10+ years experience on research for health data science. Familiar with and Well-Experienced with the Following Things:Data Analysis TechnologiesClassic data mining/machine learning algorithms (NN, SVM, Decision Tree, Logistic Regression...) Deep learning approaches (Feed Forward NN, CNN, RNN, GNN...)Graph based learningMatrix and tensor related technologies (Low-Rank Approximation, NMF/NTF, Sparse Learning...) Frequent itemset/pattern mining (APRIORI, PrefixSpan, SPAM...) Large Scale Learning (Stochastic Optimization, Online Learning, Hashing/Indexing...)Similarity/Distance Metric Learning Optimization (Gradient descent, Proximal methods...)Mechanistic models (Differential equation...)Health Informatics:Feature engineering for better analytics with big medical dataSimilarity analytics for personalized medicineCare Coordination and Pathway AnalysisDrug Theraputic Indication/Side-Effect/Interaction Prediction and Association StudyDrug Comparative Effectiveness Research Real-World Evidence for Drug DevelopmentPatient Utilization AnalysisPredictive ModelingRisk Stratification/Cohort AnalysisDisease Progression ModelingDrug Knowledge BaseProgramming Languages:Python, MATLAB, SQL, JAVA, R.Tutorials (send me email if you want any slides):Knowledge and Information Management with Graphs and Matrices. CIKM08, SDM09, ICDM09Distance Metric Learning for Data Mining. SDM12Recent Advances in Applied Matrix Technologies. SDM13Applied Matrix Analytics: Recent Advance and Case Studies. ICDM 2013.Large Scale Similarity Learning and Indexing. CIKM 2013.Feature Engineering in Health Informatics. PAKDD 2014.Data Analytics in Healthcare: Problems, Challenges and Future Directions. CIKM 2014.Data Analytics with Electronic Health Records. AAAI 2015.Deep Learning for Healthcare. CHASE 2017, MedInfo 2017.Learning with networks. KDD 2019.For more details, refer to https://wcm-wanglab.github.io/index.html