At Digital Surgery I am the manager for a couple of people. This involves onboarding, planning our sub-team's work in the context of the larger team, feedback, etc.
During my PhD I co-supervised an MSc project, in which our student researched innovative probabilistic methods for denoising MR images. This involved creating the project proposal, giving advice and feedback to our student, and formal grading (jointly with other assessors).
ENGS101P, Global Health - I was employed as a demonstrator for a coursework module on creating a biochemical reactor. Students had to use circuit theory, engineering design, software engineering.
MPHYGB20, Regaining Control - I was a demonstrator for a coursework module on creating a computer input device for users with a mobility disability. Students had to use circuit theory, engineering design, control theory, software engineering.
Oxford Royale Summer School, Preparation for Engineering - I was a summer school teacher for a class of 16-18 year-old students. The course gave a broad overview of engineering topics, including signal processing, electrical engineering, mechanical engineering, and civil engineering. In addition to teaching lessons I created several lesson plans, mentored students, and created/supervised practical laboratory sessions.
Many of these courses have notes publicly available. I'm not usually a huge fan of lectures for learning "hard" topics, but all of these have been delightful exceptions. Even if a few of them were quite painful at the time...
Inverse Problems in Imaging - a wonderful introduction to inverse problems. Taught by Simon Arridge at UCL.
Medical Imaging CDT Journal Club> - critical review of key papers in medical imaging, chosen by a different guest academic each week. Taught by Andrew Melbourne (my PhD supervisor!) and Ivana Drobnjak at UCL.
Information Processing in Medical Imaging - a whirlwind tour of medical imaging, focusing on registration/segmentation, with in-depth practical sessions and coursework. Taught by Marc Modat and Jorge Cardoso at UCL.
Graphical Models - a challenging in-depth survey of graphical models. Taught by David Barber at UCL.
Probabilistic and Unsupervised Learning - an elegant course on graphical models and inference. Taught by Maneesh Sahani at the Gatsby Institute.
Unsupervised Machine Learning - an eye-opening look at unsupervised learning. Taught by Frank Wood. Gentler than the Barber or Sahani courses above, though still quite rigorous.
B1 Optimization and C25 Optimization - introductory and intermediate courses on optimisation and numerical methods. Taught by Andrew Zisserman, whose lectures opened my mind to many possibilities.
Research Computing with C++ - a masterclass in some of the unique computing challenges, C++ and otherwise, faced in research. Taught by the awesome team of Matt Clarkson and James Hetherington at UCL.
Microcontroller Systems - a pithy and fascinating look at how computers work. Taught by the irrepressible David Murray at Oxford.
FSL course on arterial spin labelling - a great overview of ASL from Michael Chappell.
These are some of the best teachers I've had, in roughly reverse-chronological order. Some of them were managers, some of them university lecturers, some of them authors, some of them schoolteachers, some of them friends/family who've taught me things. I'm listing them here as teaching inspiration to myself.
Imanol Luengo, Lyle McDonald, Phuongtoan Tran, David Thomas, Andrew Melbourne, James Hetherington, Matt Clarkson, Jorge Cardoso, Simon Arridge, Karolina Soltys, Stephen Payne, Paul Taylor, Michael Chappell, Stephen Roberts, Toby Ord, Merlyn Rees, Alexander Wood, Peter Singer, Robert Bruce Thompson, Bob Whitelock, George Newson, Natalie Kemmitt, Paul Hayton, Mike Pickett, David Marsh, Christine Horne.