Principal ML Engineer
Benedict W. J. Irwin
AI/ML and simulations based solutions for drug discovery.
I am familiar with a range of conventional ML methods, as well as deep learning, sparse data methods, large datasets, imputation, graph convolution networks, transformers, recurrent networks, generative methods, recommender systems, kernel density methods and distribution matching, Bayesian optimisation, Gaussian processes, and generalised additive models. I have some knowledge of time series modelling, signal processing methods, image based methods. I am mathematically fluent and can comfortably generate new solutions where there is little to no existing literature. This extends to non-machine learning algorithms such as integer programming, trees and data structures.
I completed my PhD in the Theory of Condensed Matter (TCM) group at the Cavendish Laboratory at the University of Cambridge. I derived the Atomwise Free Energy Perturbation (AFEP) method to calculate approximate decompositions of the free energy of solvation and binding and have run thousands of molecular dynamics simulations. I am familiar with free energy perturbation, thermodynamic integration various computational chemistry and simulation methods.
I am familiar with AWS, AzureML/ADO, high performance computing, parallel algorithms (OpenMP, MPI) and software development in a variety of languages (C, C++, Python, Perl). I have strong mathematical skills and can use various computer algebra systems (Mathematica, Maple, Matlab). I'm generally interested in mathematics, machine learning and artificial intelligence.
I have reviewed journal papers for JMLR, JCIM, In Silico Pharmacology, and have made many additions to the OEIS.AI/ML and simulations based solutions for drug discovery. I am familiar with a range of conventional ML methods, as well as deep learning, sparse data methods, large datasets, imputation, graph convolution networks, transformers, recurrent networks, generative methods, recommender systems, kernel density methods and distribution matching, Bayesian optimisation, Gaussian processes, and generalised additive models. I have some knowledge of time series modelling, signal processing methods, image based methods. I am mathematically fluent and can comfortably generate new solutions where there is little to no existing literature. This extends to non-machine learning algorithms such as integer programming, trees and data structures. I completed my PhD in the Theory of Condensed Matter (TCM) group at the Cavendish Laboratory at the University of Cambridge. I derived the Atomwise Free Energy Perturbation (AFEP) method to calculate approximate decompositions of the free energy of solvation and binding and have run thousands of molecular dynamics simulations. I am familiar with free energy perturbation, thermodynamic integration various computational chemistry and simulation methods. I am familiar with AWS, AzureML/ADO, high performance computing, parallel algorithms (OpenMP, MPI) and software development in a variety of languages (C, C++, Python, Perl). I have strong mathematical skills and can use various computer algebra systems (Mathematica, Maple, Matlab). I'm generally interested in mathematics, machine learning and artificial intelligence. I have reviewed journal papers for JMLR, JCIM, In Silico Pharmacology, and have made many additions to the OEIS.
I am familiar with a range of conventional ML methods, as well as deep learning, sparse data methods, large datasets, imputation, graph convolution networks, transformers, recurrent networks, generative methods, recommender systems, kernel density methods and distribution matching, Bayesian optimisation, Gaussian processes, and generalised additive models. I have some knowledge of time series modelling, signal processing methods, image based methods. I am mathematically fluent and can comfortably generate new solutions where there is little to no existing literature. This extends to non-machine learning algorithms such as integer programming, trees and data structures.
I completed my PhD in the Theory of Condensed Matter (TCM) group at the Cavendish Laboratory at the University of Cambridge. I derived the Atomwise Free Energy Perturbation (AFEP) method to calculate approximate decompositions of the free energy of solvation and binding and have run thousands of molecular dynamics simulations. I am familiar with free energy perturbation, thermodynamic integration various computational chemistry and simulation methods.
I am familiar with AWS, AzureML/ADO, high performance computing, parallel algorithms (OpenMP, MPI) and software development in a variety of languages (C, C++, Python, Perl). I have strong mathematical skills and can use various computer algebra systems (Mathematica, Maple, Matlab). I'm generally interested in mathematics, machine learning and artificial intelligence.
I have reviewed journal papers for JMLR, JCIM, In Silico Pharmacology, and have made many additions to the OEIS.AI/ML and simulations based solutions for drug discovery. I am familiar with a range of conventional ML methods, as well as deep learning, sparse data methods, large datasets, imputation, graph convolution networks, transformers, recurrent networks, generative methods, recommender systems, kernel density methods and distribution matching, Bayesian optimisation, Gaussian processes, and generalised additive models. I have some knowledge of time series modelling, signal processing methods, image based methods. I am mathematically fluent and can comfortably generate new solutions where there is little to no existing literature. This extends to non-machine learning algorithms such as integer programming, trees and data structures. I completed my PhD in the Theory of Condensed Matter (TCM) group at the Cavendish Laboratory at the University of Cambridge. I derived the Atomwise Free Energy Perturbation (AFEP) method to calculate approximate decompositions of the free energy of solvation and binding and have run thousands of molecular dynamics simulations. I am familiar with free energy perturbation, thermodynamic integration various computational chemistry and simulation methods. I am familiar with AWS, AzureML/ADO, high performance computing, parallel algorithms (OpenMP, MPI) and software development in a variety of languages (C, C++, Python, Perl). I have strong mathematical skills and can use various computer algebra systems (Mathematica, Maple, Matlab). I'm generally interested in mathematics, machine learning and artificial intelligence. I have reviewed journal papers for JMLR, JCIM, In Silico Pharmacology, and have made many additions to the OEIS.