BME – Healthcare Informatics Program
June 7th
- Session Topic - Machine Learning in Health- and Bio- Informatics
- 9:00-9:30 - Aidong Zhang (NSF) – “Patient Similarity Learning for Personalized Healthcare”
- 9:30-10:00 - Srinivas Aluru (Georgia Tech) – “Parallel Machine Learning Approaches for Reverse Engineering Genome-Scale Networks”
- 10:00-10:30 - Jeremy Weiss (CMU) – “Machine Learning and Survival Analysis to Forecast Clinical Risk from Electronic Health Records”
- Coffee Break
- Session Topic - Bioinformatics
- 11:00-11:30 - Mark Borodovsky (Georgia Tech) – “Improved Gene Prediction in Prokaryotic and Eukaryotic Genomes ”
- 11:30-12:00 - Gregory Cooper (University of Pittsburgh) – “Causal Network Discovery from Biomedical Data”
- 12:00-12:30 - Steve Qin (Emory University) – “Apply Machine Learning Methods to Predict New Disease Variants Genome-Wide”
- 12:30-1:30 - Lunch/Posters
- 1:30-2:30 - Plenary
- Session Topic - Neuroinformatics
- 2:30-3:00 - Steve Chase (CMU) – “Using machine learning to understand biological learning”
- 3:00-3:30 - Eva Dyer (Georgia Tech) – “Finding low-dimensional structure in large-scale neural datasets”
- 3:30-4:00 - Daniel Clymer (CMU) – “Convolutional Neural Networks to Improve Radiologist Workflow on 3D Medical Images: Application to Shoulder Labral Tears”
- Coffee Break
- Session Topic - Bioinformatics and Health Informatics
- 4:30-5:00 - Peng Qiu (Georgia Tech) – “Understanding cellular heterogeneity using single-cell data”
- 5:00-5:30 - Ankit Agrawal (Northwestern) – “Big Data Analytics for Deriving Predictive Healthcare Insights from Electronic Healthcare Records”
- 5:30-6:00 - May Dongmei Wang (Georgia Tech) – “Integrated Deep Learning of Genomics, Imaging, and EHR Data in ADNI Diagnosis”
June 8th
- Session Topic - Neuroinformatics
- 9:00-9:30 - Rob Kass (CMU) – “Torus Graphs for Multivariate Phase Coupling Analysis”
- 9:30-10:00 - Chethan Pandarinth (GT/Emory) – “LFADS: Inferring precise estimates of neural population state and dynamics using sequential auto-encoders”
- 10:00-10:30 - Will Bishop (CMU) – “Stabilized Brain-Computer Interface through Neural Manifold Alignment”
- Coffee Break
- Session Topic - Healthcare Informatics
- 11:00-11:30 - David Goldsman (Georgia Tech) – “Using Machine Learning and Simulation to Compare Increased Risk Kidney Transplant Survival to Waiting for a Non-Increased Risk Organ for Hepatitis C Negative Recipients”
- 11:30-12:00 - Melissa Knothe-Tate (UNSW) – “Towards Cellular Epidemiology of Degenerative Disease Using Geographic Information Systems, Multi-beam Electron Microscopy, and Machine Learning”
- 12:00-12:30 - Munmun De Choudhury (Georgia Tech) – “How Machine Learning and Social Media Can Transform Psychiatric Diagnosis and Care”
- 12:30-1:30 - Lunch/Posters
- 1:30-2:30 - Plenary
- Session Topic - Systems Biology
- 2:30-3:00 - Russell Schwartz (CMU) – “Learning models of clonal evolution from cancer genomic data”
- 3:00-3:30 - Mark Styczynski (Georgia Tech) – “Machine Learning in Systems-Scale Metabolic Analysis and Modeling”
- 3:30-4:00 - Matt Ruffalo (CMU) – “Network-Guided Prediction of Aromatase Inhibitor Response in Breast Cancer”
- Coffee Break
- Session Topic - Healthcare Informatics
- 4:30-5:00 - Leontios Hadjileontiadis (Khalifa University of Science and Technology) – “Analysis of keystroke dynamics in mobile touchscreen for depression detection”
- 5:00-5:30 - Asim Smailagic (CMU) – “Machine Learning and Virtual Coaches”
- 5:30-6:00 - TBD
Posters
Anis Davoudi (University of Florida) – “Characterizing Functional Status in Delirium Patients in the Intensive Care Unit Using Machine Vision Techniques”
Shivesh Chaudhary (Georgia Tech) – “Automatic identity determination of neurons in whole-brain recordings using Conditional Random Fields”
Arna Ghosh (McGill University) – “Hierarchical Deep Convolutional Network for Analysis of Motor task EEG Data”
Kathleen Bates (Georgia Tech) – “Expanding the behavior space of the worm”
Alvaro Ulloa (Geisinger) – “Electronic Health Records Simulation Framework for Unsupervised Clustering”
Yuanda Zhu (Georgia Tech) – “Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging”
Erik Jorgensen (Georgia Tech) – “A sparse modeling framework for substructure prediction in the brain”
Daniel Roudnitsky (University of Maryland) – “Comparative Machine Learning Approaches for Parkinson’s Disease Classification Using Acoustic Data”
Octavio Mesner (CMU) – “A nonparametric approach to variable selection applied to an observational clinical dataset”
Greeshma Agasthya (Geisinger) – “A machine Learning approach to understanding the importance of echo strain measurements in cardiac outcomes research”
Manar D Samad (Geisinger) – “A Machine Learning Framework to Optimize Patient Outcome Predictions Using Large Electronic Health Records and Clinically Acquired Imaging Measurements”
Luna Zhang (BigBear, Inc.) - "Multi-function Convolutional Neural Networks for More Accurate Alzheimer’s Disease Diagnosis Using Brain MRI Images than Traditional Convolutional Neural Networks"