WiDS Ambassadors plan an event in their area that coincides with the Women in Data Science (WiDS) Worldwide Conference. They are in their regions supporting women who are currently in the field and inspiring other women to become data scientists. The New Zealand WiDS co-ambassadors for 2023 are Kate Kolich and Bing Xue who volunteer their time to organise WiDS NZ.
This is Kate's sixth year as a WiDS ambassador and this year she is also a member of the global WiDS ambassador advisory council https://www.widsconference.org/aac.html
Kate Kolich - Reserve Bank of New Zealand
Kate is Assistant Governor/General Manager Information, Data, and Analytics at Reserve Bank of New Zealand – Te Pūtea Matua. Kate has had an extensive career in digital, data and technology leadership roles across the public and private sectors. Her experience includes almost 20 years in banking at the Bank of New Zealand where she held a number of data leadership roles including Head of Enterprise Data and Information Services. In her time at BNZ, Kate led many strategic data initiatives and teams across the bank. Before joining the RBNZ, Kate led the Evidence, Insights and Innovation team at EECA (Energy Efficiency and Conservation Authority). Prior to that, she was the Director of Data Systems and Analytics at the Social Wellbeing Agency. Kate is a recognised thought leader on digital and data innovation, and is active in promoting women in STEM through her volunteer work as a Global Women in Data Science Ambassador. Kate has a Bachelor of Arts degree and a Master of Information Management in Information Systems from Victoria University of Wellington. She also holds an Executive Certificate in Strategy and Innovation from Massachusetts Institute of Technology Sloan School of Management.
Bing Xue - Victoria University of Wellington
Bing Xue is currently a Professor of Artificial Intelligence, and Deputy Head of School in the School of Engineering and Computer Science at Victoria University of Wellington. She has over 300 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, machine learning, classification, symbolic regression, feature selection, evolving deep NNs, image analysis, transfer learning, multi-objective machine learning. Dr Xue is currently the Chair of IEEE CIS Evolutionary Computation Technical Committee and Editor of IEEE CIS Newsletter. She has also served as associate editor of several international journals, such as IEEE CIM, IEEE TEVC and ACM TELO. She is also a Fellow of Engineering New Zealand.
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