Adds AIMSxImperial SA pages & updates workshops

config/_default/menus.yaml

@@ -22,6 +22,39 @@ main:
   - name: Contact
     url: contact
     weight: 60
-  - name: Workshops
-    url: workshops
-    weight: 60
+  - name: Short courses
+    identifier: short-courses
+    url: short_courses
+    weight: 70
+  - name: Past
+    parent: short-courses
+    url: short_courses/past
+    weight: 10
+  - name: Upcoming
+    parent: short-courses
+    url: short_courses/upcoming
+    weight: 20
+  - name: AIMSxImperial South Africa
+    identifier: sa_aimsximperial2025
+    url: sa_aimsximperial2025
+    weight: 80
+  - name: Overview
+    parent: sa_aimsximperial2025
+    url: sa_aimsximperial2025/overview/
+    weight: 10
+  - name: Application
+    parent: sa_aimsximperial2025
+    url: sa_aimsximperial2025/application/
+    weight: 20
+  - name: Programme
+    parent: sa_aimsximperial2025
+    url: sa_aimsximperial2025/programme/
+    weight: 30
+  - name: Lecturers
+    parent: sa_aimsximperial2025
+    url: sa_aimsximperial2025/lecturers/
+    weight: 40
+  - name: Past short courses
+    parent: sa_aimsximperial2025
+    url: /short_courses/past
+    weight: 50

content/sa_aimsximperial2025/application/_index.md

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+---
+---
+
+<style>
+  h1 {
+      color: rgb(0, 0, 205)
+  }
+  h4 {
+      color: rgb(0, 191, 255)
+  }
+</style>
+
+<center>
+<img src="../resources/imperial.png" width="250" hspace="50" style="display:inline-block;margin:10px;"/>
+<img src="../resources/mlgh.png" width="200" hspace="50" style="display:inline-block;margin:50px;"/>
+<img src="../resources/ammi.png" width="200" style="display:inline-block;"/>
+</center>
+
+# Application
+**Cost: R3,600 (ZAR)**
+
+**APPLY NOW (link coming soon...)**
+<br/>
+**Deadline to apply:** 31th January 2025
+
+Form for external applicants to apply. Should include:
+  + Organisation (and whether student/staff)
+  + Upload CV (qualifications and to confirm coding skills)
+  + Link to Github profile
+  + Short description of previous projects
+  + Details of 1 referee
+  + Short bio/personal statement on why they want to attend/what they hope to gain from the course
+  + Short test? Covering python knowledge and basic Bayesian inference
+
+**Contact:** Dr Alexandra Blenkinsop (<a.blenkinsop@imperial.ac.uk>)
+
+

content/sa_aimsximperial2025/lecturers/_index.md

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+---
+---
+
+<style>
+  .profile-picture img {
+      float: left;
+      margin-right: 20px;
+      width: 200px;
+      height: 200px;
+      position: relative;
+      overflow: hidden;
+      border-radius: 50%;
+  }
+  p {
+      text-align: justify;
+  }
+  h1 {
+      color: rgb(0, 0, 205)
+  }
+</style>
+
+
+<center>
+<img src="../resources/imperial.png" width="250" hspace="50" style="display:inline-block;margin:10px;"/>
+<img src="../resources/mlgh.png" width="200" hspace="50" style="display:inline-block;margin:50px;"/>
+<img src="../resources/ammi.png" width="200" style="display:inline-block;"/>
+</center>
+
+# Lecturers
+#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Juliette Unwin](https://research-information.bris.ac.uk/en/persons/h-juliette-t-unwin)
+<div class="profile-picture">
+<img src="../resources/ettie_crop.png"/>
+<p class="text">Dr Juliette Unwin is a lecturer in statistical science at the University of Bristol.  She is interested in developing and applying novel methods for infectious disease outbreak analysis to help inform policy makers in real time. Her current research focuses on developing spatial temporal renewal-based transmission models alongside estimating the number of children affected by COVID-19 and crises. She has previously been involved in real-time analysis of Ebola in the Democratic Republic of Congo alongside the World Health Organisation and COVID-19 in New York State with the local government.
+</p>
+</div>
+<br/>
+<br/>
+
+#### [Alexandra Blenkinsop](https://www.imperial.ac.uk/people/a.blenkinsop)
+<div class="profile-picture">
+<img src="../resources/alex.png"/>
+<p class="text">
+Dr Alexandra Blenkinsop is a Research Associate in the Department of Mathematics at Imperial College London. Her research centres around developing and applying methods to understand HIV transmission dynamics at a population level to inform policy decisions. She has also worked on projects related to COVID-19, including estimation of children affected by death of parents and caregivers during the pandemic. She has collaborated with the HIV Transmission Elimination Amsterdam Initiative, The Botswana-Harvard AIDS Institute Partnership and the United States Centers for Disease Control.
+</p>
+</div>
+<br/>
+<br/>
+
+#### &nbsp;&nbsp;&nbsp;&nbsp;[Tristan Naidoo](https://profiles.imperial.ac.uk/t.naidoo21/about)
+<div class="profile-picture">
+<img src="../resources/trist.png"/>
+<p class="text">
+Tristan is a PhD student in the Department of Infectious Disease Epidemiology. His interests lie at the intersection of Natural Language Processing and Public Health. In line with these interests, his research focuses on using Large Language Models to investigate how Twitter data can be used to quantify adherence to protective behaviours during the COVID-19 pandemic.
+</p>
+</div>
+<br/>
+<br/>
+<br/>
+<br/>
+
+#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[Josh Corneck](https://profiles.imperial.ac.uk/josh.corneck-willcox20)
+<div class="profile-picture">
+<img src="../resources/josh.png"/>
+<p class="text">
+Josh is a third year PhD student in the Department of Mathematics at Imperial College London. His research is primarily centered around network point processes, and more generally in the application of networks to aid modelling performance. He is currently working on developing models that incorporate Bayesian nonparametric approaches to help capture latent group structure among the nodes on the network.
+</p>
+</div>
+<br/>
+<br/>
+<br/>
+
+#### [Michael Whitehouse](https://michael-whitehouse.github.io/)
+<div class="profile-picture">
+<img src="../resources/michael_mono.png"/>
+<p class="text">
+Dr Michael Whitehouse is a Research Associate in the School of Public Health at Imperial College London. His research is around statistical inference in infectious disease models for heterogeneous populations and the modelling and control of infectious disease outbreaks on contact network structures.
+</p>
+</div>

content/sa_aimsximperial2025/overview/_index.md

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+---
+---
+
+<style>
+  h1 {
+      color: rgb(0, 0, 205)
+  }
+  h3 {
+/*       color: rgb(255, 182, 193) */
+      color: rgb(0, 191, 255)
+  }
+  h4 {
+      color: rgb(0, 191, 255)
+  }
+  p {
+      text-align: justify;
+  }
+
+  a.button {
+    padding: 8px 8px;
+    border: 1x outset buttonborder;
+    border-radius: 15px;
+    color: white;
+    background-color: rgb(0, 191, 255);
+    text-decoration: none;
+    font-size:25px
+}
+
+</style>
+
+<center>
+<img src="../resources/imperial.png" width="250" hspace="50" style="display:inline-block;margin:10px;"/>
+<img src="../resources/mlgh.png" width="200" hspace="50" style="display:inline-block;margin:50px;"/>
+<img src="../resources/ammi.png" width="200" style="display:inline-block;"/>
+</center>
+
+
+# AI and Probabilistic Programming for Global Health in Africa
+
+
+### A hands-on course for students and researchers at the intersection of statistics and public health
+
+
+**24th - 28th March 2025**
+<br/>
+**Location**: AIMS Cape Town, South Africa
+<br/>
+**Organised by:** Department of Mathematics, Imperial College London and the Machine Learning and Global Health Network
+
+
+#### Overview
+<p class="text">
+One of the groundbreaking advances in machine learning research in the past decade is surrounding the emergence of increasingly sophisticated, robust, and easily usable probabilistic programming languages. These new tools, including Stan or numpyro, hide tedious calculations involving automatic differentiation and gradient-based optimization from the end-user, making modern statistical methods widely available to data scientists in Africa that wish to address some of the most urgent challenges on the continent, ranging from habitat degradation, air pollution, extreme weather events, disease outbreaks and population health in general.
+
+
+This one-week course will cover how you can integrate modern statistical techniques with the Stan probabilistic programming language to effectively address a broad range of applications from epidemiological, genomic and spatial data. We hope this course will equip you with intelligence-driven statistical technologies to drive your own evidence-based discoveries in global health or other applications, and more broadly increase your fluency in artificial intelligence and modern statistics.
+</p>
+
+
+#### Content covered/What attendees will learn
++ Bayesian workflow with probabilistic programming (Stan)
++ Core regression models for hierarchical data
++ Gaussian process regression with Stan
++ State-of-the-art GP approximations for scalable inference
++ Infectious disease modelling with probabilistic programming
++ Pathogen phylogenetics with Stan
+
+***Practical real-world examples*** with applications in malaria modelling, HIV epidemiology, ecology, environmental health
+<br/>
+***Varied datasets*** including Spatial data, genomic data, epidemiological data
+<br/>
+***Stan templates*** and ***Python code*** for implementing the methods covered
+
+#### Learning styles/course structure
+  + Lectures
+  + Individuals labs
+  + Group project
+  + Presenting findings
+
+#### Who should attend and pre-requisites
+  + Students and researchers interested in advanced statistical methods and probabilistic programming with applications in global health, including  analysis of clinical trials and studies, infectious disease epidemiology and modelling outbreaks, and handling large genomic datasets for the surveillance of pathogens.
+  + Attendees should have good knowledge of python and pandas to participate fully in the practical components. Previous experience with a probabilistic programming language (e.g. Stan, NumPyro, PyMc, Turing.jl) is advantageous but not essential.
+  + Attendees should be familiar with git for reproducible analyses and collaborative coding.
+
+<center>
+<a href="https://mlgh.net/sa_aimsximperial2025/application/" class="button">Apply<a/>
+</center>
+
+<img src="../resources/cape_town.jpg" width="1080"/>
+

content/sa_aimsximperial2025/programme/_index.md

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+---
+---
+
+<style>
+  h1 {
+      color: rgb(0, 0, 205)
+  }
+  h4 {
+      color: rgb(0, 191, 255)
+  }
+</style>
+
+<center>
+<img src="../resources/imperial.png" width="250" hspace="50" style="display:inline-block;margin:10px;"/>
+<img src="../resources/mlgh.png" width="200" hspace="50" style="display:inline-block;margin:50px;"/>
+<img src="../resources/ammi.png" width="200" style="display:inline-block;"/>
+</center>
+
+# Full programme
+
+
+#### 24 March, Day 1
+***Refresher***
+
+
+1. Welcome: Introductions (9.00 - 9.30)
+
+2. Recap python basics and Bayesian inference (09.30 – 10:30)
+
+3. Break (10.30-11.00)
+
+4. Lecture: Statistical learning (11.00-12.00)
+
+5. Lecture: Intro to Stan  (12.00 - 12.30)
+
+6. Lunch (12.30 - 13.30)
+
+7. Lecture: Intro to Stan (continued) (13.30 - 14.00)
+
+8. Hands-on: Introduction to statistical learning with Stan in python (14.00 - 16.00)
+    + linear models
+    + generalised linear models
+
+#### 25 March, Day 2
+***Scalable Gaussian process regression models in Stan***
+
+
+1. Recap session and Q&A in break-out rooms (max 10 ppl) (09.00 - 09.30)
+
+2. Lecture: Intro to Gaussian processes (09.30-10.30)
+    + Motivations/applications
+    + Covariance function
+    + Different kernels
+
+3. Break (10.30-11.00)
+
+4. Scalable Gaussian process regression models in Stan (11.00-11.30)
+    + Motivate approximations
+    + 1D
+    + 2D
+    + Introduce practical
+
+5. Lunch (12.30-13.30)
+
+6. Inspirational Lecture: Two research talks from the Machine Learning and Global Health Network (13.30 - 14.30)
+
+7. Hands-on: Scalable Gaussian process regression models (14.00-16.00)
+    + Implement two GP models, provide partial code to complete
+
+8. Social: BBQ or similar (18.00 - 20.00)
+
+#### 26 March, Day 3
+***Gaussian processes continued***
+
+
+1. Recap session and Q&A in break-out rooms (max 10 ppl) (09.00 - 09.30)
+
+2. Lecture/Hands-on: Scalable Gaussian process regression models (09:30 – 10:30)
+    + HSGPs
+    + Examples including real world example
+
+3. Break (10.30-11.00)
+
+4. Group project (11.00 - 12.30)
+    + Bring together everything from the first two days -> new datasets/problems, implementing basic regression models, hierarchical models and GP approximations
+    + Mix together AIMS and non-AIMS students
+
+5. Lunch (12:30-13:30)
+
+6. Group project continued (13.30 - 15.00)
+
+7. Groups present (15.00-16.00)
+
+#### 27 March, Day 4
+***Infectious Disease Modelling with Stan***
+
+
+1. Lecture: Introduction to Infectious Disease Modelling and Compartmental Modelling (09.00-10:20)
+
+2. Break (10.20-10.50)
+
+3. Practical: Deriving simple SIR type models with pen and paper (10:50 – 11:30)
+
+4. Practical: SIR models in Stan (11.30-12.30)
+
+5. Lunch (12:30-13:30)
+
+6. Inspirational Lecture: Local research talks (two-three) (13.30 - 14.30)
+
+7. Practical: SIR models in Stan (continued) (14.30-16.00)
+
+
+#### 28 March, Day 5
+***Phylogenetics***
+
+
+1. Lecture: Introduction to phylogenetics (09:00-10:30)
+    + Content developed already
+    + Basic coalescent theory (Carsten Wiuf’s book chapter 1)
+
+2. Break (10.30-11.00)
+
+3. Practical: Running a phylogenetic pipeline (11:00-12:30)
+    + Fix alignment
+    + Build tree with neighbour-joining algorithm and MLE
+    + Spot the recombinant
+    + Root tree
+    + Bootstrapping
+
+4. Lunch (12:30-13:30)
+
+5. Guided practical: More phylogenetics (13:30 – 14:30)
+    + Example using new Mpox data
+    + Tree dating -> date origin of MPox
+
+6. Wrap-up (14:30 – 15:00)
+

content/short_courses/_index.md

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+---
+title: Past short courses
+
+# Listing view
+view: card
+
+banner:
+  caption: ''
+  image: ''
+---