Merge pull request #20 from henrymoss/henry

added moss + colborators

content/authors/henrymoss/_index.md

@@ -0,0 +1,40 @@
+---
+title: "Henry Moss"
+author: ["Henry Moss"]
+lastmod: 2023-11-10T20:49:51+00:00
+draft: false
+weight: 3002
+active: true
+superuser: false
+role: "Early Career Advanced Fellow"
+organizations:
+  - name: "University of Cambridge"
+    url: "http://www.cst.cam.ac.uk"
+  - name: "Webpage"
+    url: "https://henrymoss.github.io/"
+# Interests to show in About widget
+interests:
+  - Scalable Bayesian machine learning models to help scientists better understand the world around us
+  - Active learning and Bayesian optimisation to accelerate the design of new technologies
+  - Molecular search and gene design
+
+# Highlight the author in author lists? (true/false)
+highlight_name: true
+
+# Organizational groups that you belong to (for People widget)
+#   Remove this if you are not using the People widget.
+user_groups:
+- Supervisor
+
+image:
+  image: "avatar.jpg"
+  caption: "Henry Moss"
+  focal_point: Right
+---
+My name is Henry Moss and I am an Early Career Advanced Fellow in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge.
+
+
+I build ML methods to tackle problems from science, working with Biologists, Chemists, Physicist, Engineers and (most recently) climate scientists.  I am interested in deploying models on low-quality or low-volume experimental data. Unfortunately, I know only two tricks but they seem to work, so my research revolves around them:
+1) Encoding prior scientific knowledge into our ML algorithms (why bother learning it if we know it!)
+2) Using ML to guide the collection of a small amount of additional but high value measurements (e.g. Bayesian optimisation, active learning)
+

content/authors/sebastianober/_index.md

@@ -0,0 +1,37 @@
+---
+title: "Sebastian Ober"
+author: ["Sebastian Ober"]
+lastmod: 2023-11-10T20:49:51+00:00
+draft: false
+weight: 3002
+active: true
+superuser: false
+role: "Senior Scientist for ML"
+organizations:
+  - name: "Astrazeneca"
+    url: "hhttps://www.astrazeneca.co.uk/"
+  - name: "Webpage"
+    url: "https://tomdiethe.com/"
+# Interests to show in About widget
+interests:
+  - ML for proteins
+  - Active Learning
+  - Probabilistic ML
+  - Gaussian processes
+
+# Highlight the author in author lists? (true/false)
+highlight_name: false
+
+# Organizational groups that you belong to (for People widget)
+#   Remove this if you are not using the People widget.
+user_groups:
+- Supervisor
+
+image:
+  image: "avatar.jpg"
+  caption: "Sebastian Ober"
+  focal_point: Right
+---
+I am Sebastian Ober and I am currently a Senior Scientist for ML in AstraZeneca’s Biologics Engineering group. I recently completed my PhD in the ML group at the University of Cambridge and have also worked in the tech start-up Secondmind as an ML researcher.
+
+My current research interest centres around the application of ML to use cases relevant to the design of biologics. This is challenging because the process that biologists use to obtain data are often noisy and impose many constraints that are difficult to model, requiring a combination of various generative modelling and supervised learning approaches I believe that probabilistic modelling and active learning, combined with high throughput assays and experimental setups can help us to unlock new and more effective biologic designs.

content/authors/tomdiethe/_index.md

@@ -0,0 +1,33 @@
+---
+title: "Tom Diethe"
+author: ["Tom Diethe"]
+lastmod: 2023-11-10T20:49:51+00:00
+draft: false
+weight: 3002
+active: true
+superuser: false
+role: "Head of The Centre for Artifical Intelligence"
+organizations:
+  - name: "AstraZeneca"
+    url: "hhttps://www.astrazeneca.co.uk/"
+  - name: "Webpage"
+    url: "https://tomdiethe.com/"
+# Interests to show in About widget
+interests:
+  - probabilistic and deep learning methods for machine learning applications in the life sciences
+
+# Highlight the author in author lists? (true/false)
+highlight_name: false
+
+# Organizational groups that you belong to (for People widget)
+#   Remove this if you are not using the People widget.
+user_groups:
+- Supervisor
+
+image:
+  image: "avatar.jpg"
+  caption: "Tom Diethe"
+  focal_point: Right
+---
+Dr Tom Diethe is Head of The Centre for Artificial Intelligence (Executive Director) at AstraZeneca, Cambridge UK. Tom’s department sits within the Data Science & Artificial Intelligence organisation, which is part of the wider BioPharmaceuticals R&D. The team’s mission is to devise innovative products and solutions using machine learning algorithms that will make the drug discovery pipeline more efficient and aid in a better understanding of biology and medicinal chemistry.
+

content/authors/vigneshgopakumar/_index.md

@@ -0,0 +1,35 @@
+---
+title: "Vignesh Gopakumar"
+author: ["Vignesh Gopakumar"]
+lastmod: 2023-11-10T20:49:51+00:00
+draft: false
+weight: 3002
+active: true
+superuser: false
+role: "Scientific Machine Learning Engineer"
+organizations:
+  - name: "UK Atomic Engergy Authority"
+    url: "https://www.gov.uk/government/organisations/uk-atomic-energy-authority"
+  - name: "Webpage"
+    url: "https://www.vignesh-gopakumar.com/"
+# Interests to show in About widget
+interests:
+  - Neural Operators
+  - Physics Informed Neural Networks
+  - Conformal preddiction
+  - Bayesian Optimisation
+
+# Highlight the author in author lists? (true/false)
+highlight_name: false
+
+# Organizational groups that you belong to (for People widget)
+#   Remove this if you are not using the People widget.
+user_groups:
+- Supervisor
+
+image:
+  image: "avatar.jpg"
+  caption: "Vignesh Gopakumar"
+  focal_point: Right
+---
+My name is Vignesh Gopakumar and I an AI researcher specialising in crafting "explainable" machine learning models that blend known physical laws with real-world data. Currently, I work as an AI Research Scientist at the UK Atomic Energy Authority, leading a team that builds actionable surrogate models for exascale simulations and data-driven models for plasma control and reactor design. Additionally, I'm a visiting AI Researcher at the Rutherford Appleton Laboratory - STFC, where I contribute to foundational research in making machine learning models more robust and interpretable through physics-based approaches.

content/talk/2024_holisticml_moss_ober_diethe/index.md

@@ -6,7 +6,7 @@ draft: false
 weight: 4001
 event: "Projects 2024"
 authors: ["henrymoss","sebastianober","tomdiethe"]
-summary: "This project aims to develop interactive machine learning methods that help scientists guide high-throughput screening in drug discovery pipelines."
+summary: "This project aims to develop interactive machine learning methods that help AstraZeneca scientists guide high-throughput screening in drug discovery pipelines."
 ---
 
 Although AI has revolutionised numerous design processes, its impact on end-to-end biologic optimization campaigns, where we design large, complex molecules such as proteins or  antibodies, has so far been limited. In existing ML-guided design algorithms, a crucial assumption is that the problem is fully understood and specified before beginning optimisation. However, this assumption doesn't hold in drug design: distilling the knowledge of scientists, which is often based on intuition and partial information, into computable quantities is challenging. Furthermore, the goals of drug design can change as we gain insight about what constitutes a "good" biologic in the current context.