PRESENTATIONS

  1. Higgins, I., Kundu, S., Pal, S., Guo, Y. (2017) Anatomically Informed Estimation of Functional Brain Networks. Joint Statistical Meetings, Baltimore, MD, July, 2017.

  2. Higgins, I., and Guo, Y. (2016) Evaluating Nodal Differential Degree Centrality via Statistically Motivated Random Networks. Organization for Human Brain Mapping, Vancouver, Canada, June 2017.

  3. Lukemire, J., Verma, A., Shi, R., and Guo, Y. (2017) A Hierarchical Covariate-Adjusted ICA Matlab Toolbox for Investigating Differences in Brain Networks. Organization for Human Brain Mapping, Vancouver, Canada, June 2017.

  4. Higgins, I., Kundu, S., Pal, S., Guo, Y. (2017) Penalized Estimation of Functional Brain Networks in the Presence of Anatomical Information. Statistical Methods in Imaging, Pittsburgh, PA, May, 2017.

  5. Guo, Y. and Dai, T. (2016) Statistical Methods for Assessing Reproducibility in Multicenter Neuroimaging Studies. Joint Statistical Meeting (JSM), Chicago, August, 2016.

  6. Guo, Y., Pal, S., and Kang, J. (2016) 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, Cleveland Clinic, August, 2016.

  7. Guo, Y., and Kemmer, P. (2017) Statistical Methods for Brain Network Analysis Using Multimodal Imaging Data. 1. International Biometrics Society (ENAR) Short Course, Washington DC, March, 2017

  8. Guo, Y. (2017) Statistical ICA methods for brain network analysis using neuroimaging data. Department of Statistics, Florida State University, March, 2017.

  9. Guo, Y. (2017) Statistical methods for improving reliability in investigation of brain networks. 1. Institute of Bioinformatics, University of Georgia, April, 2017.

  10. Guo, Y. Pal, S., and Kang, J. (2017 forthcoming) A New Unified ICA Framework for Decomposing Multimodal Neuroimaging Data. New Innovations and Challenges in Computational Neuroscience”, Joint Statistical Meetings, Baltimore, Maryland, USA, Aug., 2017.

  11. Guo Y. (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.

  12. Guo Y. (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.

  13. Guo Y, Pal S. and Kang J. (2016) Investigating differences in brain functional networks using a hierarchical covariate-adjusted ICA model. Invited talk at Department of Mathematics and Statistics, University of Alberta, Canada, March, 2016.

  14. Guo Y. (2016) New ICA methods for exploring brain connectivity using neuroimaging data. Invited talk at Department of Biostatistics, Brown University, April, 2016.

  15. Kemmer, P.B. and Guo, Y. (2016) A joint model for assessing the link between functional and structural brain connectivity. ENAR, Austin, TX, March 2016.

  16. Kemmer, P.B. and Guo, Y. (2016) A joint model for assessing the link between functional and structural brain connectivity. ENAR, Austin, TX, March 2016.

  17. Guo, Y. (2015) A hierarchical ICA framework for functional magnetic resonance imaging data. Invited Talk at Statistics Alumni Symposium of Renmin University of China, Beijing, China, June, 2015.

  18. Guo, Y. (2015) Estimating brain functional networks in fMRI: a hierarchical ICA framework vs. TC-GICA. Invited talk at International Mathematics Society (IMS)-China International Conference on Statistics and Probability, Kunmin,China, July, 2015.

  19. Guo, Y. (2015) New agreement methods for accommodating censored observations. In Invited Session, 2015 International Chinese Statistical Association (ICSA) Statistics Conference, July, 2015, Shanghai, China.

  20. Dai T and Guo Y. (2015) Statistical Methods for Assessing Reproducibility in Multicenter Neuroimaging Studies. Oral presentation in contributed papers session "Methods to Assess Agreement". ENAR spring meeting, Miami, Fl, USA, Mar 2015.

  21. Dai T, Guo Y, Peng L and Manatunga AK . (2015) Statistical Methods for Assessing Reproducibility in Multicenter Neuroimaging Studies. Poster presentation in NSF/Anderson Student Poster Session session. SRCOS Summer Research Conference, Carolina Beach, NC, USA, Jun 2015.

  22. Jeffers, C. and Kang, J. (2015) A Bayesian high-dimensional Poisson graphical model for identifying functional co-activation patterns. Joint Statistical Meetings, Seattle, Washington, Aug 2015.

  23. Jeffers, C. and Kang, J. (2015) A Bayesian high-dimensional Poisson graphical model for identifying functional co-activation patterns. Contributed Poster at ASA Workshop on Statistical Methods in Imaging at University of Michigan in Ann Arbor, MI, May 2015.

  24. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2015) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. Joint Statistical Meetings (JSM), Seattle, WA, Aug 2015.

  25. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2015) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. Student Paper Competition Winner, Statistics in the 2015 Imaging Workshop, Ann Arbor, MI, May 2015.

  26. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2015) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. Clint Miller outstanding poster award winner, SRCoS Summer Research Conference, Carolina Beach, NC, June 2015.

  27. Ran, S. and Kang, J. (2015) Thresholded multiscale Gaussian Processes with Application to Bayesian Feature Selection in Massive Neuroimaging Data. Section on Bayesian Statistical Science student travel award winner, Joint Statistical Meetings (JSM), Seattle, WA, Aug 2015.

  28. Wang, Y and Guo Y. (2015) Statistical methods for characterization and classification of brain functional networks: with application to Philadelphia Neurodevelopmental Cohort study. Poster Presentation for the Charles C. Shepard Award, Atlanta, USA, May 2015.

  29. Guo Y. (2014) Computationally Efficient Estimation and Inference Methods for Hierarchical ICA of fMRI Data.” In Invited Session “ Statistical Challenges in Big Imaging Data Analysis. Joint Statistical Meetings, Boston, MA, USA, Aug., 2014.

  30. Guo Y. (2014) Statistical methods for assessing agreement among correlated survival outcomes. Invited talk in Ordered Data Analysis, Models and Health Research Methods Conference, University of Texax at Dallas, March, 2014.

  31. Kang, J., Liu, H., Bowman, D. and Mayberg, H. (2014) Learning brain connectivity network of depression via multi-attribute canonical correlation graphs” in invited session “Modeling Neurological diseases with imaging data. International Biometrics Society (ENAR) Meeting, Baltimore, MD, March, 2014.

  32. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2014) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. International Biometrics Society (ENAR) Meeting, Baltimore, MD, March, 2014.

  33. Ran, S. (2014) Modeling covariate effects in group independent component analysis with applications to functional magnetic imaging. International Biometrics Society (ENAR) Meeting, Baltimore, MD, March, 2014.

  34. Guo, Y. (2013) A hierarchical group ICA model for studying brain functional networks in fMRI studies. Invited talk at Department of Mathematical Sciences, Middle Tennessee State University, Oct, 2013.

  35. Xue, W. Kang, J., Bowman, D., Wager, D.T., Guo, J. (2013) Identifying functional co-activation patterns in neuroimaging studies via Poisson graphical models. in contributed session “Novel spatial methods for neuroimaging data” Joint Statistical Meetings, Montreal, Canada, August, 2013.

  36. Guo, Y. (2013) A statistical method for predicting clinical outcomes using resting-state fMRI. Invited talk in Topic Contributed Session “Challenges and Statistical Approaches of Resting-state fMRI”. Joint Statistical Meetings, Montreal, Canada, Aug., 2013.

  37. Guo, Y. (2013) Group level blind source separation via independent component analysis in neuroimaging studies. In invited session “Biostatistics: recent advances in statistical neuro-imaging research”. Southern Regional Council on Statistics (SRCOS) Summer Research Conference, 2013.

  38. Guo, Y. (2013) A hierarchical group ICA regression model for fMRI data. Invited talk at the mini-symposium “Statistical Computing Methods for Medical Imaging Data Processing”, Society for Industrial and Applied Mathematics (SIAM) 37th Annual Conference, Knoxville, TN, March, 2013.

  39. Guo, Y. (2013) A new statistical method for modeling covariate effects in group ICA for fMRI data. In invited session “Functional Neuroimaging Decompositions”, International Biometrics Society (ENAR) Meeting, Orlando, FL, March, 2013.

  40. Kang, J. (2013) A Bayesian Spatial Positive-Definite Matrix Regression Model for Diffusion Tensor Imaging. in contributed session “Bayesian analysis of high-dimensional data” International Biometrics Society (ENAR) Meeting, Orlando, FL, March, 2013.

  41. Guo, Y. (2012) A new probabilistic group ICA method for modeling between-subject variability in brain functional networks. In Topic Contributed Session “Novel developments in statistical blind source separation and independent component analysis.” International Biometrics Society (ENAR) Meeting, Washington DC, April, 2012.

  1. Higgins, I., Kundu, S., Pal, S., Guo, Y. (2017) Anatomically Informed Estimation of Functional Brain Networks. Joint Statistical Meetings, Baltimore, MD, July, 2017.

  2. Higgins, I., and Guo, Y. (2016) Evaluating Nodal Differential Degree Centrality via Statistically Motivated Random Networks. Organization for Human Brain Mapping, Vancouver, Canada, June 2017.

  3. Guo Y. and Dai T. (2016) Statistical Methods for Assessing Reproducibility in Multicenter Neuroimaging Studies. Joint Statistical Meeting (JSM), Chicago, August, 2016.

  4. Guo Y. Pal S., and Kang J. (2016) 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, Cleveland Clinic, August, 2016.

  5. Guo Y. and Kemmer, P. (2017) Statistical Methods for Brain Network Analysis Using Multimodal Imaging Data. 1. International Biometrics Society (ENAR) Short Course, Washington DC, March, 2017

  6. Guo, Y. (2017) Statistical ICA methods for brain network analysis using neuroimaging data. Department of Statistics, Florida State University, March, 2017.

  7. Guo, Y. (2017) Statistical methods for improving reliability in investigation of brain networks. 1. Institute of Bioinformatics, University of Georgia, April, 2017.

  8. Guo, Y. Pal, S., and Kang, J. (2017 forthcoming) A New Unified ICA Framework for Decomposing Multimodal Neuroimaging Data. New Innovations and Challenges in Computational Neuroscience”, Joint Statistical Meetings, Baltimore, Maryland, USA, Aug., 2017.

  1. Guo Y. (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.

  2. Guo Y. (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.

  3. Guo Y, Pal S. and Kang J. (2016) Investigating differences in brain functional networks using a hierarchical covariate-adjusted ICA model. Invited talk at Department of Mathematics and Statistics, University of Alberta, Canada, March, 2016.

  4. Guo Y. (2016) New ICA methods for exploring brain connectivity using neuroimaging data. Invited talk at Department of Biostatistics, Brown University, April, 2016.

  5. Kemmer, P.B. and Guo, Y. (2016) A joint model for assessing the link between functional and structural brain connectivity. ENAR, Austin, TX, March 2016.

  6. Kemmer, P.B. and Guo, Y. (2016) A joint model for assessing the link between functional and structural brain connectivity. ENAR, Austin, TX, March 2016.

  7. Guo, Y. (2015) A hierarchical ICA framework for functional magnetic resonance imaging data. Invited Talk at Statistics Alumni Symposium of Renmin University of China, Beijing, China, June, 2015.

  8. Guo, Y. (2015) Estimating brain functional networks in fMRI: a hierarchical ICA framework vs. TC-GICA. Invited talk at International Mathematics Society (IMS)-China International Conference on Statistics and Probability, Kunmin,China, July, 2015.

  9. Guo, Y. (2015) New agreement methods for accommodating censored observations. In Invited Session, 2015 International Chinese Statistical Association (ICSA) Statistics Conference, July, 2015, Shanghai, China.

  10. Dai T and Guo Y. (2015) Statistical Methods for Assessing Reproducibility in Multicenter Neuroimaging Studies. Oral presentation in contributed papers session "Methods to Assess Agreement". ENAR spring meeting, Miami, Fl, USA, Mar 2015.

  11. Dai T, Guo Y, Peng L and Manatunga AK . (2015) Statistical Methods for Assessing Reproducibility in Multicenter Neuroimaging Studies. Poster presentation in NSF/Anderson Student Poster Session session. SRCOS Summer Research Conference, Carolina Beach, NC, USA, Jun 2015.

  12. Jeffers, C. and Kang, J. (2015) A Bayesian high-dimensional Poisson graphical model for identifying functional co-activation patterns. Joint Statistical Meetings, Seattle, Washington, Aug 2015.

  13. Jeffers, C. and Kang, J. (2015) A Bayesian high-dimensional Poisson graphical model for identifying functional co-activation patterns. Contributed Poster at ASA Workshop on Statistical Methods in Imaging at University of Michigan in Ann Arbor, MI, May 2015.

  14. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2015) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. Joint Statistical Meetings (JSM), Seattle, WA, Aug 2015.

  15. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2015) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. Student Paper Competition Winner, Statistics in the 2015 Imaging Workshop, Ann Arbor, MI, May 2015.

  16. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2015) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. Clint Miller outstanding poster award winner, SRCoS Summer Research Conference, Carolina Beach, NC, June 2015.

  17. Ran, S. and Kang, J. (2015) Thresholded multiscale Gaussian Processes with Application to Bayesian Feature Selection in Massive Neuroimaging Data. Section on Bayesian Statistical Science student travel award winner, Joint Statistical Meetings (JSM), Seattle, WA, Aug 2015.

  18. Wang, Y and Guo Y. (2015) Statistical methods for characterization and classification of brain functional networks: with application to Philadelphia Neurodevelopmental Cohort study. Poster Presentation for the Charles C. Shepard Award, Atlanta, USA, May 2015.

  1. Guo Y. (2014) Computationally Efficient Estimation and Inference Methods for Hierarchical ICA of fMRI Data.” In Invited Session “ Statistical Challenges in Big Imaging Data Analysis. Joint Statistical Meetings, Boston, MA, USA, Aug., 2014.

  2. Guo Y. (2014) Statistical methods for assessing agreement among correlated survival outcomes. Invited talk in Ordered Data Analysis, Models and Health Research Methods Conference, University of Texax at Dallas, March, 2014.

  3. Kang, J., Liu, H., Bowman, D. and Mayberg, H. (2014) Learning brain connectivity network of depression via multi-attribute canonical correlation graphs” in invited session “Modeling Neurological diseases with imaging data. International Biometrics Society (ENAR) Meeting, Baltimore, MD, March, 2014.

  4. Kemmer, P.B., Guo, Y., and Bowman, F. D. (2014) Statistical Approaches for Exploring Brain Connectivity with Multi-Modal Neuroimaging Data. International Biometrics Society (ENAR) Meeting, Baltimore, MD, March, 2014.

  5. Ran, S. (2014) Modeling covariate effects in group independent component analysis with applications to functional magnetic imaging. International Biometrics Society (ENAR) Meeting, Baltimore, MD, March, 2014.

  6. Guo, Y. (2013) A hierarchical group ICA model for studying brain functional networks in fMRI studies. Invited talk at Department of Mathematical Sciences, Middle Tennessee State University, Oct, 2013.

  7. Xue, W. Kang, J., Bowman, D., Wager, D.T., Guo, J. (2013) Identifying functional co-activation patterns in neuroimaging studies via Poisson graphical models. in contributed session “Novel spatial methods for neuroimaging data” Joint Statistical Meetings, Montreal, Canada, August, 2013.

  8. Guo, Y. (2013) A statistical method for predicting clinical outcomes using resting-state fMRI. Invited talk in Topic Contributed Session “Challenges and Statistical Approaches of Resting-state fMRI”. Joint Statistical Meetings, Montreal, Canada, Aug., 2013.

  9. Guo, Y. (2013) Group level blind source separation via independent component analysis in neuroimaging studies. In invited session “Biostatistics: recent advances in statistical neuro-imaging research”. Southern Regional Council on Statistics (SRCOS) Summer Research Conference, 2013.

  10. Guo, Y. (2013) A hierarchical group ICA regression model for fMRI data. Invited talk at the mini-symposium “Statistical Computing Methods for Medical Imaging Data Processing”, Society for Industrial and Applied Mathematics (SIAM) 37th Annual Conference, Knoxville, TN, March, 2013.

  11. Guo, Y. (2013) A new statistical method for modeling covariate effects in group ICA for fMRI data. In invited session “Functional Neuroimaging Decompositions”, International Biometrics Society (ENAR) Meeting, Orlando, FL, March, 2013.

  12. Kang, J. (2013) A Bayesian Spatial Positive-Definite Matrix Regression Model for Diffusion Tensor Imaging. in contributed session “Bayesian analysis of high-dimensional data” International Biometrics Society (ENAR) Meeting, Orlando, FL, March, 2013.

  13. Guo, Y. (2012) A new probabilistic group ICA method for modeling between-subject variability in brain functional networks. In Topic Contributed Session “Novel developments in statistical blind source separation and independent component analysis.” International Biometrics Society (ENAR) Meeting, Washington DC, April, 2012.