PSI Careers - MEDMathS: Medicine Empowered by Data, Maths and Statistics
Date: Wednesday 6th November 2024 A careers talk about medical statistics and how it plays a crucial role in developing new medicines
Dates: 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.
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.
To register your place, please click here.
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
Day 2: Supervised learning
Day 3: Unsupervised learning (including clustering)
Day 4: Neural Networks and Deep Learning
Speaker |
Biography |
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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. |
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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). |
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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). |