I am a Research Fellow in Computational Mathematics at the Flatiron Institute, a part of the Simons Foundation. In 2022, I earned a PhD in Statistics from Columbia University. You can find out more by browsing this website or looking at my CV.

My primary research interest lies in statistics and probabilistic machine learning. My work bridges methodology, computation, and application through the development of probabilistic programming languages such as Stan and TensorFlow Probability. Some keywords:

• Computation: Markov chain Monte Carlo, Variational inference, (integrated) Laplace approximation, automatic differentiation
• Modeling: Bayesian workflow, Hierarchical models, ODE-based models
• Applications: Pharmacometrics, Epidemiology, Astrophysics

News

• I am on the job market this year! I am looking for research positions, including faculty positions, in statistics, data science, and machine learning which would start in the summer/fall of 2025. Feel free to reach out if you wish to share an opportunity.

• May 2025: I will attend AISTATS in Thailand to present our recent paper on Variational Inference in Location-Scale Families.

• June 2025: I will attend BayesComp in Singapore to chair a session on Parallel Computation for Markov chain Monte Carlo, and give an invited talk at the session on Advances in Variational Inference.

(last updated: February 2025)

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]

📄 (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]