push us to estimate estimands with resolution far exceeding the  resolution of our estimators.  Multi-phase inference is another reality because (big) data are almost never collected, processed, and analyzed in a single phase. The newest of the trio is multi-source inference, which aims to extract desired information in data coming from very different sources, some of which were never intended for inference purposes. All these challenges call for an expanded paradigm with much greater emphases on qualitative consistency and relative optimality than do our current inference paradigms. It is recommended attendees read the accompanying paper to better understand the talk.

Meng, X.L. (2014). A Trio of Inference Problems that Could Win You a Nobel Prize in Statistics (If You Help Fund It). In Past, Present, and Future of Statistical Science (Eds: X. Lin, et. al), CRC Press, pp. 537-562.   http://www.stat.harvard.edu/Faculty_Content/meng/COPSS_50.pdf

Handout/slides:

MSSISShandout

Bio:

Xiao-Li Meng, Dean of the Harvard University Graduate School of Arts and Sciences (GSAS), Whipple V. N. Jones Professor and former chair of Statistics at Harvard, is well known for his depth and breadth in research, his innovation and passion in pedagogy, and his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng has received numerous awards and honors for the more than 150 publications he has authored in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development; he has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files,” a regularly appearing column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, frequentist, and fiducial perspectives; quantify ignorance via invariance principles; multi-phase and multi-resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard as Professor of Statistics, where he was appointed department chair in 2004 and the Whipple V. N. Jones Professor in 2007. He was appointed GSAS Dean on August 15, 2012.

Michigan Junior Faculty Keynote: Zhenke Wu
We are excited to welcome Assistant Professor Zhenke Wu as the MSSISS 2018 Monday evening junior faculty speaker.

Professor Wu: Monday at Rackham

Abstract:

Bayesian Hierarchical Methods to Power Disease Discovery and Improve Clinical Decisions

Precision medicine presents exciting opportunities for data scientists who may sculpt modern medicine by building game-changing analytic tools. For example, an ongoing challenge in human disease discovery is to identify patient subsets likely unified by distinct mechanistic pathways. This talk will formulate the key questions in statistical terms. It will offer Bayesian hierarchical models to integrate potentially complex novel biological measurements with prior medical knowledge to address the questions. The talk will then present two applications of the models, the first to better understand the etiology of childhood pneumonia when designing prevention and treatment programs, and the second to define autoantibody signatures among autoimmune disease patients for guiding patient management. The talk will identify statistical opportunities for improving clinical and public health decisions by inferring individual health states, trajectory, and likely benefits of competing treatment options. It will highlight that teams of biological, clinical and data scientists can collaborate to build statistical models that infer underlying biology from imperfect data.

Bio:

Zhenke Wu’s research involves the development of statistical methods that inform health decisions made by individuals. He is particularly interested in scalable Bayesian methods that integrate multiple sources of evidence, with a focus on hierarchical latent variable modeling. Those methods have been applied to estimate the etiology of childhood pneumonia, autoantibody signatures for subsetting autoimmune disease patients and to predict whether a user is engaged with mobile applications.

Zhenke has developed original methods and software that are now used by investigators from research institutes such as US CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh.

Zhenke completed a BS in Math at Fudan University in 2009 and a PhD in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training. Since 2016, Zhenke is Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute for Data Science (MIDAS) at University of Michigan, Ann Arbor.