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I am currently employed as a Quantitative Researcher at Brevan Howard. Prior to joining Brevan Howard, I successfully completed my PhD in Statistics at the London School of Economics. During my doctoral journey, I was advised by Kostas Kalogeropoulos and Pauline Barrieu.

My PhD studies primarily focused on the application of Sequential Bayesian Learning for State Space Models. This involved developing methodologies to estimate model parameters in both batch and time series settings, with a particular emphasis on latent variable models.

In addition to my academic pursuits, I have actively contributed to the open source community. You can find some of my notable contributions here. Furthermore, if you would like to review my detailed qualifications and professional experiences, you can download a copy of my CV here.

Patrick Aschermayr's Projects

baytesdiff.jl icon baytesdiff.jl

Wrappers to differentiate `ModelWrapper` structs, see ModelWrappers.jl.

baytesfilters.jl icon baytesfilters.jl

A library to perform particle filtering for one parameter in a `ModelWrapper` struct.

baytesmcmc.jl icon baytesmcmc.jl

A library to perform MCMC proposal steps on `ModelWrapper` structs, see ModelWrappers.jl.

baytespmcmc.jl icon baytespmcmc.jl

A library to perform particle MCMC proposal steps for parameter in a `ModelWrapper` struct, see [ModelWrappers.jl](https://github.com/paschermayr/ModelWrappers.jl).

baytessmc.jl icon baytessmc.jl

BaytesSMC.jl is a library to perform SMC proposal steps on `ModelWrapper` structs, see ModelWrappers.jl. Kernels that are defined in BaytesMCMC.jl and BaytesFilters.jl can be used inside this library.

bijectors.jl icon bijectors.jl

Implementation of normalising flows and constrained random variable transformations

modelwrappers.jl icon modelwrappers.jl

ModelWrappers.jl is a utility package that makes it easier to work with Model parameters stated as (nested) NamedTuples.

montecarlomeasurements.jl icon montecarlomeasurements.jl

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

parameterhandling.jl icon parameterhandling.jl

WIP package with some experiments in handling parameters for models. This might need to be two packages.

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