deploy: MLGlobalHealth/starter-hugo-research-group@1386cd0

sa_aimsximperial2025/index.html

@@ -7,7 +7,7 @@
 <a class=dropdown-item href=/short_courses/past><span>Past short courses</span></a></div></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li></ul></div></nav></header></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Sa_aimsximperial2025s</h1></div><div class=universal-wrapper><div class="media stream-item view-compact"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/sa_aimsximperial2025/application/></a></div><a href=/sa_aimsximperial2025/application/ class=summary-link><div class=article-style>Application Cost: R3,600 (ZAR)
 APPLY NOW (link coming soon&mldr;) 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?</div></a><div class="stream-meta article-metadata"><div class=article-metadata><span class=article-date>Jan 1, 0001</span></div></div></div><div class=ml-3></div></div><div class="media stream-item view-compact"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/sa_aimsximperial2025/lecturers/></a></div><a href=/sa_aimsximperial2025/lecturers/ class=summary-link><div class=article-style>Lecturers Juliette Unwin 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.</div></a><div class="stream-meta article-metadata"><div class=article-metadata><span class=article-date>Jan 1, 0001</span></div></div></div><div class=ml-3></div></div><div class="media stream-item view-compact"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/sa_aimsximperial2025/overview/></a></div><a href=/sa_aimsximperial2025/overview/ class=summary-link><div class=article-style>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 Location: AIMS Cape Town, South Africa Organised by: Department of Mathematics, Imperial College London and the Machine Learning and Global Health Network</div></a><div class="stream-meta article-metadata"><div class=article-metadata><span class=article-date>Jan 1, 0001</span></div></div></div><div class=ml-3></div></div><div class="media stream-item view-compact"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/sa_aimsximperial2025/programme/></a></div><a href=/sa_aimsximperial2025/programme/ class=summary-link><div class=article-style>Full programme 24 March, Day 1 Refresher
+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?</div></a><div class="stream-meta article-metadata"><div class=article-metadata><span class=article-date>Jan 1, 0001</span></div></div></div><div class=ml-3></div></div><div class="media stream-item view-compact"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/sa_aimsximperial2025/lecturers/></a></div><a href=/sa_aimsximperial2025/lecturers/ class=summary-link><div class=article-style>Lecturers Juliette Unwin 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.</div></a><div class="stream-meta article-metadata"><div class=article-metadata><span class=article-date>Jan 1, 0001</span></div></div></div><div class=ml-3></div></div><div class="media stream-item view-compact"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/sa_aimsximperial2025/overview/></a></div><a href=/sa_aimsximperial2025/overview/ class=summary-link><div class=article-style>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 Location: AIMS Cape Town, South Africa Organised by: Department of Mathematics, Imperial College London; the Machine Learning and Global Health Network; and the African Institute for Mathematical Sciences</div></a><div class="stream-meta article-metadata"><div class=article-metadata><span class=article-date>Jan 1, 0001</span></div></div></div><div class=ml-3></div></div><div class="media stream-item view-compact"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=/sa_aimsximperial2025/programme/></a></div><a href=/sa_aimsximperial2025/programme/ class=summary-link><div class=article-style>Full programme 24 March, Day 1 Refresher
 Welcome: Introductions (9.00 - 9.30)
 Recap python basics and Bayesian inference (09.30 – 10:30)
 Break (10.30-11.00)

sa_aimsximperial2025/overview/index.html

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 <a class=dropdown-item href=/sa_aimsximperial2025/application/><span>Application</span></a>
 <a class=dropdown-item href=/sa_aimsximperial2025/programme/><span>Programme</span></a>
 <a class=dropdown-item href=/sa_aimsximperial2025/lecturers/><span>Lecturers</span></a>
-<a class=dropdown-item href=/short_courses/past><span>Past short courses</span></a></div></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li></ul></div></nav></header></div><div class=page-body><div class="universal-wrapper pt-3"><h1></h1></div><div class=universal-wrapper><div class=article-style><style>h1{color:#0000cd}h3{color:#00bfff}h4{color:#00bfff}p{text-align:justify}a.button{padding:8px;border:1x outset buttonborder;border-radius:15px;color:#fff;background-color:#00bfff;text-decoration:none;font-size:25px}</style><center><img src=../resources/imperial.png width=250 hspace=50 style=display:inline-block;margin:10px>
+<a class=dropdown-item href=/short_courses/past><span>Past short courses</span></a></div></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li></ul></div></nav></header></div><div class=page-body><div class="universal-wrapper pt-3"><h1></h1></div><div class=universal-wrapper><div class=article-style><style>h1{color:#000}h3{color:#00bfff}h4{color:#00bfff}p{text-align:justify}a.button{padding:8px;border:1x outset buttonborder;border-radius:15px;color:#fff;background-color:#00bfff;text-decoration:none;font-size:25px}.background-img{position:absolute;z-index:-1;top:690 px}.text{position:relative;z-index:1}.space{margin-top:630px}.shift-up-img{margin-top:-240px}.shift-up-title{margin-top:-35px}</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><h1 id=ai-and-probabilistic-programming-for-global-health-in-africa>AI and Probabilistic Programming for Global Health in Africa</h1><h3 id=a-hands-on-course-for-students-and-researchers-at-the-intersection-of-statistics-and-public-health>A hands-on course for students and researchers at the intersection of statistics and public health</h3><p><strong>24th - 28th March 2025</strong><br><strong>Location</strong>: AIMS Cape Town, South Africa<br><strong>Organised by:</strong> Department of Mathematics, Imperial College London and the Machine Learning and Global Health Network</p><h4 id=overview>Overview</h4><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.<p>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></p><h4 id=content-coveredwhat-attendees-will-learn>Content covered/What attendees will learn</h4><ul><li>Bayesian workflow with probabilistic programming (Stan)</li><li>Core regression models for hierarchical data</li><li>Gaussian process regression with Stan</li><li>State-of-the-art GP approximations for scalable inference</li><li>Infectious disease modelling with probabilistic programming</li><li>Pathogen phylogenetics with Stan</li></ul><p><em><strong>Practical real-world examples</strong></em> with applications in malaria modelling, HIV epidemiology, ecology, environmental health<br><em><strong>Varied datasets</strong></em> including Spatial data, genomic data, epidemiological data<br><em><strong>Stan templates</strong></em> and <em><strong>Python code</strong></em> for implementing the methods covered</p><h4 id=learning-stylescourse-structure>Learning styles/course structure</h4><ul><li>Lectures</li><li>Individuals labs</li><li>Group project</li><li>Presenting findings</li></ul><h4 id=who-should-attend-and-pre-requisites>Who should attend and pre-requisites</h4><ul><li>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.</li><li>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.</li><li>Attendees should be familiar with git for reproducible analyses and collaborative coding.</li></ul><center><a href=https://mlgh.net/sa_aimsximperial2025/application/ class=button>Apply<a></center><img src=../resources/cape_town.jpg width=1080></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class="powered-by copyright-license-text">© 2024 MLGlobalHealth. This work is licensed under <a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank>CC BY NC ND 4.0</a></p><p class="powered-by footer-license-icons"><a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank aria-label="Creative Commons"><i class="fab fa-creative-commons fa-2x" aria-hidden=true></i>
+<img src=../resources/ammi.png width=200 style=display:inline-block></center><br><br><div class=shift-up-title></div><h1 id=ai-and-probabilistic-programming-for-global-health-in-africa>AI and Probabilistic Programming for Global Health in Africa</h1><div class=shift-up-img></div><img src=../resources/cape_town.jpg width=960 class=background-img><div class=space></div><h3 id=a-hands-on-course-for-students-and-researchers-at-the-intersection-of-statistics-and-public-health>A hands-on course for students and researchers at the intersection of statistics and public health</h3><p><strong>24th - 28th March 2025</strong><br><strong>Location</strong>: AIMS Cape Town, South Africa<br><strong>Organised by:</strong> Department of Mathematics, Imperial College London; the Machine Learning and Global Health Network; and the African Institute for Mathematical Sciences</p><h4 id=overview>Overview</h4><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.<p>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></p><h4 id=content-coveredwhat-attendees-will-learn>Content covered/What attendees will learn</h4><ul><li>Bayesian workflow with probabilistic programming (Stan)</li><li>Core regression models for hierarchical data</li><li>Gaussian process regression with Stan</li><li>State-of-the-art GP approximations for scalable inference</li><li>Infectious disease modelling with probabilistic programming</li><li>Pathogen phylogenetics with Stan</li></ul><p><em><strong>Practical real-world examples</strong></em> with applications in malaria modelling, HIV epidemiology, ecology, environmental health<br><em><strong>Varied datasets</strong></em> including Spatial data, genomic data, epidemiological data<br><em><strong>Stan templates</strong></em> and <em><strong>Python code</strong></em> for implementing the methods covered</p><h4 id=learning-stylescourse-structure>Learning styles/course structure</h4><ul><li>Lectures</li><li>Individuals labs</li><li>Group project</li><li>Presenting findings</li></ul><h4 id=who-should-attend-and-pre-requisites>Who should attend and pre-requisites</h4><ul><li>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.</li><li>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.</li><li>Attendees should be familiar with git for reproducible analyses and collaborative coding.</li></ul><center><a href=https://mlgh.net/sa_aimsximperial2025/application/ class=button>Apply<a></center></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class="powered-by copyright-license-text">© 2024 MLGlobalHealth. This work is licensed under <a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank>CC BY NC ND 4.0</a></p><p class="powered-by footer-license-icons"><a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank aria-label="Creative Commons"><i class="fab fa-creative-commons fa-2x" aria-hidden=true></i>
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