Difference between revisions of "Main Page"

Latest revision as of 12:08, 1 August 2016

Welcome to the U-M Big Data 2016 Summer Program Wiki!

Consult the User's Guide for information on using the wiki software.

Reading Material

Data Mining Group

• Automatic Construction and Natural-Language Description
• Interestingness Measures for Data Mining a Survey
• Fast Algorithms for Mining Association Rules

EHR Group

• Statistical Inference, Learning and Models in Big Data
• USRDS 2015 Annual Data Report, Volume 1: Chronic Kidney Disease
• Dialysis Facility Characteristics and Services
• Data Dictionary for Quarterly Dialysis Facility Compare
• USRDS Introduction to Volume 1: CKD in the United States

Genomics Group

• A Global Reference for Human Genetic Variation
• A Map of Human Genome Variation from Population-Scale Sequencing

2016 Presentations

Day 1: June 13

• Orientation 2016 (slides) - Bhramar Mukherjee, PhD
• Welcome Reception (slides) - Bhramar Mukherjee, PhD
• Computing Resources (slides)
• Ethics Review (slides)
• On Being A Scientist (slides)
• Life in Ann Arbor (slides) - Evan Reynolds, Biostatistics PhD Student
• Intro to Probabilities and Distributions (file) - Rebecca Rothwell, Biostatistics PhD student

Day 2: June 14

• Computing Platforms (website) - Jacob Abernethy, PhD
• Introductory Statistics (slides & audio) - Bhramar Mukherjee, PhD

Day 3: June 15

• Intro to Unix (slides) - Hyun Min Kang, PhD

Day 4: June 16

• Data Structure using Python (website) - Jacob Abernethy, PhD
• Observational Data, Bias and Confounding (slides & audio) - Roderick Little, PhD

Day 5 June 17

• Data Structure using Python (website) - Jacob Abernethy, PhD
• From Pure Mathematics to Gene Discovery: A Biostatistician's Journey (slides & audio) - Michael Boehnke, PhD

Day 6 June 20

• Statistical Modeling using R (slides) - Phil Boonstra, PhD
• Study Design and Inference (slides & audio) - Roderick Little, PhD

Day 7 June 21

• Visualization using R (slides & audio) - Phil Boonstra, PhD
• Distributed Computing (slides) - Harsha Madhyastha, PhD

Day 8 June 22

• Fundamentals of Data Processing (slides) - Jed Carlson, Biostatistics PhD student

Day 9 June 23

• Matrix Computation (audio) - Jason Estes, Research Fellow in Biostatistics

Day 10 June 24

• Career Journey and A Principal Approach to Dimensionality Reduction Part 1 (slides) - Stephen Gliske, PhD
• Career Journey and A Principal Approach to Dimensionality Reduction Part 2 (slides) - Stephen Gliske, PhD
• Career Journey and A Principal Approach to Dimensionality Reduction Part 3 (slides) - Stephen Gliske, PhD

Day 11 June 27

• Large Scale Optimization Part 1 (slides & audio) - Tewari Ambuj, PhD
• link Causal Inference (audio) - Lu Wang, PhD
• Likelihood Functions and Parameter Estimation (slides) - Matthew Zawistowski, Research Specialist at the University of Michigan

Day 12 June 28

• Large Scale Optimization Part 2 (slides & audio) - Tewari Ambuj, PhD
• Sequential Decision Making (slides & audio) - Tewari Ambuj, PhD

Day 13 June 29

• R - dplyr (slides) - Matthew Flickinger, Senior Analyst at the University of Michigan
• R - Troubleshooting (slides) - Matthew Flickinger, Senior Analyst at the University of Michigan

Day 14 June 30

• Clustering: Graphical Models and Sampling Algorithm Part 1 (slides & audio) - Long Nguyen, PhD
• Clustering: Graphical Models and Sampling Algorithm Part 2 (slides & audio) - Long Nguyen, PhD

Day 15 July 1

• My SMART Journey (slides & audio) - Kelley Kidwell, PhD
• My Spatial Journey to UM Biostatistics (slides & audio) - Veronica Berrocal, PhD

Day 17 July 5

• Supervised Machine Learning 1 (slides & audio) - Hui Jiang, PhD
• Intro to Bayes (slides & audio) - Bhramar Mukherjee, PhD

Day 18 July 6

• R Data Visualization (slides) - Matthew Flickinger, Senior Analyst at the University of Michigan

Day 19 July 7

• Bayes Computation 1 (slides & audio) - Tim Johnson, PhD
• Unsupervised Machine Learning 1 (slides) - Jenna Wiens, PhD

Day 20 July 8

• Bayes Computation 2 (slides & audio) - Tim Johnson, PhD
• The Moments and the Journey (slides & audio) - Bhramar Mukherjee, PhD

Day 21 July 11

• Visualization 1 (slides) - Eytan Adar, PhD
• Supervised Machine Learning 2 (slides & audio) - Hui Jiang, PhD

Day 22 July 12

• Visualization 2 (slides) - Eytan Adar, PhD
• Personalized Medicine (slides & audio) - Lu Wang, PhD

Day 23 July 13

• Reproducible Research (slides) - Jed Carlson, Biostatistics PhD student

Day 24 July 14

• Social Netwrok Analysis (slides) - Eytan Adar, PhD
• Unsupervised Machine Learning 2 (slides) - Jenna Wiens, PhD

Day 25 July 15

• My Journey to Bigger than Average Data (slides & audio) - Joe Messana, MD

Day 26 July 18

• Supervised Machine Learning 3 (slides & audio) - Hui Jiang, PhD
• Population Genetics (slides & audio) - Sebastian Zoellner, PhD
• Adventures in Human Genetics (slides & audio) - Goncalo Abecasis, PhD, Department of Biostatistics Chair at the University of Michigan

Day 27 July 19

• Data Privacy and Security (slides) - Jacob Abernethy, PhD
• Learning Health Systems (slides & audio) - Karandeep Singh, PhD

Day 28 July 20

• Journey Lecture (slides & audio) - Phil Boonstra, PhD

Symposium

• Symposium Welcome - Bhramar Mukherjee, PhD
• Identifying and Correcting for Contamination in DNA Sequencing Studies - Michael Boehnke, PhD
• Assessing Time-Varying Causal Effect Moderation using Intensively Collected Longitudinal Intervention Data - Susan Murphy, PhD
• Statistical Methods for Personalized Medicine - Jeremy Taylor, PhD
• Software For (and With) Big Data - Eytan Adar, PhD
• Using Doctors' Notes to Uncover Everyday Natural Experiments in Healthcare - Karandeep Singh, MD
• Data Science for the Flint Water Crisis - Jacob Abernethy, PhD and Eric Schwartz. PhD
• Doing Data Science - Rachel Schutt, Chief Data Scientist at News Corp

Student Group Presentations

• Data Mining
• Electronic Health Records (EHR)
• Genomics
• Machine Learning

Student Poster Presentations

• Using Data Mining Techniques and Classification Algorithms to Predict Impact of Academic Papers
• Clustering Gene Expression Profiles of Single Cells Using Expectation Maximization
• Data Mining: Association Rules Using the Apriori Algorithm
• Exploring U.S. Population Stratification with Genes for Good Data
• Network Structure in Offshore Leaks Data
• A Genome-Wide Association Study for Mental Illness
• Evaluation of the End Stage Renal Disease Quality Incentive Program
• Genome-Wide Association Study of Alcohol Consumption and Stress Levels
• Perservation of Semantic Parallels Through Word2Vec
• Single Cell Diffenence in Gene Expression between Macular and Peripheral Human Retinal Cells
• Twitter Sentiment Analysis & The Stock Market
• Data Curation & Wrangling
• Predicting CKD and Potential Risk Factors with Multiple Linear Regression
• The ICIJ Panama Leak: Temporal & Spatial Visualizations of the World's Hidden Wealth
• A Streamlined Approach for Comparing Two Genomic Catalogs
• From Interviews to Blood Tests: Using Statistical and Machine Learning Models to Predict Kidney Function
• Flagging Facilities: An Empirical Null Method

Getting started

• Configuration settings list
• MediaWiki FAQ
• MediaWiki release mailing list
• Localise MediaWiki for your language