| 24/7/9 | Outcome-guided Disease Subtyping by Generative Model and Weighted Joint Likelihood in Omics Applications. | International Conference for Statistics and Data Science (ICSDS), Taiwan. |
| 23/11/7 | Selective Introduction to Multi-Omits Integrative Analysis. | Data Integration Workshop. Indian Statistical Institute (Remote). |
| 23/10/19 | Recent advances in the problem of combining p-values. | Department of Applied Mathematics, Hong Kong Polytechnic University. |
| 23/10/18 | Recent advances in the problem of combining p-values. | Department of Statistics, Chinese University of Hong Kong. |
| 23/10/16 | Congruence analysis of model organisms and selection of cancer models towards precision medicine. | Joint Bioconductor Asia - Hong Kong Bioinformatics Symposium. |
| 23/8/18 | Congruence analysis for model organisms and multi-facet clustering in omics data. | Conference in Celebration of Wing Wong's 70th Birthday. |
| 23/8/5 | Congruence analysis of animal and cancer models by omics data. | JSM, Toronto. |
| 23/7/16 | On P-Value Combination Of Independent And Non-Sparse Signals: Asymptotic Efficiency & Fisher Ensemble. | ISI World Statistics Congress, Ottawa. |
| 23/4/12 | Transcriptomic congruence analysis for evaluating model organisms. | Department of Population and Quantitative Health Sciences. Case Western Reserve University. |
| 23/3/29 | Transcriptomic congruence analysis for evaluating model organisms. | Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati. |
| 23/1/13 | Omics congruence analysis of animal models and cancer models. | School of Public Health, National Taiwan University, Taiwan. |
| 22/4/28 | On p-value combination of independent and frequent signals. | Division of Biostatistics and Bioinformatics, Penn State University, Virtual. |
| 22/4/28 | Two p-value combination methods with emphasis on omics applications | Waterloo Conference in Statistics, Actuarial Science, and Finance, Department of Statistics and Actuarial Science, University of Waterloo, Virtual. |
| 22/4/22 | Machine learning for aging research | Epidemiology of Aging Workshop. Department of Epidemiology, University of Pittsburgh. |
| 22/3/23 | On p-value combination of independent and frequent signals. | Biostatistics Division, University of Miami, Virtual. |
| 22/1/3 | Recent advances in p-value combination methods with emphasis on omics applications. | Statistical Methods in Genetic/Genomic Studies Workshop, National University of Singapore, Virtual. |
| 21/9/27 | Genetic, transcriptomic and metabolomics studies in aging and circadian analysis. | Pittsburgh PEPPER Center Annual Retreat, Virtual. |
| 21/9/13 | Robust testing with transformation of heavy-tailed distribution for combining weak, sparse and dependent p-values . | Department of Statistics, Chinese University of Hong Kong, Virtual. |
| 21/3/24 | Outcome-guided disease subtyping for high-dimensional omics data. | Department of Epidemiology and Biostatistics, University of Arizona, Virtual. |
| 21/3/16 | Outcome-guided disease subtyping for high-dimensional omics data. | ENAR, Virtual. |
| 20/12/15 | Robust testing with transformation of heavy-tailed distribution for combining dependent p-values. | ICSA Applied Statistics Symposium, Virtual. |
| 20/12/4 | Simultaneous estimation of number of clusters and feature sparsity in high-dimensional cluster analysis. | Department of Biostatistics and Health Data Science, Indiana University, Virtual. |
| 20/8/3 | Outcome-guided disease subtyping for high-dimensional omics data. | JSM, Virtual. |
| 20/3/22 | Cluster analysis in omics data integration. | ENAR, Virtual. |
| 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 studies | Institute of Statistical Science, Academia Sinica, Taiwan |
| 10/05/11 | Genomic meta-analysis for dimension reduction and gene clustering | Department 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 studies | 7th World Congress in Probability and Statistics, Singapore |
| 08/06/4-7 | An optimal-weight statistic for meta-analysis of multiple genomic studies | 17th 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 Incorporation | Institute 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 |
