Meet Josephine Sullivan, new PA for the TMAIM programme
On 1 January, Josephine Sullivan will begin her new assignment as PA for the Master's programme in Machine learning. What is she most looking forward to with her new assignment?
How does it feel to start as a PA for the TMAIM programme?
Exciting. The Machine Learning programme is very popular and the material taught on the programme is highly relevant to many research areas and society as a whole. Thus it is very exciting to help facilitate and shape the education of the students who will become the practitioners, drivers and leaders of ML and AI within Sweden, Europe and beyond in the coming decades.
What are you most looking forward to with the assignment?
Interacting with the many smart, talented and enthusiastic students as well as the faculty who teach courses within the programme. As you progress in your career you realise that it is a real privilege to work with youth and especially those that are engaged and passionate about their studies. Their enthusiasm is infectious!
How do you view the future development of the programme in 5 or 10 years?
I think the programme will have to bring some more explicit focus on how ML can be used for the good of society especially sustainability. Our current portfolio of courses gives the students a good grounding in the theory and adaptation of ML to traditional applied problems. However, the programme has yet to explicitly engage and take the advantage of the wealth of research that is on-going already within sustainability at KTH. Much of this research has lots of data and we have the students with the skills (both modelling and software development) to potentially help make new findings and discoveries.
What is the first thing you will do on 1 January when you begin your assignment?
On January 1 I plan to be still on vacation! But when I return to work in January the first thing I plan to do, outside of the inevitable admin, is review the programme's syllabus, engage with the teachers and researchers in the sustainability field and see how we can get the ball rolling on developing courses and/or activities that can stimulate work exploiting ML to help with their problems.