"Exploring Brain Function and Connectivity with fMRI: From Data Characteristics to Statistical and AI Methods". ” in “Statistical and AI Methods for Health Data Science” seminar series hosted by Stats Up AI. Youtube.
“Multi-View LOCUS for Investigating Latent Traits Underlying Multi-View Brain Connectome”, in invited session "Statistics in Neuroscience", Computational and Methodological Statistics (CMStatistics 2024), London, UK, Dec., 2024.
“A Regularized Low-Rank Blind Source Separation Framework for Unveiling Hidden Sources of Brain Functional Connectome”, Department of Biostatistics, Brown University, April, 2024.
“Causal Learning of Brain Connectivity with Neuroimaging Data”, in invited session "Advances in Causality with Applications to Brain Imaging", International Biometrics Society ENAR Meeting, March, 2024.
“Emerging Directions in Statistical and Computational Methodologies in Biomedical Imaging Discussion panel”, Joint Statistical Meetings (JSM), Toronto, Canada, Aug., 2023.
“Statistical learning for multimodal brain connectomes using neuroimaging”. Statistical Methods in Imaging (SMI) Conference, Minneapolis, MN, May, 2023.
“Statistical learning with neuroimaging for reliable mapping of human brain connectome”. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, April, 2023.
“Mapping the connectome: statistical learning for reliable brain network analysis”. International Biometrics Society ENAR Meeting, Nashville, TN, March, 2023.
“A regularized blind source separation method for probing human whole-brain connectome”. 2022 IEEE International Conference on Big Data (IEEE BigData 2022), Osaka, Japan.
“Statistical learning with neuroimaging for reliable and reproducible brain network analysis”, Second Penn Conference on Big Data in Population Health Sciences, Sep., 2022.
“Enhancing the reliability of brain connectomics research using large-scale neuroimaging studies”, Joint Statistical Meetings (JSM), Baltimore, MD, Aug., 2022.
“A sparse blind source separation method for probing human whole-brain connectome”. The 5th International Conference on Econometrics and Statistics (EcoSta 2022), Kyoto, Japan, June, 2022.
“Statistical learning with neuroimaging for reliable and reproducible brain network analysis”. The University of Georgia (UGA) Data Science and AI Seminars, May, 2022.
“A sparse blind source separation method for probing human whole-brain connectome”. International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021). London, UK, Dec. 2021.
“A regularized blind source separation with low-rank structure for investigating brain connectivity traits”. In Invited Session “Statistical methods for complex imaging data”, Joint Statistical Meetings (JSM), Aug., 2020.
"Statistical methods for reliable and reproducible brain network analysis”, In Invited Session “Advancing the statistical analysis of neuroimaging data”, Joint Statistical Meetings (JSM), Denver, CO, Aug., 2019. PDF of the talk
“A longitudinal independent component analysis framework”, Statistical Methods in Imaging Annual Meeting, June 2019, University of California at Irvine.
“Statistical methods for exploring brain networks using multimodality neuroimaging”, In Invited Session "Translational Methods for the Assessment of Brain Function", Joint Statistical Meetings (JSM), Vancouver, CA, Aug., 2018.
“Imaging analytics for investigating brain functional and structural connections”, In invited session "Recent Developments in Statistical Analysis of Brain Data", International Biometrics Society (ENAR) Meeting, Atlanta, GA, March, 2018.
“A New Unified ICA Framework for Decomposing Multimodal Neuroimaging Data” In Invited Session “New Innovations and Challenges in Computational Neuroscience”, Joint Statistical Meetings (JSM), Baltimore, Maryland, USA, Aug., 2017.
“New ICA methods for more effective decomposition of neuroimaging data”, Challenges and Advances on Big Data in Neuroimaging Conference, jointly sponsored by Cleveland Clinic and American Statistical Association (ASA), Cleveland Clinic, August, 2016.
“Statistical Methods for Assessing Reproducibility in Multicenter Neuroimaging Studies”, Joint Statistical Meeting (JSM), Chicago, August, 2016.
“A novel distributional ICA model for multimodal neuroimaging data”. In invited session “Computational-Intensive Bayesian Techniques and Neurostatistics”, International Biometrics Society (ENAR) Meeting, Austin, TX, March, 2016.
“Exploring the brain connectivity: questions, challenges and recent findings ” . Banff International Research Station (BIRS) workshop “Mathematical and Statistical Challenges in Neuroimaging Data Analysis”, January, 2016, Banff, Alberta, Cananda.
Copyright 2019 Ying Guo. All Rights Reserved.