My research concerns statistics and probabilistic machine learning, often with an emphasis on Bayesian modeling. Some keywords:

• Markov chain Monte Carlo
• Variational inference
• Bayesian workflow
• Automatic differentiation

I am also keen to work on scientific applications, and I have had fruitful collaborations in Pharmacometrics, Epidemiology, and Astrophysics.

Papers

📄 (2025) Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix. C.Margossian and L. Saul. PMLR: Artificial Intelligence and Statistics (oral) [pdf, Code]

📄 (2024) Variational Inference for Uncertainty Quantification: an Analysis of Trade-Offs. C. Margossian, L. Pillaud-Vivien and L. Saul. [pdf, 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. Bayesian Analysis [article, pdf, code]

📄 (2024) EigenVI: score-based variational inference with orthogonal function expansions. D. Cai, C. Modi, C. Margossian, R. Gower, D. Blei and L. Saul. Advances in Neural Information Processing Systems (spotlight) [pdf, code]

📄 (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 [article, pdf, code]

📄 (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 (spotlight) [article, pdf, code]

📄 (2024) Amortized Variational Inference: when and why? C. Margossian and D. Blei. PMLR: Uncertainty in Artificial Intelligence [article,pdf, 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 [pdf]

📄 (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 [article, pdf, 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) [article, pdf, 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 [article, pdf, code]

📄 (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, pdf, code, software]

📄 (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, pdf, R package]

📄 (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) Bayesian Workflow. A. Gelman et al. [pdf]

📄 (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, pdf, 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, pdf]

📄 (2019) A Review of automatic differentiation and its efficient implementation. C. Margossian. Wiley interdisciplinary reviews: data mining and knowledge discovery [article, pdf]

📄 (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]

Book

📚 (2022) Modernizing Markov chains Monte Carlo for scientific and Bayesian modeling C. Margossian. PhD Thesis, Columbia University [pdf]

Posters

📊 (2024) Monitoring Nonstationary Variance to Assess Convergence of MCMC. E. Mokel and C. Margossian. International Society of Bayesian Analysis (ISBA) world meeting. Best poster award.

📊 (2023) Parallelization for Markov chains with heterogeneous runtimes. S. du Ché and C. Margossian. BayesComp. [poster]

📊 (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]

📊 (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. American Conference on Pharmacometrics. [poster]

📊 (2016) Stan functions for pharmacometrics. C. Margossian and W. Gillespie. American Conference on Pharmacometrics. [poster]

Technical reports and notebooks

📝 (2023) General adjoint-differentiated Laplace approximation. C. Margossian. [pdf]

📝 (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]

📝 (2022) Efficient Automatic Differentiation of Implicit Functions. C. Margossian and M. Betancourt [pdf]

📝 (2020) The Discrete adjoint method: efficient derivatives for functions of discrete sequences. M. Betancourt, C. Margossian and V. Leos-Barajas. [pdf]

📝(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]

📝(2018) Computing Steady states with Stan’s nonlinear algebraic solver. C. Margossian. Stan Con Asilomar 2018. [article, code, talk]

📝(2017) Differential equation based models in Stan. C. Margossian and W. Gillespie. Stan Con 2017. [article, code, talk]