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Research

I am currently working under the supervision of Dr. Martin Lysy and Dr. Reza Ramezan. My research interests include:

  • Computational Bayesian methods
  • Computational neuroscience
  • Spatial models

Publications

Simone Paradiso, Maria DiMarco, Meixi Chen, Glen McGee, Will J. Percival. (2024). A convenient approach to characterizing model uncertainty with application to early dark energy solutions of the Hubble tension. Monthly Notices of the Royal Astronomical Society. arXiv

Meixi Chen, Martin Lysy, David Moorman, Reza Ramezan. (2023). Functional Connectivity: Continuous-Time Latent Factor Models for Neural Spike Trains. Proceedings of 2023 Conference on Cognitive Computational Neuroscience. link 

Reza Ramezan, Meixi Chen, Martin Lysy, and Paul Marriott. (2022). A Multivariate Point Process Model for Simultaneously Recorded Neural Spike Trains. Proceedings of 2022 Conference on Cognitive Computational Neuroscience. link

Meixi Chen, Reza Ramezan and Martin Lysy (2023+). Fast Approximate Inference for Spatial Extreme Value Models. Submitted. arXiv

Mhd. Wasem Alsabbagh, Feng Chang, Martin Cooke, Susan J. Elliott, and Meixi Chen (2021). National trends in population rates of opioid-related mortality, hospitalization and emergency department visits in Canada between 2000 and 2017. A population-based study. Addiction. link

Software

SpatialGEV: An efficient computational method for fitting spatial extreme value models in R. Github. CRAN.

fastr: Factor Analysis of Spike Trains in R. Github.

Academic Presentations

  • 2023 Conference on Cognitive Computational Neuroscience (Poster presentation), Oxford
    • Title: Functional Connectivity: Continuous-Time Latent Factor Models for Neural Spike trains
  • 2023 Statistical Society of Canada Annual Meeting (Contributed talk), Ottawa
    • Title: Decoding Neural Population Dynamics Through Continuous-Time Latent Factor Models (Awared Best Student Research Presentation)
  • 2022 Winter Meeting of Canadian Society of Mathematics (Invited talk), Toronto
    • Title: Decoding Neural Population Dynamics Through Latent Factor Models
  • 2022 The Third Waterloo Student Conference in Statistics, Actuarial Science and Finance (Contributed talk), Waterloo
    • Title: Decoding Multi-Neuronal Activities Through Statistical Models (Honorable mention for best presentation)
  • 2022 International Society for Bayesian Analysis World Meeting (Poster presentation), Montreal
    • Title: Fast Approximate Inference for Spatial Extreme Value Models
  • 2022 Statistical Society of Canada Annual Meeting (Contributed talk), Virtual
    • Title: Decoding Multi-Neuronal Activities Through Latent Factor Models
  • 2021 Statistical Society of Canada Annual Meeting (Contributed talk), Virtual
    • Title: Fast Approximate Inference for Spatial Extreme Value Models
  • 2021 Department of Statistics and Actuarial Science Research Presentation Day (Contributed talk), Virtual
    • Title: Fast Approximate Inference for Spatial Extreme Value Models (Awarded one of the five winners for best presentation)
  • 2020 Annual Canadian Statistics Student Conference (Poster presentation), Virtual
    • Title: On Classical Conditioning: Multiscale Modeling in Non-homogeneous Poisson Process (Awarded 1st prize for the best Master’s poster presentation)
  • 2020 International Conference on Mathematical Neuroscience (Poster presentation), Virtual
    • Title: On Classical Conditioning: Multiscale Modeling in Non-homogeneous Poisson Process