I am interested in multiple aspects of computational statistics and in the development of novel methodologies. My area of research falls under the general domain of “high-dimensional filtering”, with epidemiology as a real-world application (see section “Area of interests” for more details). I am also an avid programmer, with knowledge of C++, R, and Python.

# Articles in Peer-Reviewed Journals
• Scalable calibration for partially observed individualbased epidemic models through categorical approximations,
  by L. Rimella, N. Whiteley, C. Jewell, P. Fearnhead, M. Whitehouse. (JASA:T&M to appear)
• Disclosure risk assessment with Bayesian non-parametric hierarchical modelling,
  by M. Battiston, L. Rimella (Statistics and Computing)
• Simulation Based Composite Likelihood,
  by L. Rimella, C. Jewell, P. Fearnhead (Statistics and Computing)
• Hidden Markov Neural Networks,
  by L. Rimella, N. Whiteley (Entropy)
• Machine Learning Method for As-Is Tunnels Information Model Reconstruction,
  by N. Rimella, L. Rimella, A. Osello (Automation in Construction)
• A State-Space Perspective on Modelling and Inference for Online Skill Rating,
  by S. Duffield, S. Power, L. Rimella (JRSS:Series C)
• Key environmental exposure pathways to antimicrobial resistant bacteria in southern Malawi: A SaniPath approach,
  by T. Mwapasa, K. Chidziwisano, M. Mphasa, D. Cocker, L. Rimella, S. Amos, N. Feasey, T. Morse (STOTEN)
• Approximating optimal SMC proposal distributions in individual-based epidemic models,
  by L. Rimella, C. Jewell, P. Fearnhead (Statistica Sinica)
• Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods,
  by M. Whitehouse, N. Whiteley, L. Rimella (JRSS:Series B)
• Inference on Extended-Spectrum Beta-Lactamase Escherichia coli and Klebsiella pneumoniae data through SMC2,
  by L. Rimella, S. Alderton, M. Sammarro, B. Rowlingson, D. Cocker, N. Feasey, P. Fearnhead, C. Jewell (JRSS:Series C)
• Exploiting locality in high-dimensional factorial hidden Markov models,
  by L. Rimella, N. Whiteley (JMLR)

# Conference Proceedings and Discussions
• Discussion of "Regression by Composition by Farewell et al",
  by F. Gaffi, S. Legramanti, L. Rimella (JRSS:Series B to appear)
• A Comparison between Econometric and Machine Learning Approaches to S&P500 Volatility,
  by M. Selva, P. Cincinelli, L. Rimella (SIS-FENStatS 2026 to appear)
• Discussion of "Statistical aspects of the Covid-19 response by Wood et al.",
  by M. Whitehouse, N. Whiteley, L. Rimella (JRSS:Series A)
• Empirical Bayes for the Ridge Penalty in Probit Models,
  by A. Fasano, G. Rebaudo, L. Rimella (SIS 2025)
• Inference in stochastic epidemic models via multinomial approximations,
  by N. Whiteley, L. Rimella (AISTAT)

# Area of interests

1- Hidden Markov models
filtering, smoothing and inference when scaling up to high-dimensions


2- Monte Carlo methods
Sequential Monte Carlo and the development of smart proposal distributions and resampling schemes


3- Epidemiological modelling
compartmental models in epidemiology at both population and individuals level


4- Deep Learning
training different architecture of neural networks (Bayesian or not) to solve complex tasks