Past PSI Events

Conferences

PSI Training Course: Introduction to Machine Learning

Dates: 
Session 1 - Monday 13th October 2025
Session 2 - Tuesday 14th October 2025
Session 3 - Wednesday 15th October 2025
Session 4 - Thursday 16th October 2025
Time: 09:00 - 12:30 BST
Location: Online via Zoom

Who is this event intended for? 
This course is aimed at statisticians who are new to or with limited experience of machine learning. 

What is the benefit of attending? 

Attendees will learn about a range of topics in machine learning, including practical sessions in R.

Overview

Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).

Cost

Early Bird PSI Members: £320 +VAT
PSI Members: £360 +VAT

Early Bird Non-PSI Members: £430 +VAT
Non-PSI Members: £470 +VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2026.

Registration

Early Bird registration closes on Friday 5th September.
To register for this event, please click here.

Agenda

Day 1: ML Foundation

  • Definitions and terminology
  • Key aspects of ML workflow
    • Planning
    • Pre-processing
    • Data splitting
    • Modelling, cross validation, hyperparameter estimation
    • Evaluation
    • Reporting
  • “ML in a day”. Overview of simple ML workflow using R packages dplyr, caret, ggplot2 using logistic regression. This will be used as a starting point to exemplify key aspects of ML, and as a point of comparison for further methods such as Elastic Net (regularised regression), gradient boosting and neural networks.
  • Common pitfalls and key considerations for using ML models in practice, and reviewing ML analyses, with a focus on the analysis of clinical trial data

Day 2: Supervised learning

  • What is supervised learning?
  • Definitions and examples of scenarios
  • Supervised learning workflow (short recap)
  • Supervised Algorithms:
    • LASSO and Elastic Net
    • Decision trees and Random Forest
    • Support Vector Machines
  • Evaluation and interpretation of these algorithms
  • Practical session in R: Application of above methods in caret

Day 3: Unsupervised learning

  • Definitions and comparison to supervised learning
  • K-means, PCA, Non-linear approaches such as tSNE
  • Worked examples in R

Day 4: Neural Networks and Deep Learning

  • DEMO: ML Using Caret in R
    • Preparing data: data splitting and preprocessing
    • Building and comparing different ML algorithms
  • Introduction to neural networks and basic architectures
  • Deep learning, Large Language models, Computer Vision
  • Discussion of Machine Learning vs Statistics: what are the differences, how much to they overlap, when to use what 

Speaker details

Speaker

Biography

Abstract

20240513-Jolyon Faria-5043Jolyon Faria
Data Science Director, AstraZeneca

Jolyon Faria is in the Clinical Data Sciences Lung Team within Oncology Data Science & AI, Oncology R&D, in AstraZeneca. His role is to lead and perform retrospective analyses of AZ clinical trials, Real World Data and multimodal data, with a focus on Statistics and Machine Learning, to inform the AZ Strategy for instance for PhIII Investment Decisions. He has a background in Biology: PhD (Univ. Leeds); Postdoc (Princeton Univ.), and Applied Statistics: (MSc Univ. Oxford) and is a Chartered Statistician (CSTAT; Royal Statistical Society, UK).
Moira_Verbelen
Moira Verbelen
Computational Methodology Lead, UCB


Moira Verbelen is Computational Methodology Lead in the Advanced Methods and Data Science team at UCB and is passionate about adopting and implementing cutting-edge AI methods in pharmaceutical research, in particular delivering AI solutions throughout all phases of clinical development. With a background bridging Pharmaceutical Sciences and Biostatistics, Moira holds a PhD from King’s College London, specializing in Machine Learning applied to Pharmacogenetics

This session introduces supervised learning in the context of pharmaceutical data analysis, focusing on its definition and practical applications. We'll briefly discuss the supervised learning workflow before diving into key algorithms including LASSO, Elastic Net, Decision Trees, Random Forests, and Support Vector Machines. Emphasis will be placed on how to evaluate and interpret these models effectively. Participants will gain hands-on experience using the caret package in R.
leoedit

Leo Souliotis
Associate Director, RWE, AstraZeneca

Leonidas Souliotis is part of the CDS Hematology team within the Oncology Data Science at AstraZeneca. His role includes developing statistical and ML methods and tools for evidence synthesis in the Heme space. He is also leading an internal course on how to use Python for Data Analysis. He is a Data Camp instructor, after developing the “Efficient Python coding in pandas". He has a background in Statistics: MSc (Imperial College London), PhD (University of Warwick).

  

 
danedit

Dan de Vassimon Manela
UCB

Dan de Vassimon Manela is a Machine Learning R&D and Innovation Lead at UCB and is interested in using Probabilistic Machine Learning methods to solve clinical problems across the pharmaceutical development chain. He has a background in Physics (BA, MSc Cambridge) and Statistical Machine Learning (MSci UCL).

  

 

Scientific Meetings

PSI Training Course: Introduction to Machine Learning

Dates: 
Session 1 - Monday 13th October 2025
Session 2 - Tuesday 14th October 2025
Session 3 - Wednesday 15th October 2025
Session 4 - Thursday 16th October 2025
Time: 09:00 - 12:30 BST
Location: Online via Zoom

Who is this event intended for? 
This course is aimed at statisticians who are new to or with limited experience of machine learning. 

What is the benefit of attending? 

Attendees will learn about a range of topics in machine learning, including practical sessions in R.

Overview

Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).

Cost

Early Bird PSI Members: £320 +VAT
PSI Members: £360 +VAT

Early Bird Non-PSI Members: £430 +VAT
Non-PSI Members: £470 +VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2026.

Registration

Early Bird registration closes on Friday 5th September.
To register for this event, please click here.

Agenda

Day 1: ML Foundation

  • Definitions and terminology
  • Key aspects of ML workflow
    • Planning
    • Pre-processing
    • Data splitting
    • Modelling, cross validation, hyperparameter estimation
    • Evaluation
    • Reporting
  • “ML in a day”. Overview of simple ML workflow using R packages dplyr, caret, ggplot2 using logistic regression. This will be used as a starting point to exemplify key aspects of ML, and as a point of comparison for further methods such as Elastic Net (regularised regression), gradient boosting and neural networks.
  • Common pitfalls and key considerations for using ML models in practice, and reviewing ML analyses, with a focus on the analysis of clinical trial data

Day 2: Supervised learning

  • What is supervised learning?
  • Definitions and examples of scenarios
  • Supervised learning workflow (short recap)
  • Supervised Algorithms:
    • LASSO and Elastic Net
    • Decision trees and Random Forest
    • Support Vector Machines
  • Evaluation and interpretation of these algorithms
  • Practical session in R: Application of above methods in caret

Day 3: Unsupervised learning

  • Definitions and comparison to supervised learning
  • K-means, PCA, Non-linear approaches such as tSNE
  • Worked examples in R

Day 4: Neural Networks and Deep Learning

  • DEMO: ML Using Caret in R
    • Preparing data: data splitting and preprocessing
    • Building and comparing different ML algorithms
  • Introduction to neural networks and basic architectures
  • Deep learning, Large Language models, Computer Vision
  • Discussion of Machine Learning vs Statistics: what are the differences, how much to they overlap, when to use what 

Speaker details

Speaker

Biography

Abstract

20240513-Jolyon Faria-5043Jolyon Faria
Data Science Director, AstraZeneca

Jolyon Faria is in the Clinical Data Sciences Lung Team within Oncology Data Science & AI, Oncology R&D, in AstraZeneca. His role is to lead and perform retrospective analyses of AZ clinical trials, Real World Data and multimodal data, with a focus on Statistics and Machine Learning, to inform the AZ Strategy for instance for PhIII Investment Decisions. He has a background in Biology: PhD (Univ. Leeds); Postdoc (Princeton Univ.), and Applied Statistics: (MSc Univ. Oxford) and is a Chartered Statistician (CSTAT; Royal Statistical Society, UK).
Moira_Verbelen
Moira Verbelen
Computational Methodology Lead, UCB


Moira Verbelen is Computational Methodology Lead in the Advanced Methods and Data Science team at UCB and is passionate about adopting and implementing cutting-edge AI methods in pharmaceutical research, in particular delivering AI solutions throughout all phases of clinical development. With a background bridging Pharmaceutical Sciences and Biostatistics, Moira holds a PhD from King’s College London, specializing in Machine Learning applied to Pharmacogenetics

This session introduces supervised learning in the context of pharmaceutical data analysis, focusing on its definition and practical applications. We'll briefly discuss the supervised learning workflow before diving into key algorithms including LASSO, Elastic Net, Decision Trees, Random Forests, and Support Vector Machines. Emphasis will be placed on how to evaluate and interpret these models effectively. Participants will gain hands-on experience using the caret package in R.
leoedit

Leo Souliotis
Associate Director, RWE, AstraZeneca

Leonidas Souliotis is part of the CDS Hematology team within the Oncology Data Science at AstraZeneca. His role includes developing statistical and ML methods and tools for evidence synthesis in the Heme space. He is also leading an internal course on how to use Python for Data Analysis. He is a Data Camp instructor, after developing the “Efficient Python coding in pandas". He has a background in Statistics: MSc (Imperial College London), PhD (University of Warwick).

  

 
danedit

Dan de Vassimon Manela
UCB

Dan de Vassimon Manela is a Machine Learning R&D and Innovation Lead at UCB and is interested in using Probabilistic Machine Learning methods to solve clinical problems across the pharmaceutical development chain. He has a background in Physics (BA, MSc Cambridge) and Statistical Machine Learning (MSci UCL).

  

 

Training Courses

PSI Training Course: Introduction to Machine Learning

Dates: 
Session 1 - Monday 13th October 2025
Session 2 - Tuesday 14th October 2025
Session 3 - Wednesday 15th October 2025
Session 4 - Thursday 16th October 2025
Time: 09:00 - 12:30 BST
Location: Online via Zoom

Who is this event intended for? 
This course is aimed at statisticians who are new to or with limited experience of machine learning. 

What is the benefit of attending? 

Attendees will learn about a range of topics in machine learning, including practical sessions in R.

Overview

Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).

Cost

Early Bird PSI Members: £320 +VAT
PSI Members: £360 +VAT

Early Bird Non-PSI Members: £430 +VAT
Non-PSI Members: £470 +VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2026.

Registration

Early Bird registration closes on Friday 5th September.
To register for this event, please click here.

Agenda

Day 1: ML Foundation

  • Definitions and terminology
  • Key aspects of ML workflow
    • Planning
    • Pre-processing
    • Data splitting
    • Modelling, cross validation, hyperparameter estimation
    • Evaluation
    • Reporting
  • “ML in a day”. Overview of simple ML workflow using R packages dplyr, caret, ggplot2 using logistic regression. This will be used as a starting point to exemplify key aspects of ML, and as a point of comparison for further methods such as Elastic Net (regularised regression), gradient boosting and neural networks.
  • Common pitfalls and key considerations for using ML models in practice, and reviewing ML analyses, with a focus on the analysis of clinical trial data

Day 2: Supervised learning

  • What is supervised learning?
  • Definitions and examples of scenarios
  • Supervised learning workflow (short recap)
  • Supervised Algorithms:
    • LASSO and Elastic Net
    • Decision trees and Random Forest
    • Support Vector Machines
  • Evaluation and interpretation of these algorithms
  • Practical session in R: Application of above methods in caret

Day 3: Unsupervised learning

  • Definitions and comparison to supervised learning
  • K-means, PCA, Non-linear approaches such as tSNE
  • Worked examples in R

Day 4: Neural Networks and Deep Learning

  • DEMO: ML Using Caret in R
    • Preparing data: data splitting and preprocessing
    • Building and comparing different ML algorithms
  • Introduction to neural networks and basic architectures
  • Deep learning, Large Language models, Computer Vision
  • Discussion of Machine Learning vs Statistics: what are the differences, how much to they overlap, when to use what 

Speaker details

Speaker

Biography

Abstract

20240513-Jolyon Faria-5043Jolyon Faria
Data Science Director, AstraZeneca

Jolyon Faria is in the Clinical Data Sciences Lung Team within Oncology Data Science & AI, Oncology R&D, in AstraZeneca. His role is to lead and perform retrospective analyses of AZ clinical trials, Real World Data and multimodal data, with a focus on Statistics and Machine Learning, to inform the AZ Strategy for instance for PhIII Investment Decisions. He has a background in Biology: PhD (Univ. Leeds); Postdoc (Princeton Univ.), and Applied Statistics: (MSc Univ. Oxford) and is a Chartered Statistician (CSTAT; Royal Statistical Society, UK).
Moira_Verbelen
Moira Verbelen
Computational Methodology Lead, UCB


Moira Verbelen is Computational Methodology Lead in the Advanced Methods and Data Science team at UCB and is passionate about adopting and implementing cutting-edge AI methods in pharmaceutical research, in particular delivering AI solutions throughout all phases of clinical development. With a background bridging Pharmaceutical Sciences and Biostatistics, Moira holds a PhD from King’s College London, specializing in Machine Learning applied to Pharmacogenetics

This session introduces supervised learning in the context of pharmaceutical data analysis, focusing on its definition and practical applications. We'll briefly discuss the supervised learning workflow before diving into key algorithms including LASSO, Elastic Net, Decision Trees, Random Forests, and Support Vector Machines. Emphasis will be placed on how to evaluate and interpret these models effectively. Participants will gain hands-on experience using the caret package in R.
leoedit

Leo Souliotis
Associate Director, RWE, AstraZeneca

Leonidas Souliotis is part of the CDS Hematology team within the Oncology Data Science at AstraZeneca. His role includes developing statistical and ML methods and tools for evidence synthesis in the Heme space. He is also leading an internal course on how to use Python for Data Analysis. He is a Data Camp instructor, after developing the “Efficient Python coding in pandas". He has a background in Statistics: MSc (Imperial College London), PhD (University of Warwick).

  

 
danedit

Dan de Vassimon Manela
UCB

Dan de Vassimon Manela is a Machine Learning R&D and Innovation Lead at UCB and is interested in using Probabilistic Machine Learning methods to solve clinical problems across the pharmaceutical development chain. He has a background in Physics (BA, MSc Cambridge) and Statistical Machine Learning (MSci UCL).

  

 

Journal Club

PSI Training Course: Introduction to Machine Learning

Dates: 
Session 1 - Monday 13th October 2025
Session 2 - Tuesday 14th October 2025
Session 3 - Wednesday 15th October 2025
Session 4 - Thursday 16th October 2025
Time: 09:00 - 12:30 BST
Location: Online via Zoom

Who is this event intended for? 
This course is aimed at statisticians who are new to or with limited experience of machine learning. 

What is the benefit of attending? 

Attendees will learn about a range of topics in machine learning, including practical sessions in R.

Overview

Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).

Cost

Early Bird PSI Members: £320 +VAT
PSI Members: £360 +VAT

Early Bird Non-PSI Members: £430 +VAT
Non-PSI Members: £470 +VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2026.

Registration

Early Bird registration closes on Friday 5th September.
To register for this event, please click here.

Agenda

Day 1: ML Foundation

  • Definitions and terminology
  • Key aspects of ML workflow
    • Planning
    • Pre-processing
    • Data splitting
    • Modelling, cross validation, hyperparameter estimation
    • Evaluation
    • Reporting
  • “ML in a day”. Overview of simple ML workflow using R packages dplyr, caret, ggplot2 using logistic regression. This will be used as a starting point to exemplify key aspects of ML, and as a point of comparison for further methods such as Elastic Net (regularised regression), gradient boosting and neural networks.
  • Common pitfalls and key considerations for using ML models in practice, and reviewing ML analyses, with a focus on the analysis of clinical trial data

Day 2: Supervised learning

  • What is supervised learning?
  • Definitions and examples of scenarios
  • Supervised learning workflow (short recap)
  • Supervised Algorithms:
    • LASSO and Elastic Net
    • Decision trees and Random Forest
    • Support Vector Machines
  • Evaluation and interpretation of these algorithms
  • Practical session in R: Application of above methods in caret

Day 3: Unsupervised learning

  • Definitions and comparison to supervised learning
  • K-means, PCA, Non-linear approaches such as tSNE
  • Worked examples in R

Day 4: Neural Networks and Deep Learning

  • DEMO: ML Using Caret in R
    • Preparing data: data splitting and preprocessing
    • Building and comparing different ML algorithms
  • Introduction to neural networks and basic architectures
  • Deep learning, Large Language models, Computer Vision
  • Discussion of Machine Learning vs Statistics: what are the differences, how much to they overlap, when to use what 

Speaker details

Speaker

Biography

Abstract

20240513-Jolyon Faria-5043Jolyon Faria
Data Science Director, AstraZeneca

Jolyon Faria is in the Clinical Data Sciences Lung Team within Oncology Data Science & AI, Oncology R&D, in AstraZeneca. His role is to lead and perform retrospective analyses of AZ clinical trials, Real World Data and multimodal data, with a focus on Statistics and Machine Learning, to inform the AZ Strategy for instance for PhIII Investment Decisions. He has a background in Biology: PhD (Univ. Leeds); Postdoc (Princeton Univ.), and Applied Statistics: (MSc Univ. Oxford) and is a Chartered Statistician (CSTAT; Royal Statistical Society, UK).
Moira_Verbelen
Moira Verbelen
Computational Methodology Lead, UCB


Moira Verbelen is Computational Methodology Lead in the Advanced Methods and Data Science team at UCB and is passionate about adopting and implementing cutting-edge AI methods in pharmaceutical research, in particular delivering AI solutions throughout all phases of clinical development. With a background bridging Pharmaceutical Sciences and Biostatistics, Moira holds a PhD from King’s College London, specializing in Machine Learning applied to Pharmacogenetics

This session introduces supervised learning in the context of pharmaceutical data analysis, focusing on its definition and practical applications. We'll briefly discuss the supervised learning workflow before diving into key algorithms including LASSO, Elastic Net, Decision Trees, Random Forests, and Support Vector Machines. Emphasis will be placed on how to evaluate and interpret these models effectively. Participants will gain hands-on experience using the caret package in R.
leoedit

Leo Souliotis
Associate Director, RWE, AstraZeneca

Leonidas Souliotis is part of the CDS Hematology team within the Oncology Data Science at AstraZeneca. His role includes developing statistical and ML methods and tools for evidence synthesis in the Heme space. He is also leading an internal course on how to use Python for Data Analysis. He is a Data Camp instructor, after developing the “Efficient Python coding in pandas". He has a background in Statistics: MSc (Imperial College London), PhD (University of Warwick).

  

 
danedit

Dan de Vassimon Manela
UCB

Dan de Vassimon Manela is a Machine Learning R&D and Innovation Lead at UCB and is interested in using Probabilistic Machine Learning methods to solve clinical problems across the pharmaceutical development chain. He has a background in Physics (BA, MSc Cambridge) and Statistical Machine Learning (MSci UCL).

  

 

Webinars

PSI Training Course: Introduction to Machine Learning

Dates: 
Session 1 - Monday 13th October 2025
Session 2 - Tuesday 14th October 2025
Session 3 - Wednesday 15th October 2025
Session 4 - Thursday 16th October 2025
Time: 09:00 - 12:30 BST
Location: Online via Zoom

Who is this event intended for? 
This course is aimed at statisticians who are new to or with limited experience of machine learning. 

What is the benefit of attending? 

Attendees will learn about a range of topics in machine learning, including practical sessions in R.

Overview

Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).

Cost

Early Bird PSI Members: £320 +VAT
PSI Members: £360 +VAT

Early Bird Non-PSI Members: £430 +VAT
Non-PSI Members: £470 +VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2026.

Registration

Early Bird registration closes on Friday 5th September.
To register for this event, please click here.

Agenda

Day 1: ML Foundation

  • Definitions and terminology
  • Key aspects of ML workflow
    • Planning
    • Pre-processing
    • Data splitting
    • Modelling, cross validation, hyperparameter estimation
    • Evaluation
    • Reporting
  • “ML in a day”. Overview of simple ML workflow using R packages dplyr, caret, ggplot2 using logistic regression. This will be used as a starting point to exemplify key aspects of ML, and as a point of comparison for further methods such as Elastic Net (regularised regression), gradient boosting and neural networks.
  • Common pitfalls and key considerations for using ML models in practice, and reviewing ML analyses, with a focus on the analysis of clinical trial data

Day 2: Supervised learning

  • What is supervised learning?
  • Definitions and examples of scenarios
  • Supervised learning workflow (short recap)
  • Supervised Algorithms:
    • LASSO and Elastic Net
    • Decision trees and Random Forest
    • Support Vector Machines
  • Evaluation and interpretation of these algorithms
  • Practical session in R: Application of above methods in caret

Day 3: Unsupervised learning

  • Definitions and comparison to supervised learning
  • K-means, PCA, Non-linear approaches such as tSNE
  • Worked examples in R

Day 4: Neural Networks and Deep Learning

  • DEMO: ML Using Caret in R
    • Preparing data: data splitting and preprocessing
    • Building and comparing different ML algorithms
  • Introduction to neural networks and basic architectures
  • Deep learning, Large Language models, Computer Vision
  • Discussion of Machine Learning vs Statistics: what are the differences, how much to they overlap, when to use what 

Speaker details

Speaker

Biography

Abstract

20240513-Jolyon Faria-5043Jolyon Faria
Data Science Director, AstraZeneca

Jolyon Faria is in the Clinical Data Sciences Lung Team within Oncology Data Science & AI, Oncology R&D, in AstraZeneca. His role is to lead and perform retrospective analyses of AZ clinical trials, Real World Data and multimodal data, with a focus on Statistics and Machine Learning, to inform the AZ Strategy for instance for PhIII Investment Decisions. He has a background in Biology: PhD (Univ. Leeds); Postdoc (Princeton Univ.), and Applied Statistics: (MSc Univ. Oxford) and is a Chartered Statistician (CSTAT; Royal Statistical Society, UK).
Moira_Verbelen
Moira Verbelen
Computational Methodology Lead, UCB


Moira Verbelen is Computational Methodology Lead in the Advanced Methods and Data Science team at UCB and is passionate about adopting and implementing cutting-edge AI methods in pharmaceutical research, in particular delivering AI solutions throughout all phases of clinical development. With a background bridging Pharmaceutical Sciences and Biostatistics, Moira holds a PhD from King’s College London, specializing in Machine Learning applied to Pharmacogenetics

This session introduces supervised learning in the context of pharmaceutical data analysis, focusing on its definition and practical applications. We'll briefly discuss the supervised learning workflow before diving into key algorithms including LASSO, Elastic Net, Decision Trees, Random Forests, and Support Vector Machines. Emphasis will be placed on how to evaluate and interpret these models effectively. Participants will gain hands-on experience using the caret package in R.
leoedit

Leo Souliotis
Associate Director, RWE, AstraZeneca

Leonidas Souliotis is part of the CDS Hematology team within the Oncology Data Science at AstraZeneca. His role includes developing statistical and ML methods and tools for evidence synthesis in the Heme space. He is also leading an internal course on how to use Python for Data Analysis. He is a Data Camp instructor, after developing the “Efficient Python coding in pandas". He has a background in Statistics: MSc (Imperial College London), PhD (University of Warwick).

  

 
danedit

Dan de Vassimon Manela
UCB

Dan de Vassimon Manela is a Machine Learning R&D and Innovation Lead at UCB and is interested in using Probabilistic Machine Learning methods to solve clinical problems across the pharmaceutical development chain. He has a background in Physics (BA, MSc Cambridge) and Statistical Machine Learning (MSci UCL).

  

 

Careers Meetings

PSI Training Course: Introduction to Machine Learning

Dates: 
Session 1 - Monday 13th October 2025
Session 2 - Tuesday 14th October 2025
Session 3 - Wednesday 15th October 2025
Session 4 - Thursday 16th October 2025
Time: 09:00 - 12:30 BST
Location: Online via Zoom

Who is this event intended for? 
This course is aimed at statisticians who are new to or with limited experience of machine learning. 

What is the benefit of attending? 

Attendees will learn about a range of topics in machine learning, including practical sessions in R.

Overview

Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).

Cost

Early Bird PSI Members: £320 +VAT
PSI Members: £360 +VAT

Early Bird Non-PSI Members: £430 +VAT
Non-PSI Members: £470 +VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2026.

Registration

Early Bird registration closes on Friday 5th September.
To register for this event, please click here.

Agenda

Day 1: ML Foundation

  • Definitions and terminology
  • Key aspects of ML workflow
    • Planning
    • Pre-processing
    • Data splitting
    • Modelling, cross validation, hyperparameter estimation
    • Evaluation
    • Reporting
  • “ML in a day”. Overview of simple ML workflow using R packages dplyr, caret, ggplot2 using logistic regression. This will be used as a starting point to exemplify key aspects of ML, and as a point of comparison for further methods such as Elastic Net (regularised regression), gradient boosting and neural networks.
  • Common pitfalls and key considerations for using ML models in practice, and reviewing ML analyses, with a focus on the analysis of clinical trial data

Day 2: Supervised learning

  • What is supervised learning?
  • Definitions and examples of scenarios
  • Supervised learning workflow (short recap)
  • Supervised Algorithms:
    • LASSO and Elastic Net
    • Decision trees and Random Forest
    • Support Vector Machines
  • Evaluation and interpretation of these algorithms
  • Practical session in R: Application of above methods in caret

Day 3: Unsupervised learning

  • Definitions and comparison to supervised learning
  • K-means, PCA, Non-linear approaches such as tSNE
  • Worked examples in R

Day 4: Neural Networks and Deep Learning

  • DEMO: ML Using Caret in R
    • Preparing data: data splitting and preprocessing
    • Building and comparing different ML algorithms
  • Introduction to neural networks and basic architectures
  • Deep learning, Large Language models, Computer Vision
  • Discussion of Machine Learning vs Statistics: what are the differences, how much to they overlap, when to use what 

Speaker details

Speaker

Biography

Abstract

20240513-Jolyon Faria-5043Jolyon Faria
Data Science Director, AstraZeneca

Jolyon Faria is in the Clinical Data Sciences Lung Team within Oncology Data Science & AI, Oncology R&D, in AstraZeneca. His role is to lead and perform retrospective analyses of AZ clinical trials, Real World Data and multimodal data, with a focus on Statistics and Machine Learning, to inform the AZ Strategy for instance for PhIII Investment Decisions. He has a background in Biology: PhD (Univ. Leeds); Postdoc (Princeton Univ.), and Applied Statistics: (MSc Univ. Oxford) and is a Chartered Statistician (CSTAT; Royal Statistical Society, UK).
Moira_Verbelen
Moira Verbelen
Computational Methodology Lead, UCB


Moira Verbelen is Computational Methodology Lead in the Advanced Methods and Data Science team at UCB and is passionate about adopting and implementing cutting-edge AI methods in pharmaceutical research, in particular delivering AI solutions throughout all phases of clinical development. With a background bridging Pharmaceutical Sciences and Biostatistics, Moira holds a PhD from King’s College London, specializing in Machine Learning applied to Pharmacogenetics

This session introduces supervised learning in the context of pharmaceutical data analysis, focusing on its definition and practical applications. We'll briefly discuss the supervised learning workflow before diving into key algorithms including LASSO, Elastic Net, Decision Trees, Random Forests, and Support Vector Machines. Emphasis will be placed on how to evaluate and interpret these models effectively. Participants will gain hands-on experience using the caret package in R.
leoedit

Leo Souliotis
Associate Director, RWE, AstraZeneca

Leonidas Souliotis is part of the CDS Hematology team within the Oncology Data Science at AstraZeneca. His role includes developing statistical and ML methods and tools for evidence synthesis in the Heme space. He is also leading an internal course on how to use Python for Data Analysis. He is a Data Camp instructor, after developing the “Efficient Python coding in pandas". He has a background in Statistics: MSc (Imperial College London), PhD (University of Warwick).

  

 
danedit

Dan de Vassimon Manela
UCB

Dan de Vassimon Manela is a Machine Learning R&D and Innovation Lead at UCB and is interested in using Probabilistic Machine Learning methods to solve clinical problems across the pharmaceutical development chain. He has a background in Physics (BA, MSc Cambridge) and Statistical Machine Learning (MSci UCL).

  

 

Upcoming Events

Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars

Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.

AIMS SIG - Open-Source Lunch Bites

Topic: R Package Basics. Our monthly webinar series allows attendees to gain practical knowledge and skills in open-source coding and tools, with a focus on applications in the pharmaceutical industry. This month’s session, “R Package Basics,” will introduce the fundamentals of working with R packages—covering how to install, load, and manage them effectively to support data analysis and reproducible research. The session will provide a solid starting point, clarify common misconceptions, and offer valuable resources for continued learning.

PSI Mentoring 2026

Date: Ongoing 6 month cycle beginning late April/early May 2026 Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.

PSI Book Club Lunch and Learn: Communicating with Clarity and Confidence

If you have read Ros Atkins’ book The Art of Explanation or want to listen to the BBC’s ‘Communicator in Chief’, you are invited to join the PSI Book Club Lunch and Learn, to discuss the content and application with the author, Ros Atkins. Having written the book within the context of the news industry, Ros is keen to hear how we have applied the ideas as statisticians within drug development and clinical trials. There will be dedicated time during the webinar to ASK THE AUTHOR any questions – don’t miss out on this exclusive PSI Book Club event! Haven’t read the book yet? Pick up a copy today and join us. Explanation - identifying and communicating what we want to say - is described as an art, in the title of his book. However, the creativity comes from Ros’ discernment in identifying and describing a clear step-by-step process to follow and practice. Readers can learn Ros’ rules, developed and polished throughout his career as a journalist, to help communicate complex written or spoken information clearly.

PSI Training Course: Effective Leadership – the keys to growing your leadership capabilities

This course will consist of three online half-day workshops. The first will be aimed at building trust, the backbone of leadership and a key to becoming effective. This is key to building a solid foundation. The second will be on improving communication as a technical leader. This workshop will focus on communication strategies for different stakeholders and will involve tips on effective communication and how to develop the skills of active listening, coaching and what improv can teach us about good communication. The final workshop will bring these two components together to help leaders become more influential. This will also focus on how to use Steven Covey’s 7-Habits, in particular Habits 4, 5 and 6, which are called the habits of communication. The workshops will be interactive, allowing you to practice the concepts discussed. There will be plenty of time for questions and discussion. There will also be reflective time where you can think about what you are learning and how you might experiment with it.