Biostatistics and Biomathematics

Data Science Affinity Group

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2017 Data Science Affinity Group meetings will take place the first and third Tuesdays of each month at noon in M1-A303. We are currently scheduling seminars for the year and are interested in hearing about both the big picture ideas in Data Science, as well as hands-on/technical details about your particular slice of data science. If you are interested in presenting at one of our seminars please contact the group’s co-ordinator, Amy Leonardson (aleonard@fredhutch.org).

Links to past seminars:

2015

2016


Current and Upcoming Seminars


February 21, 2017

3:00 p.m., M1-A303

Johannes Lederer, University of Washington

A General Framework for Uncovering Dependence Networks

Dependencies in multivariate observations are a unique gateway to uncovering relationships among processes. An approach that has proved particularly successful in modeling and visualizing such dependence structures is the use of graphical models. However, whereas graphical models have been formulated for finite count data and Gaussian-type data, many other data types prevalent in the sciences have not been accounted for. For example, it is believed that insights into microbial interactions in human habitats, such as the gut or the oral cavity, can be deduced from analyzing the dependencies in microbial abundance data, a data type that is not amenable to standard classes of graphical models. We present a novel framework that unifies existing classes of graphical models and provides other classes that extend the concept of graphical models to a broad variety of discrete and continuous data, both in low- and high-dimensional settings. Moreover, we present a corresponding set of statistical methods and theoretical guarantees that allows for efficient estimation and inference in the framework.

 

March 21, 2017

3:00 p.m., M1-A303

Rishabh Jain, University of Washington

Title to come

Abstract to come

 

April 4, 2017

3:00 p.m., M1-A303

Michael Zager, Fred Hutch

Title to come

Abstract to come

 

April 18, 2017

3:00 p.m., M1-A303

TBA

Title to come

Abstract to come

 

May 2, 2017

3:00 p.m., M1-A303

Xiuwen Zheng, University of Washington

Title to come

Abstract to come

 

May 16, 2017

3:00 p.m., M1-A303

Fred Hutch Genomics Shared Resource

Title to come

Abstract to come

 

June 6, 2017

3:00 p.m., M1-A303

Garnet Anderson, Fred Hutch

Title to come

Abstract to come

 


Past Seminars


 

February 7, 2017

Justin Guinney, Sage Bionetworks

DREAM Challenges: Crowdsourcing Solutions to Complex Biomedical Problems

The Dialogue on Reverse Engineering Assessment and Methods (DREAM) -- better known as DREAM Challenges -- is an open science, collaborative competition framework, and recognized as a successful model for motivating research teams to solve complex biomedical problems. The DREAM vision is to allow individuals and groups to collaborate openly so that the “wisdom of the crowd” provides the greatest impact on science and human health. DREAM has now successfully run over 38 Challenges in multiple disease and biological areas, including Alzheimer’s, rheumatoid arthritis, amyotrophic lateral sclerosis, olfaction, toxicology, and cancer. 

 

January 17, 2017

Matthew Trunnell, CIO, Fred Hutch

Naveen Ashish, Principal Data Scientist, Hutch Data Commonwealth
James Ryan, Sr Director of Engineering Operations, Hutch Data Commonwealth
Mija Lee, Manager of Big Data Engineering, Hutch Data Commonwealth
Aubree Hoover, Director of Software & Data Products, Hutch Data Commonwealth

Hutch Data Commonwealth and Data Science: Where we are and where we are going. . . .

The vision of the Hutch Data Commonwealth (HDC) is “to enable investigators to leverage all possible data in the effort to eliminate disease by driving the development of data infrastructure and data science capabilities through collaborative research and robust engineering.”  Members of the HDC, including Matthew Trunnell, Naveen Ashish and James Ryan, will spend the first part of the hour bringing people up to date on the current state of this newly formed organization, how we are structured, our current initiatives and how you can get involved.

At that point, we will open the floor to questions and comments from the audience for the HDC leadership team.  Although we meet with researchers across campus on a regular basis and we have guidance from the HDC Scientific Steering Committee, this is an opportunity for us to hear from the broader community of Fred Hutch data scientists.

 

January 3, 2017

Erick Matsen, Fred Hutch

Phylogenetics for Modern Data Sets, from Foundation on up

Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, yields valuable evolutionary understanding of many biological systems. Although mathematical foundations and algorithms for phylogenetic inference have been under development for many decades, many questions remain. In this talk I will describe some new mathematical results that counter common assumptions, as well as foundations for new Bayesian phylogenetic inference algorithms.