WeCREATE Ambassadors

We are always looking for people who would like to become WeCREATE ambassadors, to help us grow our initiative and shape the future of WeCREATE. Our ambassadors are the glue that will hold the WeCREATE community together and are representing the voice of their region/country/institution.

Who are we looking for?

  • Anyone who is passionate about inspiring more girls and women to follow creative careers in applied maths/engineering/computer science/AI/Machine Learning and beyond.

  • People who are willing to help build our network and make useful connections to broaden our reach.

  • People who have the skills, passion, and commitment to support our initiative.

Why become an ambassador?

  • Ambassadors will be invited to brainstorming sessions to help shape the future of WeCREATE.

  • Ambassadors will be featured on the WeCREATE website with a profile and opportunity to link to their personal webpage or work.

  • Opportunity to build your own network and be part of a community of likeminded people.

  • Opportunity to collaborate internationally.

  • Be part of organising and running WeCREATE events and talks.

If you think you could contribute something to the WeCREATE initiative and would like to become part of our community, please get in touch.


Cambridge, UK

Ioana Bica

Ioana Bica is a second year PhD student at the University of Oxford and at the Alan Turing Institute. She has previously completed a BA and MPhil in Computer Science at the University of Cambridge where she has specialised in machine learning and its applications to biomedicine.
Ioana’s PhD research focuses on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time.
Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.