Research

Keywords: Markov chain Monte Carlo; variational inference; (integrated) Laplace approximation; automatic differentiation; Bayesian hierarchical models; ODE-based models; Bayesian workflow; Ising, Potts, and spin glass models.

Writing

Google scholar page

Preprints:

  • (2024) An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations. C. Margossian, L. Pillaud-Vivien and L. Saul. [preprint, Code]
  • (2024) Nested $\widehat R$: Assessing the convergence of Markov chain Monte Carlo when running many short chains. C. Margossian, M. Hoffman, P. Sountsov, L. Riou-Durand, A. Vehtari and A. Gelman [preprint, code]
  • (2023) General adjoint-differentiated Laplace approximation. C. Margossian. [preprint]
  • (2022) Efficient Automatic Differentation of Implicit Functions. C. Margossian and M. Betancourt [preprint]
  • (2021) Simulating Ising and Potts models at critical and cold temperatures using auxiliary Gaussian variables. C. Margossian and S. Mukherjee. [preprint]
  • (2020) Bayesian Workflow. A. Gelman et al. [preprint]
  • (2020) The Discrete adjoint method: efficient derivatives for functions of discrete sequences. M. Betancourt, C. Margossian and V. Leos-Barajas. [preprint]

Published papers:

  • (2024) Listening to the Noise: Blind Denoising with Gibbs Diffusion. D. Heurtel-Depeiges, C. Margossian, R. Ohana and B. Regaldo-Saint Blancard. PMLR: International Conference on Machine Learning [preprint]
  • (2024) Batch and match: black-box variational inference with a score-based divergence. D. Cai, C. Modi, L. Pillaud-Vivien, C. Margossian, R. Gower, D. Blei and L. Saul. PMLR: International Conference on Machine Learning [preprint]
  • (2024) Amortized Variational Inference: when and why? C. Margossian and D. Blei. PMLR: Uncertainty in Artificial Intelligence [preprint, code, talk]
  • (2024) For how many iterations should we run Markov chain Monte Carlo? C. Margossian and A. Gelman. Handbook of Markov chain Monte Carlo, (upcoming) 2nd Edition. [preprint]
  • (2023) Variational Inference with Gaussian Score Matching. C. Modi, C. Margossian, Y. Yao, R. Gower, D. Blei and L. Saul. Advances in Neural Information Processing Systems [preprint, code]
  • (2023) The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference. C. Margossian and L. Saul. PMLR: Uncertainty in Artificial Intelligence (Oral) [paper, preprint, code, talk]
  • (2022) Adaptive Tuning for Metropolis Adjusted Langevin Trajectories. L. Riou-Durand, P. Sountsov, J. Vogrinc, C. Margossian and S. Power. PMLR: Artificial Intelligence and Statistics [paper, preprint, code]
  • (2021) Fast methods for posterior inference of two-group normal-normal models. P. Greengard, J. Hoskins, C. Margossian, A. Gelman and A. Vehtari. Bayesian Analysis. [article, preprint, R package]
  • (2022) Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I. C. Margossian, Y. Zhang and W. Gillespie. CPT: Pharmacometrics & Systems Pharmacology. [article, preprint, code, software]
  • (2021) Bayesian Workflow for disease transmission modeling in Stan. L. Grinsztajn, E. Semenova, C. Margossian and J. Riou. Statistics in medicine. [article, preprint, code, talk]
  • (2020) Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. C. Margossian, A. Vehtari, D. Simpson and R. Agrawal. Advances in Neural Information Processing Systems. [article, poster, preprint, code]
  • (2020) Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modelling study in Hubei, China and six regions of Europe. J. Riou et al. PLOS Medicine. [article, preprint]
  • (2019) A Review of automatic differentiation and its efficient implementation. C. Margossian. Wiley interdisciplinary reviews: data mining and knowledge discovery. [article, preprint]
  • (2014) Planet Hunters. VII. Discovery of a new low-mass, low-density (PH3 C) orbiting Kepler-289 with mass measurements of two additional plamets of two additional planets (PH3 B and D). J. Schmitt et al. Astrophysical Journal. [article]

Books:

  • (2022) Modernizing Markov chains Monte Carlo for Scientific and Bayesian modeling. C. Margossian. PhD Thesis in Statistics, Columbia University, Graduate School of Arts and Sciences. [book, erratum]
  • (2022+) Bayesian Workflow. A. Gelman et al. In progress

Conference posters and notebooks:

  • (2023) Parallelization for Markov chains with heterogeneous runtimes. S. du Ché abd C.Margossian. BayesComp. [poster]
  • (2023) Assessing the Convergence of Markov chain Monte Carlo when running many short chains. C. Margossian, M. Hoffman, P. Sountsov, L. Riou-Durand, A. Vehtari and A. Gelman. BayesComp. [poster]
  • (2022) Approximate Bayesian inference for latent Gaussian models in Stan – two years later. C. Margossian, S. Bronder, A. Vehtari, D. Simpson and R. Agrawal. [notebook, code]
  • (2021) Developping a model of SARS-CoV-2 viral dynamics under monoclonal antibody treatment. A. Marc, M. Kerioui, C. Margossian, J. Bertrand, P. Maisonnasse, Y. Aldon, R. Sanders, M. Van Gils, R. Le Grand and J. Guedj. Population Approach Group in Europe.
  • (2021) Solving ODEs in a Bayesian context: challenges and opportunities. C. Margossian, L. Zhang, S. Weber and A. Gelman. Population Approach Group in Europe. [poster]
  • (2020) Bayesian model of planetary motion: exploring ideas for a modeling workflow when dealing with ordinary differential equations and multimodality. C. Margossian and A. Gelman. Stan Case Studies 7. [article, code]
  • (2020) Approximate Bayesian inference for latent Gaussian models in Stan. C. Margossian, A. Vehtari, D. Simpson and R. Agrawal. presented at Stan Con 2020 – Recommendation: use updated 2022 notebook!. [notebook, code, talk]
  • (2018) Computing Steady states with Stan’s nonlinear algebraic solver. C. Margossian. Stan Con Asilomar 2018. [article, code, talk]
  • (2017) Gaining Efficiency by combining analytical and numerical methods to solve ODEs: implementation in Stan and application to Bayesian PK/PD modeling. C. Margossian and W. Gillespie. Journal of Pharmacokinetics and Pharmacodynamics, presented at the American Conference on Pharmacometrics 8. [poster]
  • (2017) Differential equation based models in Stan. C. Margossian and W. Gillespie. Stan Con 2017. [article, code, talk]
  • (2016) Stan functions for pharmacometrics. C. Margossian and W. Gillespie. Journal of Pharmacokinetics and Pharmacodynamics, presented at the American Conference on Pharmacometrics 7. [poster]

Technical reports:

  • The Stan Book. Stan development team. [manual]
  • Torsten User Manual. W. Gillespie, Y. Zhang, C. Margossian, and Metrum Research Group. [manual]
  • (2018) Technical appendix for Torsten. C. Margossian. in progress. [technical report]
  • Contributing New Functions to Stan. Stan development team. [wiki page]

Software

  • Stan: a probabilistic programing language. Stan development team. [Home page]
  • Torsten: a pharmacometrics library for Stan. W. Gillespie, Y. Zhang, C. Margossian, and Metrum Research Group. [GitHub]
  • mrgsolve: pharmacometrics and quantitative systems pharmacology simulation in R. K. Baron and contributors. [Home page]