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Dr A. (Reza) Mohammadi

Assistant Professor of Statistics
Faculty of Economics and Business
Section Business Analytics

Visiting address
  • Plantage Muidergracht 12
  • Room number: M 4.11
Postal address
  • Postbus 15953
    1001 NL Amsterdam
  • Profile

    I am an Assistant Professor of Statistics at the Business Analytics Section of the University of Amsterdam. As a statistician and data scientist, my research interests lie in developing efficient computational methods for inference and learning from high-dimensional multivariate data that routinely arise in econometrics, machine learning, neuroscience, and health care. My current research is focused on developing Bayesian statistical methods in graphical models for multivariate statistical analysis to understand the underlying mechanisms in complex systems. These methods have a wide range of applications, such as health care to capture brain connectivity to treat Alzheimer's. 

    Short Bio

    I joined the Business Analytics Section of the University of Amsterdam as an Assistant Professor in November 2017. During 2016-2017, I worked as a postdoctoral researcher as a statistician at the Department of Methodology and Statistics at the Tilburg University and Jheronimus Academy of Data Science (JADS). In 2015, I received my Ph.D. in Statistics from the University of Groningen, my thesis entitled: Bayesian Model Determination in Complex Systems.

  • Software

    I am a core developer of the following open source software which are used in my publications:

  • Publications

    2024

    • Vogels, L. F. O., Schoonhoven, M., Mohammadi, A., & Birbil, S. I. (2024). Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2024.2395504

    2023

    2019

    2018

    2017

    • Mohammadi, A., Abegaz, F., van den Heuvel, E., & Wit, E. C. (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models. Journal of the Royal Statistical Society. Series C: Applied Statistics, 66(3), 629-645. https://doi.org/10.1111/rssc.12171

    2016

    • Mohammadi, A., & Kaptein, M. (2016). Contributed discussion on article by Pratola [Comment on "M.T. Pratola, Efficient metropolis-hastings proposal mechanisms for Bayesian regression tree models"]. Bayesian Analysis, 11(3), 938-940. https://doi.org/10.1214/16-BA999H

    2015

    2014

    • Mohammadi, A., & Wit, E. C. (2014). Contributed discussion on article by Finegold and Drton: Comment by Abdolreza Mohammadi and Ernst C. Wit. Bayesian Analysis, 9(3), 577-579. https://doi.org/10.1214/13-BA856D

    2013

    • Mohammadi, A., Salehi-Rad, M. R., & Wit, E. C. (2013). Using mixture of Gamma distributions for Bayesian analysis in an M/G/1 queue with optional second service. Computational Statistics, 28(2), 683-700. https://doi.org/10.1007/s00180-012-0323-3

    2012

    • Mohammadi, A., & Salehi-Rad, M. R. (2012). Bayesian inference and prediction in an M/G/1 with optional second service. Communications in Statistics: Simulation and Computation, 41(3), 419-435. https://doi.org/10.1080/03610918.2011.588358

    2017

    Prize / grant

    • Mohammadi, R. (2018). Travel Grant for COSTNET Sandpit Meeting at Oxford.
    • Mohammadi, R. (2018). Short Term Scientific Misson Grant.
    • Mohammadi, R. (2017). Travel Grant for COSTNET17 Conference.

    Talk / presentation

    • Mohammadi, R. (speaker) (10-2017). Bayesian Structure Learning of High-dimensional Graphical Models with Application to Brain Connectivity, COSTNET17, Mallorca. http://costnet2017.ifisc.uib-csic.es/
    • Mohammadi, R. (speaker) (22-8-2016). Bayesian Modelling of Dupuytren Disease Using Gaussian Copula Graphical Models, Network Science and its Applications, Cambridge. https://www.newton.ac.uk/event/snaw02
    • Mohammadi, R. (speaker) (8-8-2015). Bayesian Structure Learning in Graphical Models, Joint Statistical Meetings, Seattle. http://ww2.amstat.org/meetings/jsm/2015/
    • Mohammadi, R. (speaker) (7-2015). Bayesian Structure Learning in Sparse Graphical Models, European Meeting of Statisticians, amsterdam.
    • Mohammadi, R. (speaker) (7-2014). Bayesian Copula Gaussian Graphical Modelling, 29th International Workshop on Statistical Modelling, Gottingen. http://www.uni-goettingen.de/en/432678.html
    • Mohammadi, R. (speaker) (3-2013). Gaussian graphical model determination based on birth-death MCMC inference, STAR meeting day, Leiden.
    • Mohammadi, R. (speaker) (3-2013). Gene Network inference for high-dimensional problems, Gene Network Inference Meeting 2013, Paris.
    • Mohammadi, R. (speaker) (12-2011). Using mixture of Gammas for Bayesian analysis in an M/G/1 queue with optional second service, 4th International Conference of the ERCIM WG on COMPUTING & STATISTICS, Londen. http://www.cfe-csda.org/ercim11/sessions.php?ShowERCIM=Show+ERCIM+programme
    • Mohammadi, R. (speaker) (8-2010). On Bayesian estimation for the M/G/1‭ ‬queue with optional second service, International Conference of Mathematical Sciences, Istanbul.

    Others

    • Mohammadi, R. (participant) (2-7-2017 - 7-7-2017). International Workshop on Statistical Modelling 2017, Groningen. I was a local organizer of the International Workshop on Statistical Modelling 2017 (organising a conference, workshop, ...). https://iwsm2017.webhosting.rug.nl/

    2015

    • Mohammadi, A. (2015). Bayesian Model Determination in Complex Systems.

    2017

    2024

    • Vogels, L., Mohammadi, R., Schoonhoven, M. & Birbil, Ş. . (2024). Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison. Taylor & Francis. https://doi.org/10.6084/m9.figshare.26880600.v1

    2021

    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
  • Ancillary activities
    • No ancillary activities