| Google Scholar | https://scholar.google.co.uk/citations?user=XTkiyj4AAAAJ&hl=en&oi=ao |
Excited that MIT Technology Review is covering our Nature Portfolio Machine Intelligence paper co-authored with @BernhardKainz and Ciarán M. Gilligan Lee
Check out how we employ counterfactual reasoning to empower decision making in a wide range of fields, 👇
Causal machine learning sounds very intriguing but comes with unique challenges especially for medical image analysis. Together with @vlontzos and Daniel Rueckert we explore the state of affairs in the literature about this fascinating topic. More to come...
(No quote boost on Mastadon? let’s see https://sigmoid.social/@vlontzos/109433588748912219).
🚨in our latest paper with @BernhardKainz and D. Rueckert, published in MELBA https://melba-journal.org/papers/2022:028.html we survey the current state of the art in medical imaging and causality. We definitely can foresee some exciting advances when combining the fields
Time for a quick #Introduction
I am a Research Scientist at Spotify's Advanced Causal Inference Lab. I recently got my PhD from Imperial College London working with @BernhardKainz
My main research focus is causal inference in ML, but I also enjoy reading about and working in computer vision, healthcare related ML and space exploration !
Bernhard Kainz