Senior Scientist Thomas Cook to receive the 2015 Chancellor's Award for Excellence in Research

BMI Senior Scientist Thomas Cook has been selected to receive the 2015 Chancellor's Award for Excellence in Research as an Independent Investigator! He was one of nine honorees to receive an Academic Staff Excellence Award.
This designation is awarded to Tom for his deep and broad contributions to the conduct and analysis of clinical trials, both in applications and methodology development.

Tom has satisfied all of the Award criteria: "Outstanding achievement and performance by the candidate, [who] consistently and substantially exceeds the expectations of the position; has made important and significant contributions to the departmental unit; has furthered the mission of the university; and has become a distinguished member of his/her profession campus-wide, system wide, nationally, or internationally".
BMI Distinguished Scientist Marian Fisher spearheaded Tom's nomination and worked diligently to complete it with great attention to the details!

Congratulations, Tom and Marian!

David Page awarded a Kellett Mid-Career Faculty Researcher Award

Professor David Page has been awarded a Kellett Mid-Career Faculty Researcher Award for the period starting in July 2015 through June of 2020. This is a wonderful recognition by the University of David's broad and deep contributions to the research enterprise across the UW campus and beyond. The award recognizes his academic success and provides an opportunity for continued development of his outstanding research program.

The fellowship is named in honor of William R. Kellett, a WARF Trustee and President of the WARF board. A Wisconsin native, graduate of the College of Engineering and an enthusiastic supporter of the University, Kellett held an abiding commitment to the reform and progress of Wisconsin’s educational institutions.

Professor Jude Shavlik of Computer Sciences, with an affiliate appointment in Biostatistics and Medical Informatics, spearheaded Page's nomination.

David is BMI's second Kellett Awardee in recent years. Professor Michael Newton, who is joint with Statistics, is a Kellett Awardee named in the 2012-13 Academic Year.

The award is generously provided by the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education, with funding from the Wisconsin Alumni Research Foundation.

Vilas Distinguished Achievement Professor David Page

Congratulations to Professor David Page who has been named a Vilas Distinguished Achievement Professor. This appointment was made by Provost Sarah Mangelsdorf after a nomination by Dean Robert Golden. Provost Mangelsdort, Dean Golden and Dean Moss were instrumental in providing approval and support for this appointment.

The Vilas Distinguished Achievement Professorships recognize professors whose distinguished scholarship has advanced the confines of knowledge, and whose excellence has also included teaching or service. The professorship provides a fixed allocation of flexible funds to be used by David over the next five years. David will carry the title of Vilas Distinguished Achievement Professor for the duration of his career at UW-Madison.

Professor Rick Chappell an Elected Fellow of the Society of Clinical Trials

Congratulations to Professor Rick Chappell for his election as a Fellow of the Society of Clinical Trials, an organization which Rick has previously served as President. Fellows are Society members who have made significant contributions to the advancement of clinical trials and to the Society. Rick was elected for "important contributions to the statistical methodology for the design and analysis of clinical trials, particularly in cancer and aging; for leadership in clinical trial coordination and conduct at the University of Wisconsin Comprehensive Cancer Center; for national leadership on numerous DSMBs and advisory committees, including NIH and FDA; and for distinguished service to the Society."

Congratulations, Rick!

BMI faculty presented at Morgridge Symposium

BMI faculty to present at Morgridge Symposium
The BMI Department was a participating sponsor in a symposium entitled "When is an algorithm a medical device?", primarily organized and sponsored by the Morgridge Institute for Research. From the announcement materials, the "symposium will provide information on the current and potential regulatory framework for medical software development, guidelines for identifying when software becomes a medical device, and guidance on how to integrate the required practices into biomedical research."
Video are now available here:
BMI faculty members David DeMets and David Page are among the panelist who will present their work in the half-day symposium. 
More information is available here:

The Center for Predictive Computational Phenotyping (CPCP) at UW-Madison

Transcription-based cellular phenotyping

The University of Wisconsin, in a partnership with the Morgridge Institute for Research and the
Marshfield Clinic Research Foundation, has received a grant from the NIH to establish the Center
for Predictive Computational Phenotyping (CPCP), which is one of the NIH's
new Centers of Excellence for Big Data Computing in the Biomedical Sciences.
The grant will provide nearly $11 million over a four-year period.

The Center for Predictive Computational Phenotyping will develop innovative computational and statistical
methods and software for a broad range of problems that can be cast as computational phenotyping.
The term phenotype, which is derived from the Greek word phainein meaning‚ "to show"
refers to the observable properties of an organism that result from the interaction of its genotype
and its environment. Some phenotypes are easily measured and interpreted, and are available in an accessible
format. However, a wide range of scientifically and clinically important phenotypes do not satisfy these
criteria. In such cases, computational phenotyping methods are required either to extract a relevant phenotype
from a complex data source or collection of heterogeneous data sources, and to predict clinically important
phenotypes before they are exhibited. The Center will have a particular focus on screening for breast cancer
and Alzheimer's disease, and it will investigate how to exploit a wide array of data types for these tasks,
including molecular profiles, medical images, electronic health records, and population-level data. The Center
will also provide training in biomedical Big Data analysis to scientists and clinicians, and it will
investigate the bioethical issues surrounding the technology being developed.

The director of the Center is Mark Craven and the associate director is Michael Newton, both of whom are
professors in the Department of Biostatistics & Medical Informatics.

BMI student and faculty share in computer security award

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing

BMI Student Eric Lantz, his advisor Professor David Page, and their colleagues Matthew Fredrikson, Somesh Jha, Simon Lin, and Thomas Ristenpart have won the best paper award at USENIX Security, a major computer security conference held August 20th - 22nd, 2014, in San Diego. The paper, “Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing,” Fredrikson, Jha and Ristenpart are in Computer Sciences. Lin is at Nationwide Children's Hospital in Columbus, Ohio.

The paper demonstrates that publishing a predictive model for personalized medicine or pharmacogenetics could in some cases reveal private information about patients on whose data the model was trained, via a "model inversion attack." It also shows that while differential privacy can protect against such an attack, for current typical data set sizes if the privacy level is elevated high enough to provide meaningful privacy protection then the trained predictive model is of little or no utility.


Sushmita Roy obtains NSF CAREER Award for Regulatory Networks

Reconstructing the evolution of regulatory networks

Assistant Professor Sushmita Roy has obtained a prestigious NSF CAREER award to pursue an innovative project entitled "Comparative Network Biology to Study the Evolution of Regulatory Networks".

Central to how living cells accomplish diverse biological functions are regulatory networks that control what genes need to be activated under different environmental conditions. As evolution is the ultimate tinkerer of living systems systematic comparisons of how regulatory networks evolve to drive species-specific differences are critical to understand cellular functions. Through advances in genomics, it is now possible to measure the activity levels of almost all genes for many species. This provides a unique opportunity to systematically compare these gene activity levels across multiple organisms and link changes in activity to changes in the networks of individual species. However, this is challenging because, first, such comparisons require the regulatory networks to be known in not one but multiple species including those that are poorly characterized, and second, computational methods to compare molecular datasets across species other than DNA sequence are in their infancy. This project will address these challenges by developing novel computational methods to identify and compare regulatory networks across multiple species, and correlate regulatory network patterns of divergence to phenotypic changes.

Yajuan Si has joined BMI and PHS

The Departments of Biostatistics & Medical Informatics and of Population Health Sciences are pleased to welcome new Assistant Professor Yajuan Si, PhD. Yajuan is joining us after completing her doctoral work in Statistical Science at Duke University and a post-doctoral fellowship at Columbia University under the direction of Professor Andrew Gelman. Her research interests are centered around Bayesian statistics and include latent variable models, complex survey inference methods, causal inference and post-stratification, and data confidentiality protection. Welcome Yajuan!

It has historically been a challenge to perform Bayesian inference in a design-based survey context. Yet, classical analysis is not robust especially for subgroup estimation. Model-based estimates are subject to bias under misspecified model assumptions. To address these problems, Yajuan develops a nonparametric Gaussian process regression of model-based survey inference and a unified framework under multilevel regression and post-stratification in realistic settings with survey weights.