Date: Monday 22nd - Thursday 25th April 2024 Time: 09:00-12:30 BST (on all 4 days) Location: Online via Zoom Presenters: Moira Verbelen (UCB), Jolyon Faria (AstraZeneca), Leo Souliotis (AstraZeneca) and Dan de Vassimon Manela (UCB).
Who is this event intended for? This course is aimed at clinical trial 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.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 22nd March 2024.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
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).
Agenda
The examples covered in this Course will be in relation to Clinical Trials. For an overview of what will be covered across the four sessions, please see below. (Please be advised: this agenda may be subject to minor revisions)
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”. “Lightening” 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 scenario’s
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 (including clustering)
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 in SAS
- How to use R ML from SAS Viya
- How to use no-code/low-code ML
- How to use SAS ML procs in R Studio
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
Moira Verbelen
Moira Verbelen is Head of Machine Learning, AI and Data at UCB, and is passionate about adopting and implementing innovative 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.
Jolyon Faria
Jolyon Faria works within the LASER Team within Oncology Data Science at AstraZeneca. He is aligned to the Lung cancer portfolio and his role is to perform retrospective analyses of AZ clinical trials and relevant 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).
Leo Souliotis
Leo Souliotis is part of the LASER team within the Oncology Data Science at AstraZeneca.
His role includes performing statistical and ML analyses in Lung studies, focusing on multi-study analysis in the Lung 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).
Dan de Vassimon Manela
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
Date: Monday 22nd - Thursday 25th April 2024 Time: 09:00-12:30 BST (on all 4 days) Location: Online via Zoom Presenters: Moira Verbelen (UCB), Jolyon Faria (AstraZeneca), Leo Souliotis (AstraZeneca) and Dan de Vassimon Manela (UCB).
Who is this event intended for? This course is aimed at clinical trial 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.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 22nd March 2024.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
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).
Agenda
The examples covered in this Course will be in relation to Clinical Trials. For an overview of what will be covered across the four sessions, please see below. (Please be advised: this agenda may be subject to minor revisions)
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”. “Lightening” 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 scenario’s
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 (including clustering)
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 in SAS
- How to use R ML from SAS Viya
- How to use no-code/low-code ML
- How to use SAS ML procs in R Studio
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
Moira Verbelen
Moira Verbelen is Head of Machine Learning, AI and Data at UCB, and is passionate about adopting and implementing innovative 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.
Jolyon Faria
Jolyon Faria works within the LASER Team within Oncology Data Science at AstraZeneca. He is aligned to the Lung cancer portfolio and his role is to perform retrospective analyses of AZ clinical trials and relevant 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).
Leo Souliotis
Leo Souliotis is part of the LASER team within the Oncology Data Science at AstraZeneca.
His role includes performing statistical and ML analyses in Lung studies, focusing on multi-study analysis in the Lung 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).
Dan de Vassimon Manela
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
Date: Monday 22nd - Thursday 25th April 2024 Time: 09:00-12:30 BST (on all 4 days) Location: Online via Zoom Presenters: Moira Verbelen (UCB), Jolyon Faria (AstraZeneca), Leo Souliotis (AstraZeneca) and Dan de Vassimon Manela (UCB).
Who is this event intended for? This course is aimed at clinical trial 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.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 22nd March 2024.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
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).
Agenda
The examples covered in this Course will be in relation to Clinical Trials. For an overview of what will be covered across the four sessions, please see below. (Please be advised: this agenda may be subject to minor revisions)
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”. “Lightening” 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 scenario’s
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 (including clustering)
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 in SAS
- How to use R ML from SAS Viya
- How to use no-code/low-code ML
- How to use SAS ML procs in R Studio
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
Moira Verbelen
Moira Verbelen is Head of Machine Learning, AI and Data at UCB, and is passionate about adopting and implementing innovative 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.
Jolyon Faria
Jolyon Faria works within the LASER Team within Oncology Data Science at AstraZeneca. He is aligned to the Lung cancer portfolio and his role is to perform retrospective analyses of AZ clinical trials and relevant 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).
Leo Souliotis
Leo Souliotis is part of the LASER team within the Oncology Data Science at AstraZeneca.
His role includes performing statistical and ML analyses in Lung studies, focusing on multi-study analysis in the Lung 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).
Dan de Vassimon Manela
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
Date: Monday 22nd - Thursday 25th April 2024 Time: 09:00-12:30 BST (on all 4 days) Location: Online via Zoom Presenters: Moira Verbelen (UCB), Jolyon Faria (AstraZeneca), Leo Souliotis (AstraZeneca) and Dan de Vassimon Manela (UCB).
Who is this event intended for? This course is aimed at clinical trial 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.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 22nd March 2024.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
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).
Agenda
The examples covered in this Course will be in relation to Clinical Trials. For an overview of what will be covered across the four sessions, please see below. (Please be advised: this agenda may be subject to minor revisions)
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”. “Lightening” 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 scenario’s
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 (including clustering)
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 in SAS
- How to use R ML from SAS Viya
- How to use no-code/low-code ML
- How to use SAS ML procs in R Studio
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
Moira Verbelen
Moira Verbelen is Head of Machine Learning, AI and Data at UCB, and is passionate about adopting and implementing innovative 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.
Jolyon Faria
Jolyon Faria works within the LASER Team within Oncology Data Science at AstraZeneca. He is aligned to the Lung cancer portfolio and his role is to perform retrospective analyses of AZ clinical trials and relevant 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).
Leo Souliotis
Leo Souliotis is part of the LASER team within the Oncology Data Science at AstraZeneca.
His role includes performing statistical and ML analyses in Lung studies, focusing on multi-study analysis in the Lung 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).
Dan de Vassimon Manela
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
Date: Monday 22nd - Thursday 25th April 2024 Time: 09:00-12:30 BST (on all 4 days) Location: Online via Zoom Presenters: Moira Verbelen (UCB), Jolyon Faria (AstraZeneca), Leo Souliotis (AstraZeneca) and Dan de Vassimon Manela (UCB).
Who is this event intended for? This course is aimed at clinical trial 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.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 22nd March 2024.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
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).
Agenda
The examples covered in this Course will be in relation to Clinical Trials. For an overview of what will be covered across the four sessions, please see below. (Please be advised: this agenda may be subject to minor revisions)
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”. “Lightening” 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 scenario’s
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 (including clustering)
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 in SAS
- How to use R ML from SAS Viya
- How to use no-code/low-code ML
- How to use SAS ML procs in R Studio
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
Moira Verbelen
Moira Verbelen is Head of Machine Learning, AI and Data at UCB, and is passionate about adopting and implementing innovative 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.
Jolyon Faria
Jolyon Faria works within the LASER Team within Oncology Data Science at AstraZeneca. He is aligned to the Lung cancer portfolio and his role is to perform retrospective analyses of AZ clinical trials and relevant 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).
Leo Souliotis
Leo Souliotis is part of the LASER team within the Oncology Data Science at AstraZeneca.
His role includes performing statistical and ML analyses in Lung studies, focusing on multi-study analysis in the Lung 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).
Dan de Vassimon Manela
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
Date: Monday 22nd - Thursday 25th April 2024 Time: 09:00-12:30 BST (on all 4 days) Location: Online via Zoom Presenters: Moira Verbelen (UCB), Jolyon Faria (AstraZeneca), Leo Souliotis (AstraZeneca) and Dan de Vassimon Manela (UCB).
Who is this event intended for? This course is aimed at clinical trial 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.
Cost
Early Bird PSI Members = £320+VAT Early Bird Non-Members = £430*+VAT *Please note: Early Bird prices expire at 17:00 on Friday 22nd March 2024.
Standard PSI Members = £360+VAT Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
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).
Agenda
The examples covered in this Course will be in relation to Clinical Trials. For an overview of what will be covered across the four sessions, please see below. (Please be advised: this agenda may be subject to minor revisions)
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”. “Lightening” 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 scenario’s
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 (including clustering)
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 in SAS
- How to use R ML from SAS Viya
- How to use no-code/low-code ML
- How to use SAS ML procs in R Studio
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
Moira Verbelen
Moira Verbelen is Head of Machine Learning, AI and Data at UCB, and is passionate about adopting and implementing innovative 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.
Jolyon Faria
Jolyon Faria works within the LASER Team within Oncology Data Science at AstraZeneca. He is aligned to the Lung cancer portfolio and his role is to perform retrospective analyses of AZ clinical trials and relevant 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).
Leo Souliotis
Leo Souliotis is part of the LASER team within the Oncology Data Science at AstraZeneca.
His role includes performing statistical and ML analyses in Lung studies, focusing on multi-study analysis in the Lung 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).
Dan de Vassimon Manela
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.
The event will open with an overview on drug development in women’s health from a clinician perspective. This talk is followed by talks about statistical challenges when planning IVF studies and analysing the menstrual cycles.
This webinar will provide an overview of surrogacy for licensing and reimbursement. In turn, the need of extensions of the SPIRIT and CONSORT statement will be defined and outlined, with case studies to support.
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Virtual Control Groups in Toxicity Studies
Lea Vaas will present how replacement of concurrent control animals by Virtual Control Groups (VCGs) in systemic toxicity studies may help in contributing to the 3R's principle of animal experimentation: Reduce, Refine, Replace.
Joint PSI/EFSPI Data Science SIG Webinar: Developing Digital Measures (Digital Biomarkers) in Drug Development – insights from Mobilise D consortium
We will share a brief overview of what Mobilise D is and why it is an important step stone in the development of digital biomarkers, and how Mobilise D outputs can be relevant for you.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
PSI Webinar: Development of Gene Therapies: Strategic, Scientific, Regulatory and Access Considerations
This webinar will cover the history of cell/gene therapy, major regulatory advances, the role of quantitative scientists in drug development of these novel therapeutics, and discuss opportunities for innovation and product advancement.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
PSI Introduction to Industry Training (ITIT) Course - 2024/2025
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
Statisticians in the Pharmaceutical Industry Executive Office: c/o MCI UK Ltd | Unit 24/22 South | Building 4000 | Langstone Park| Langstone Road | Havant | PO9 1SA | UK