Training
& Outreach

CPCP develops, conducts and evaluates training activities that reach a broad set of audiences whose education, research and practice can significantly benefit from having state-of-the-art knowledge about data science, predictive models for biomedicine, and computational phenotyping. These audiences include biomedical scientists, clinicians, data scientists, postdocs, graduate students, undergraduates, and the general public.

Upcoming Events

Nov 22, 2016

CPCP Seminar: Big Data in Behavioral Medicine by Dr. James Rehg

James Rehg on understanding and developing interventions for adverse health-related behaviors

Past Events

Oct 18, 2016

Big Privacy: Policy Meets Data Science Symposium

This symposium examined the legal, policy, and technical issues at the intersection of data privacy

Jun 30, 2016

CPCP Second Annual Retreat

A day-long retreat highlighting recent research in the CPCP

Apr 21, 2016

CPCP Seminar: Mining Structures from Massive Bio-Text Data: A Data-Driven Approach by Dr. Jiawei Han

Jiawei Han from the BD2K KnowEng Center-UIUC discussed mining structures from massive bio-text data

Nov 10, 2015

CPCP Seminar: Transforming Your Research with High-Throughput Computing by Lauren Michael

Lauren Michael from the CHTC discussed high-throughput computing approaches to Big Data

Oct 15, 2015

Big Privacy: Policy Meets Data Science Symposium

A symposium on the legal, policy, & technical issues at the intersection of privacy and data science

Jun 12, 2015

CPCP First Annual Retreat

May 06, 2015

Small Talks on Big Data

These talks are designed to demonstrate how Big Data is transforming the biomedical sciences.

Feb 12, 2015

CPCP Seminar: Towards Demystifying Big Data Technologies by Jignesh Patel

Jignesh Patel will evaluate software/computing platforms used to process large complex data sets

Staff

Whitney Sweeney

Debora Treu

Deborah Muganda-Rippchen

Training Resources

CPCP Seminar: Big Data in Behavioral Medicine Seminar Video

Complex chronic diseases are creating a growing burden on society. This burden affects the quality of life for many individuals in addition to the financial burden associated with treatment. Every year a large percentage of deaths in the United States are caused by poor diet, physical inactivity or substance abuse (primarily tobacco). These problems are fundamentally behavioral in nature. In addition, developmental disorders such as autism are diagnosed by qualification of behaviors. Dr. James Rehg talks about the role of Big Data on these types of behavioral health disorders. Dr. Rehg and his coleagues work with the new types of sensors that are becoming increasingly available to measure behavioral patterns. They have developed a number of computational models to improve the analysis of these measurements. These models allow them to make quantitative statements about what types of therapies have the greatest affects on behavior.

CPCP Privacy Symposium 2016: Privacy Preserving Federated Biomedical Data Analysis Symposium Video

Learn about the challenges associated with the technical approaches for utilizing data from multiple sources to build more accurate machine learning algorithms from Dr. Xiaoquian Jiang. We know that having more types of data and data from distributed sources provides a stronger platform for research and discovering with machine learning. To address privacy in this context, Dr. Jiang proposes a privacy-preserving distributed data framework and describes various models implemented to solve the such problems. Dr. Jiang's research group has produced versions of this framework in R and Java as well as an online web-service. All version of this framework are available for other researchers to use for their own analysis.

CPCP Privacy Symposium 2016: Privacy is an Essentially Contested Concept Symposium Video

What does privacy mean in the context of Big Data? Dr. Deirdre Mulligan discusses various definitions of privacy in law, philosophy and computer science. Traditional approaches to privacy in data place most of the responsibility for the control of private information flow on the individual with mechanisms such as consent. This idea, known as informational actualization, has limitations that have been exposed by machine learning on big data. These limitations cause violations of privacy such as uncovering identity of individuals where it has been withheld or unexpected inferences made from data that have been intentionally disclosed. Dr. Mulligan suggests new ways of viewing privacy that evolve as social life and technology change.

CPCP Privacy Symposium 2016: Proving that Programs Do Not Discriminate Symposium Video

As the field of Artificial Intelligence (AI) continues to advance, an increasing number of prediction are made by computer programs about humans. These predictions affect decisions made about humans in a wide variety for areas including decisions about: who should get the job, the bank loan, or early release from prison. As we increasingly rely on AI programs to help make decisions about peoples lives, it becomes vitally important that we are able to ensure the programs we are depending on do not have an unfairly biased against certain groups of people. Dr. Aws Albarghouthi of the University of Wisconsin - Madison Computer Sciences department uses his expertise in programming languages to address this issue of fairness.

CPCP Privacy Symposium 2016: Panel Discussion Symposium Video

Dr. Pilar Ossorio from Morgridge Institute for Research at the University of Wisconsin-Madison, and Dr. Peggy Peissig from the Biomedical Informatics Research Foundation join Dr. Aws Albarghouthi, Dr. Deirdre Mulligan, and Dr. Xiaoquian Jiang to answer questions from the audience about privacy and fairness in the context of computational analysis.