About me
I am a Senior Research Engineer at Polygeist interested in all things machine learning, statistics, data science, optimization, and Monte Carlo. In the near past, I developed tools for machine learning, Bayesian inference, and computational statistics by combining concepts and techniques from optimization and Monte Carlo; see here for a recent example. In the more distant past, I worked on Markov processes, numerical methods for the analysis thereof, and applications of control theory to synthetic biology. I have also been writing a book on Markov chains, see here for the latest draft.
Interests
- Machine Learning
- Computational Statistics
- Optimization
- Monte Carlo Methods
- Control Theory
Education
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PhD in Bioengineering, 2018
Imperial College London
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MEng in Biomedical Engineering, 2012
Imperial College London
Publications
Click here to search the publications. Or you could try the tags below the list.
(2023).
Particle algorithms for maximum likelihood training of latent variable models.
In AISTATS 2023.
(2022).
Product-form estimators: exploiting independence to scale up Monte Carlo.
In Statistics and Computing.
(2021).
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems.
Under review (Accepted).
(2021).
Approximations of Countably Infinite Linear Programs over Bounded Measure Spaces.
In SIAM Journal on Optimization.
(2021).
Stationary Distributions of Continuous-Time Markov Chains: A Review of Theory and Truncation-Based Approximations.
In SIAM Review.
(2020).
Markov chains revisited.
Book draft.
(2019).
Diffusion limit for the random walk Metropolis algorithm out of stationarity.
In Annales de l’Institut Henri Poincaré, Probabilités et Statistiques.
(2019).
Bounding the stationary distributions of the chemical master equation via mathematical programming.
In The Journal of Chemical Physics.
(2019).
The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains.
In SIAM Journal on Scientific Computing.
(2018).
Non-stationary phase of the MALA algorithm.
In Stochastics and Partial Differential Equations: Analysis and Computations.
(2017).
Deterministic approximation schemes with computable errors for the distributions of Markov chains.
PhD Thesis.
(2016).
Bounding Stationary Averages of Polynomial Diffusions via Semidefinite Programming.
In SIAM Journal on Scientific Computing.
(2014).
Model Reduction of Genetic-Metabolic Networks via Time Scale Separation.
In A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations.