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*[https://drive.google.com/file/d/0B2ht_TCS6xC-NEZ1YnQyVW0tREE/view?usp=sharing Genomics] | *[https://drive.google.com/file/d/0B2ht_TCS6xC-NEZ1YnQyVW0tREE/view?usp=sharing Genomics] | ||
*[https://drive.google.com/file/d/0B2ht_TCS6xC-cDVJNUtIcjRjcFE/view?usp=sharing Imaging] | *[https://drive.google.com/file/d/0B2ht_TCS6xC-cDVJNUtIcjRjcFE/view?usp=sharing Imaging] | ||
− | ======2017 Symposium | + | ======2017 Symposium Group Posters====== |
*[https://drive.google.com/file/d/0B2ht_TCS6xC-Zm5RWHByeWdUd2M/view?usp=sharing A Time-to-Event Analysis of Heart Failure via Electronic Health Records] | *[https://drive.google.com/file/d/0B2ht_TCS6xC-Zm5RWHByeWdUd2M/view?usp=sharing A Time-to-Event Analysis of Heart Failure via Electronic Health Records] | ||
*[https://drive.google.com/file/d/0B2ht_TCS6xC-UEIwRVNZaVJXRFk/view?usp=sharing Melanoma Detection by Classifying Skin Lesion Images] | *[https://drive.google.com/file/d/0B2ht_TCS6xC-UEIwRVNZaVJXRFk/view?usp=sharing Melanoma Detection by Classifying Skin Lesion Images] |
Revision as of 12:52, 2 July 2021
Welcome to the U-M Big Data Summer Institute 2021 Wiki!
Contents
Electronic Health Records Group
Machine Learning Group
Imaging Group
Data Mining Group
Lectures
Week 1
- Study Design and Inference, Observational Data and Bias, Parameter Estimation and Likelihood - Dr. Rod Little
-Part 1, Part 1 Slides -Part 2, Part 2 Slides -Part 3, Part 3 Slides -Part 4, Part 4 Slides -Part 5, Part 5 Slides
- Linear Algebra - Lap Sum Chan
-Recording -Linear Algebra Slides
- Linear Regression - Fatema Shafie Khorassani
-Recording -Linear Regression Slides
- Probability - Rupam Bhattacharyya
-Recording -Probability Slides
- Data Wrangling in R with dplyr - Dr. Matthew Flickinger
-Lecture Slides
- Data Visualization in R with ggplot2 - Dr. Matthew Flickinger
-Lecture Slides
Week 2
- Machine Learning - Dr. Jenna Wiens
- Data Mining - Jiong Zhu
- Logistic Regression - Aubrey Annis
-Recording -Logistic Regression Slides
- Generalized Linear Models - Aubrey Annis
-Recording -GLM Slides
- R Markdown - Dr. Phil Boonstra
-Synchronous Lecture Recording -R Markdown Slides
- Python - Dr. Fred Feng
-Synchronous Lecture Part I and II Recording -Synchronous Lecture Part III and IV Recording
Week 3
- Assessment of Predictive Models - Dr. Phil Boonstra
-Recording
- Causal Inference - Dr. Walter Dempsey
-Recording -Causal Inference Slides
- Model Selection - Nicky Wakim
-Recording -AUC/ROC Recording -Model Selection Slides -Annotated Model Selection Slides
- Data Visualization Workshop
-Synchronous Lecture Recording -Data Visualization Slides
Week 4
- Introduction to Bayes - Dr. Xiaoquan William Wen
-Intro to Bayes I Recording -Intro to Bayes II Recording -Intro to Bayes I Slides -Intro to Bayes II Slides
- Missing Data - Dr. Peisong Han
- Clustering - Fahad Kamran
-Clustering I Recording -Clustering II Recording -Clustering I Slides -Clustering II Slides
Week 5
- Social Networks - Dr. Eytan Adar
- Electronic Health Records - Dr. Xu Shi
- Genetics/Genomics - Dr. Jean Morrison
- Health Disparities in Strokes - Dr. Lynda Lisabeth
- Precision Health - Dr. Sachin Kheterpal
Week 6
- Data Integration and Precision Medicine - Dr. Veera Baladandayuthapani
- Radiation Oncology - Dr. Arvind Rao
- Better, Not Just Bigger Data Analytics - Dr. Brahmajee Nallamothu
- Clinical Trials - Dr. Kelley Kidwell
Week 7
- Preparing for Graduate School - Dr. Kelley Kidwell
- CVs and Resumes - Krystle Forbes
- Scientific Writing - Dr. Brett Griffiths
- Pick Me! - Dr. Brett Griffiths
Week 8
Journey Lectures
- Week 1: Dr. Michael Boehnke
- Week 2: Dr. Mousumi Banerjee
- Week 3: Dr. Lauren Beesley VanDervort
- Week 4: Dr. Rod Little
Past BDSI Student Poster Presentations
2019 Student Poster Presentations
2018 Student Poster Presentations
- Imaging Group Presentation
- Machine Learning Group Presentation
- Genetics Group Presentation
- Data Mining Presentation
2017 Student Poster Presentations
2017 Symposium Group Posters
- A Time-to-Event Analysis of Heart Failure via Electronic Health Records
- Melanoma Detection by Classifying Skin Lesion Images
- Classifying Skin Lesions Images Using Adaptive Boosting
- Machine Learning Classification of Skin Lesion Images
- Genomics: Genome Storage and Assembly
- Predicting the Transcriptome from the Genome
- Classification of Cell Types from Peripheral Mononuclear Blood Cells
- EHR-Based Study of Long-Term Infectious Diseases
- Visualizing Lab and Phenotype Associations Using PheWAS and Electronic Health Records
- Data Mining: Microenvironment Microarray Spot Based Approach for Cell Prediction
- Estimating Cell Growth with Machine Learning and Data Mining