Talks

2024

• Integrating single-cell and spatial transcriptomics data using deep generative models. The EAC ISBA conference 2024. June 25 - 26, 2024.

• Panel discussion. Artificial Intelligence and IoT for Healthcare 2024. June 13, 2024.

• AI for science: examples from spatial transcriptomics data analysis. HKUST Scientific Computation Concentration Workshop 2024. May 4, 2024.

• Integrating single-cell and spatial transcriptomics data using deep generative models. The second Young Statisticians Spring Festival Forum. March 24, 2024.

• Spatiotemporal Imputation and Prediction of Chlorophyll-a in Pearl River Estuary with Conditional Diffusion Model. EARTH-HK Opening Ceremony and First Symposium. March 22, 2024.

• Integrating single-cell and spatial transcriptomics data using deep generative models. HKUST-Kyoto University Joint Symposium on Informatics. March 21, 2024.

• Strengthen causal inference using Mendelian randomization. Invited talk at Xian Jiao Tong University. Jan 3, 2024.

2023

• STitch3D: Construction of a 3D whole organism spatial atlas by joint modelling of multiple slices with deep neural networks. Invited talk at Xian Jiao Tong University. Dec 21, 2023.

• AI for science: examples from spatial transcriptomics data analysis. Hong Kong Laureate Forum. November 17, 2023. 

• Machine learning methods for characterization and prediction of spatio-temporal patterns. Earth-HK Planning Workshop. Sept 6, 2023.

• Strengthen causal inference by leveraging genetic data. Hangzhou International conference on Frontiers of Data Science. August 21, 2023.

• SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models. [video link]. July 6, 2023. Single-Cell Plus – Data Science Challenges in Single-Cell Research workshop in Banff from July 2 to July 7, 2023. [website]

• SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models. IAS workshop on Biological Data Science, AI and Medicine. June 15, 2023. [website]

• SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models. The 15th China Bioindustry convention, Spatial-temporal Omics. [website]June 9, 2023.

• A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models. HKUST Big data institute workshop on Big Data and Biomedical & Chemical Science. May 8. 2023. [link]

• Strengthen causal inference by leveraging genetic data. Statistics Colloquium @ Tsinghua University. April 10, 2023.

• AI for science: two examples from spatial transcriptomics data analysis. Department of Applied Mathematics Seminar. March 16, 2023.

2022

• Construction of polygenic risk scores for East Asia population by leveraging large-scale bio-bank data of European population. Asian Future Leaders Scholarship Program (AFLSP) seminar. HKUST. Dec 1. 2022. 

• Deep generative model learning and its application. Cross-disciplinary Seminar: Artificial Intelligence. Nov. 7. 2022.

• Challenges and Solutions in Atlas-scale Single-cell RNA-sequencing Data Integration and Analysis. NNI Research FReNs Seminar Series. October 21, 2022.

• Deep generative model learning and its application. Tianyuan Mathematical center in Northeast China. Northeast Normal University. August 22, 2022.

• Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets. NSFC/RGC Workshop on Single-Cell Data Science [link]. June 2, 2022.

• Strengthen causal inference using genome-wide summary statistics. The 4-th open talk at Capital of Statistics [link], May 27, 2022.

• Mendelian randomization for causal inference. The symposium on statistics and data science. Southern University of Science and Technology. May 22, 2022.

• Mendelian randomization for causal inference. Shanghai University of International Business and Economics. May 13, 2022.

• Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Tianyuan Mathematical center in central China. Wuhan University. March 31, 2022.

• Deep generative model learning and its application. Department of Mathematics, Fudan University. March 29, 2022.

• Deep generative model learning and its application. Department of Statistics, University of Science and Technology of China. March 24, 2022.

• A fast and accurate method for genetic risk prediction by leveraging biobank scale data. The CityU Day of Biostatistics. March 21, 2022. 

• Fast and accurate methods for integration of single-cell datasets. Tsinghua University. March 18, 2022.

• A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. Statistical Methods in Genetic/Genomic Studies workshop, Singapore. Jan, 4, 2022.

2021

• Mendelian Randomization for causal inference accounting for pleiotropy and sample structure using the genome-wide summary statistics. Xiamen University, Nov., 13, 2021.

• One Century Journey of Statistical Genetics: From Galton's Family Data to UKB-WeGene Data. Tianjin Normal University. Jun 10, 2020.

• Deep Generative model and its application for art synthesis. The first Victoria Peak Conference, April 26, 2021.

2020

• Deep Generative Learning via Variational Gradient Flow. Dongbei University of Finance and Economics, Nov. 13, 2020.

• MR-APSS: a unified approach to Mendelian Randomization accounting for pleiotropy, sample overlap and selection bias using the genome-wide summary data. Southwestern University of Finance and Economics. July 17, 2020.

• One Century Journey of Statistical Genetics: From Galton's Family Data to UKB-WeGene Data. Department of Statististics, Southern University of Science and Technology. Jun 30, 2020.

2019

• Deep Generative Learning via Variational Gradient Flow. The Institute of Statistics and Big Data, Renmin university of china, May 15, 2019.

2018

•  CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information. Distinguished lecture session Recent Advances in Statistical Genetics, IMS-APRM, June, 26, 2018. [link] 

2017

• Adaptive False Discovery Rate Regression With Application In Integrative Analysis of Large-Scale Genomic Data. 2017 IASC-ARS/NZSA Conference, 10-14 December, Auckland.

• Recent Advances in Statistical Genomics. 2017 IMS China, International Conference on Statistics and Probability. Nanning, Guangxi, June 29 – July 1, 2017.

• IMAC: A Statistical Framework for Integrating Multiple Annotations to Characterize Functional Roles. 2017 ICSA Applied Statistics Symposium in Chicago. June 25–June 28, 2017.

2016

• EM meets Boosting in big genomic data analysis. Invited talk at Xi’an JiaoTong University, Xi’an, Dec., 27. 2016.

• A Unified Statistical Framework for Exploring the Genetic Architecture of Human Complex Phenotypes Using Summary Statistics. Invited talk at Department of Management Sciences, City University of Hong Kong, Sept. 23, 2016.

• A statistical approach to colocalizing risk variants in multiple GWAS. Invited talk at The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting. June 29, 2016.

2015

• Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. Invited talk at The Centre for Genomic Sciences of Hong Kong University, July 17, 2015.

• IPAC: a flexible statistical approach to integrating pleiotropy and annotation for characterizing functional roles of genetic variants that underlie human complex phenotypes. Invited talk at TheWorkshop on Youth Statistician. Forum at PolyU. June, 26, 2015.

• GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation. (Invited talk at Statistics and Computational Interface to Big Data, Jan. 10, 2015)

2013

• Exploring the genetic architecture of alcohol dependence in African-Americans via analysis of a genomewide set of common variants. (Invited talk at The 1st Annual Molecular Psychiatry Meeting, November 10, 2013)

• Low-rank approximation and its application in Bioinformatics. (Invited talks at the Statistical Bioinformatics seminar at Purdue University, Oct., 22, 2013)

• Statistical methods to handle multiple correlated traits in GWAS. (Yale Center of Statistical Genomics and Proteomics, April, 20, 2013)

•  Alternating Direction Method of Multipliers.  (Yale Center of Statistical Genomics and Proteomics, Feb., 13, 2013)

2012

• Analyzing GWAS data of psychiatric disorders: Gene-gene interactions, Heritability and Risk prediction.  (Invited talk at Upenn, May 23, 2012)

• Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping. (Yale Center of Statistical Genomics and Proteomics, May, 7, 2012) [paper] [slides]

• Fast linear mixed models for genome-wide association studies. [Paper from Nature Methods] (Yale Center of Statistical Genomics and Proteomics, April 26, 2012)

• DECOLOR: Moving Object Segmentation by DEtecting Contiguous Outliers in the Low-rank Representation. [Paper][Related materials] (Yale Center of Statistical Genomics and Proteomics, Feb., 10, 2012)

2011

• SNP data analysis in genome-wide association studies. (PhD defense, HKUST, May 30, 2011)

• Learning from sparsity. (ECE Journal Club, HUKST, March, 31, 2011)

• Using genetic architecture for genomic risk prediction. (ECE, HKUST, Feb.,18, 2011)

2010

• BOOST: A fast approach to detecting gene-gene interactions in genome-wide case control study. (ECE department seminar, HKUST, Nov., 26, 2010)

• Regularization in matrix learning. (ECE Journal Club, HKUST, Nov. 12, 2010)

•  A unified framework of ensemble learning. (ECE, HKUST, May 13, 2010)

2009

•  Large-scale-inference. (ECE, HKUST, Nov., 20, 2009)

• Least angle regression, Lasso and Boosting. (ECE, HKUST, Feb., 24, 2009)

Some interesting talks from our group

2011

• X. Zhou. Spectral clustering. (ECE Journal Club, HKUST, May, 6, 2011)

2010

• X. Zhou. Random walk. (ECE Journal Club, HKUST, Dec., 10, 2010)

• X. Zhou. Energy minimization via graph cuts. (ECE Journal Club, HKUST, June, 11, 2010)