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Invited Presentations (slides are password protected for internal use)

20/4/30 Conference on "Biostatistics: Foundations and the Era of Data Science", University of Waterloo.
20/4/17 Department of Biostatistics, Ohio State University.
20/3/22 Bayesian regression and clustering models to incorporate multi-layer overlapping group structure in multi-omics applications. ENAR, Nashville.
20/2/27 Overview and recent advances of omics data integration. Big Data Conference, University of Maryland.
20/2/4 Selected examples of how new statistical methods can help biomedical research and precision medicine. Pediatric Hematology/Oncology Division, Children Hospital of Pittsburgh.
20/1/17 CAMO: Congruence Analysis of Model Organisms. Department of Biomedical Informatics, University of Pittsburgh.
20/1/14 CAMO: Congruence Analysis of Model Organisms. Children Hospital John G. Rangos, Sr. Research Seminar Lecture.
19/11/12 Cluster analysis in omics data integration. iBRIGHT (Integrative Biostatistics Research for Imaging, Genomics, & High-throughput Technologies in Precision Medicine) 2019 at MD Anderson.
19/7/31 Discussant of session: Aligning Data Normalization with Analysis Goals for Reproducible Research. JSM, Denver.
19/5/31 Heterogeneity and robust machine learning in transcriptomic analysis of multiple studies. Conference on Lifetime Data Science (LiDS).
19/3/25 Reproducibility and heterogeneity in meta-analysis and replication of transcriptomic studies. ENAR, Philadelphia.
19/3/19 Heterogeneity issue in meta-analysis and replication of transcriptomic studies. Division of Biostatistics. Medical College of Wisconsin.
18/10/29 CAMO: Congruence analysis of model organisms. School of Medicine, Florida Atlantic University.
18/10/24 Congruence analysis of model organisms (CAMO) and Bayesian variable selection incorporating multi-layer overlapping group structure (MOG). Department of Computer Science, University of Pittsburgh.
18/7/31 Sparse negative binomial model-based clustering for RNA-seq count data. JSM, Vancouver.
18/6/1 Bayesian variable selection for multi-layer overlapping group structure with omics integration. China-Taiwan Cross-Strait Conference on Probability and Statistics.
18/5/19 Bayesian variable selection for multi-layer overlapping group structure with omics integration. Hangzhou International Conference on Frontiers of Data Science.
18/5/4, 5/14, 6/7, 6/15 Recent advances in omics data integration for disease subtype clustering and biomarker detection. Sun Yat-sen University, Academia Sinica, National Tsing-Hua University and National Taiwan University.
18/3/26 Bayesian variable selection for multi-layer overlapping group structure and variable screening with multiple studies. ENAR, Atlanta.
18/1/22 Poorly mimic or greatly mimic? A model-based evaluation with functional characterization for comparison of differential transcriptomic systems across model organisms or across species. Department of Critical Care, University of Pittsburgh.
17/12/4 Poorly mimic or greatly mimic? A model-based evaluation with functional characterization for comparison of differential transcriptomic systems across model organisms or across species. Bioinformatics Seminar, UCLA.
17/10/26 A statistical evaluation pipeline of whether animal models mimic human and a Bayesian hierarchical model with multi-layer overlapping group structure for multi-omics integrative analysis. Dept of Mathematical Sciences; Dept of Bioinformatics and Computational Biology. Worcester Polytechnic Institute
17/08/02 Properties of adaptively weighted Fisher (AW-Fisher) meta-analysis method for integrating omics data: fast computing, biomarker categorization and asymptotic theory. JSM, Baltimore.
17/06/26 Bayesian hierarchical model with multi-layer overlapping group structure in omics applications. ICSA Applied Statistics Symposium, Chicago.
17/05/19 Integrative cluster and differentially co-expression network analysis methods in omics applications. Department of Biostatistics, Boston University.
17/03/17 Omics data integration for disease subtype discovery, differential co-expression network and fusion transcript detection. Department of Computational Biology, Carnegie Mellon University.
17/02/20 A joint Bayesian modeling for integrating microarray and RNA-seq transcriptomic data Dahshu Data Science Symposium: Computational Precision Health 2017, San Francisco.
16/12/20 A joint Bayesian modeling for integrating microarray and RNA-seq transcroptomic data. The 10th ICSA International Conference, Shanghai.
16/12/01 Frequentist and Bayesian approaches for characterizing heterogeneity in transcriptomic meta-analysis. Department of Mathematics, University of Maryland.
16/09/23 Biomarker detection and categorization in RNA-seq meta-analysis using Bayesian hierarchical model. Division of Biostatistics, Albert Einstein College of Medicine.
16/09/16 Frequentist and Bayesian approaches for characterizing heterogeneity in transcriptomic meta-analysis. Department of Statistics, University of Pittsburgh.
16/07/30 Integrative analysis with complex group structure in multi-level omics data. Joint Statistical Meeting, Chicago.
16/06/24 Adaptively weighted meta-analysis in -omics applications. Institute of Statistical Mathematics. Tokyo, Japan
16/05/17 MetaDCN: meta-analytic framework for differential coexpression network detection with an application to breast cancer. The GLBIO/CCBC Great Lakes Bioinformatics and the Canadian Computational Biology Conference 2016, Toronto.
16/05/9, 5/26, 6/17 Integration of experimental data or analytical pipelines for improved precision medicine. Department of Statistics, Tsinghua University, Sun Yat-sen University, Academia Sinica.
16/04/28 Adaptively weighted meta-analysis in -omics applications. Genomics workshop, International Biometric Society, Korean Region.
16/03/14 Integration of experimental data or analytical pipelines for improved precision medicine. Department of Biomedical Informatics, University of Pittsburgh.
16/03/06-09 Adaptively weighted meta-analysis in omics applications. ENAR, Austin.
15/11/14 Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data. Women's Cancer Research Center (WCRC) Retreat.
15/11/1-3 Integrative clustering of multi-level omics data using overlapping group lasso. iBRIGHT (Integrative Biostatistics Research for Imaging, Genomics, & High-throughput Technologies in Precision Medicine) 2015 at MD Anderson.
15/07/26 Identifying differential disease subnetworks in multiple transcriptomic studies via a meta-analysis framework. 60th ISI World Statistics Congress, Rio de Janeiro, Brazil.
15/06/14-17 Integrative multi-omics clustering for disease subtype discovery by overlapping group lasso regularization and tight clustering. 24th ICSA Applied Statistics Symposium, Fort Collins
15/04/30 Methods for heterogeneity, robust hypothesis and information censoring issues in transcriptomic meta-analysis. Department of Biostatistics, University of Florida
15/03/25 Statistical and Computational Methods for OMICS Data Integration. Campbell Family Research Institute Special Seminar, University of Toronto
15/02/12 Methods for heterogeneity, robust hypothesis and information censoring issues in transcriptomic meta-analysis. Department of Statistics, National Chiao Tung University, Taiwan
14/06/18 MetaOmics applications in cancer research (slides). 2014 UPCI retreat.
14/03/17 Statistical methods for disease subtype discovery by multi-study and mutli-omics data integration (slides). ENAR, Baltimore.
14/03/12 Career talk: Some reflection and experiences on career planning and interdisciplinary research (slides). Department of Life Sciences, National Taiwan University.
14/03/12 Omics data integrative analysis in major depressive disorder and RNA-seq fusion gene detection in prostate cancer (slides). Department of Life Sciences, National Taiwan University, Taiwan.
14/02/28 Disease subtype discovery from omics data integration and RNA-seq fusion marker detection (slides). Department of Human Genetics, University of Pittsburgh.
13/12/20 Statistical methods for disease subtype discovery by multi-study and mutli-omics data integration. 2013 ICSA International Conference, Hong Kong.
13/10/5 Statistical opportunities in high-throughput genomic data analysis and recent advances in omics data integration. Invited talk for ASA Cleveland Chapter's Symposium: Statistics at the Crossroads: Its Multifaceted Impact on the Society.
13/08/05 Genomic data integration to improve disease subtype discovery. Joint Statistical Meeting, Montreal, Canada.
13/06/13 Genomic meta-analysis methods and applications. Dept of Statistics, National Cheng-Kung University, Taiwan.
13/06/10 Machine learning bias correction for minimal-error classifier and a meta-analysis framework for sparse K-means in genomic applications. Institute of Statistical Science, Academia Sinica, Taiwan.
13/06/04 Genomic meta-analysis methods and applications in psychiatric research. School of Public Health, National Taiwan University, Taiwan.
13/05/30 Genomic meta-analysis methods and applications. Dept of Math, National Sun Yat-sen University, Taiwan.
13/05/27 Machine learning bias correction for minimal-error classifier and a meta-analysis framework for sparse K-means in genomic applications. Dept of Statistics, Chiao-Tung University, Taiwan.
13/05/24 Integrative Genomic Modeling and Data Analysis in Major Depressive Disorder. Colloquium, School of Medicine, National Yang-Ming University, Taiwan.
13/05/22 Genomic meta-analysis methods and applications. Dept of Math, National Taiwan University, Taiwan.
13/05/14 Integrative Genomic Modeling and Data Analysis in Major Depressive Disorder and molecular aging prediction. Dept of Math, Tamkang University, Taiwan.
13/03/18 Age X Disease omics association project. Translational Neuroscience Program (TNP) seminar, University of Pittsburgh.
12/12/17 Some new advances in genomic meta-analysis. Workshop on Statistical Frontiers, Institute of Statistical Science, Academia Sinica, Taiwan.
12/10/18 Hypothesis settings and characterizations of genomic meta-analysis methods and some new developments. University of Pittsburgh, Department of Biostatistics.
12/09/27 Hypothesis settings and characterizations of genomic meta-analysis methods and some new developments. Penn State, Department of Statistics.
12/09/06 Hypothesis settings and characterizations of genomic meta-analysis methods and some new developments. USC, Department of Molecular and Computational Biology.
12/07/29 Integrative Genomic Modeling and Data Analysis in Major Depressive Disorder. Joint Statistical Meeting, San Diego.
12/06/24 Hypothesis setting, order statistics and a Bayesian hierarchical model for robust genomic meta-analysis. 2012 ICSA Applied Statistics Symposium, Boston.
12/03/21 Student presentations. ENAR, Washington DC.
11/12/18 Order statistics for robust genomic meta-analysis. 2011 Taipei International Statistical Symposium, Academia Sinica, Taiwan.
11/07/01 Introduction to microarrays and proteomics. PSC MARC Workshop - Biological Sciences; Pittsburgh Supercomputing Center
11/03/10 Quality assessment and principal component analysis when combining multiple microarray studies. Institute of Statistical Science, Academia Sinica, Taiwan
10/12/10 Information Integration and Statistical Meta-analysis for Combining Multiple Genomic Studies: MetaGenomics. Senior Vice Chancellor Seminar Series
10/11/16 Statistal integration of multiple transcriptomic studies. Center for Genetics and Molecular Medicine, University of Louisville
10/06/22 Genomic meta-analysis for dimension reduction and gene clustering.ICSA 2010 APPLIED STATISTICS SYMPOSIUM
10/05/14 An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies.Department of Mathematics, National Taiwan University, Taiwan
10/05/12 Meta-analysis for pathway enrichment analysis when combining multiple microarray studiesInstitute of Statistical Science, Academia Sinica, Taiwan
10/05/11 Genomic meta-analysis for dimension reduction and gene clusteringDepartment of Mathematics, Tamkang University, Taiwan
10/05/10 Career talk for undergraduate students: career in Biostatistics and Bioinformatics.Department of Mathematics, National Taiwan University, Taiwan
09/04/02 Optimally weighted statistic for combining multiple genomic studies.Department of Statistics, University of Pittsburgh
08/07/24 Issues in Combining Multiple Genomic Studies.Institute of Statistical Science, Academia Sinica, Taiwan
08/07/14-19 Nonparametric meta-analysis for identifying signature genes in the integration of multiple genomic studies7th World Congress in Probability and Statistics, Singapore
08/06/4-7 An optimal-weight statistic for meta-analysis of multiple genomic studies17th ICSA Applied Statistics Symposium
08/03/16-19 Inferring the true correlation in cross-species microarray data.ENAR 2008, Arlington, Virginia
08/02/13 Meta-analysis for cross-prediction and DE gene detection in multiple genomic data sets.Simmons Center for Interstitial Lung Disease, UPMC
07/07/29 Statistical framework for integrative analysis of multiple gene expression. JSM 2007, Salt Lake City, Utah
07/06/27 Statistical Framework for Integrative Analysis of Multiple Gene Expression Profiles.2007 Taipei International Statistical Symposium, Academia Sinica, Taiwan.
07/06/22
07/06/21
One-day miniworkshop: Microarray Data Analysis (link)
Meta-analysis for multiple microarray data sets.
Institute of Biomedical Informatics, National Yang-Ming University, Taiwan.
06/11/28 Statistical integrative analysis of multiple expression profile and biological data: two examples in cancer and aging.Dept. of Computational Biology, University of Pittsburgh.
06/09/07 Which Missing Value Imputation Method to Use in Expression Profiles: a Comparative Study and Two Selection Schemes.Dept. of Biostatistics, University of Pittsburgh.
06/06/15 Comparative study of gene clustering in microarray and penalized and weighted K-means.ICSA 2006 Applied Statistics Symposium.
06/03/01 Evaluation and comparison of gene clustering methods in microarray analysis.(slides) Dept of Statistics, Texas A&M.
05/12/23 Integrated Clustering and Classification Analysis for Learning Inducing Structural Motifs contributing to MS/MS Fragmentation Patterns.Dept of Statistics, National Chiao Tung University.
05/12/21 Opportunities and challenges in Biostatistics and Bioinformatics for Math major students. (slides)Dept of Mathematics, National Taiwan University.
05/12/20
05/12/21
Penalized and Weighted K-means for Clustering with Noises and Prior Information IncorporationInstitute of Statistical Science, Academia Sinica.
Dept of Mathematics, National Taiwan University.
05/11/02 A generalized form of K-means. (slides) Neyman Seminar, Dept of Statistics, UC Berkeley.
05/08/08 Penalized and weighted K-means. JSM 2005
04/12/15 Tutorial: Statistical analysis and software for Affymetrix GeneChip arrays and some recent advances. (slides) (R code)
Tutorial: Classification and clustering problems in microarray analysis and some recent advances. (slides)
2004 Taipei Symposium on Statistical Genomics (Academia Sinica, Taiwan)
04/12/14 A data mining scheme for identifying peptide structural motifs behind different MS/MS fragmentation intensity. National Health Research Institutes, Taiwan
04/12/07 A comparative review of gene clustering in expression profile. (slides) ICARCV 2004 at Kunming
04/08/10 Tight Clustering and Penalized Weighted K-means applied in genomic research. Laboratory of DNA Information Analysis, University of Tokyo
04/06/02 Tight Clustering: a method for extracting stable and tight patterns in expression profiles. IPAM Functional Genomics 2004 Reunion Conference (UCLA)
04/05/10 Tight Clustering: a method for extracting stable and tight patterns in expression profiles.(slides) International Conference on Analysis of Genomic Data (Harvard University)
03/12/15~20 Tight Clustering: a method for extracting stable and tight patterns in expression profiles. National Taiwan University
Academia Sinica
National Chiao Tung University
03/08/03 A method for tight clustering: with application to microarray.Joint Statistical Meetings 2003
01/06~02/04 Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. (slides)UCLA, Brighan Women Hospital, Harvard University, MIT