Merge pull request #14 from williamjameshandley/wjh_bio

Will Handley Bio

content/authors/willhandley/_index.md

@@ -0,0 +1,37 @@
+---
+title: "Will Handley"
+author: ["Will Handley"]
+lastmod: 2024-01-30T13:40:00+00:00
+draft: false
+weight: 3002
+active: true
+superuser: false
+role: "Royal Society University Research Fellow"
+organizations:
+  - name: "University of Cambridge"
+    url: "https://www.kicc.cam.ac.uk/"
+  - name: "Webpage"
+    url: "https://www.willhandley.co.uk/"
+# Interests to show in About widget
+interests:
+  - Bayesian inference
+  - Emulators
+  - Simulation Based inference
+  - Cosmology & Particle Physics
+
+# 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.png"
+  caption: "Will Handley"
+  focal_point: Center
+---
+
+I am a Royal Society URF at the [University of Cambridge](https://willhandley.co.uk), and CTO of [PolyChord Ltd](https://polychord.io/). I lead a group of researchers developing Bayesian and machine learning algorithms, who when apply these to fuse data from accelerators, satellites and terrestrial sensors to understand the properties and fundamental physics of our universe. Within PolyChord Ltd these techniques are spun out into commercial applications in the defence, telecommunications & pharmaceutical sectors. Over the past two years I have worked on a succession of projects with [DSTL](https://www.gov.uk/government/organisations/defence-science-and-technology-laboratory/about) developing the [MIDAS](https://willhandley.co.uk/dstl/will_handley_2023_OFEME.pdf) system for maximum information data acquisition strategies.
+