Shazia Akbar's Anita Borg experience
Posted: 15/08/2012
Shazia Akbar, PhD student in the Computer Vision and Image Processing group, spent part of the June in a networking retreat in Zurich with industry giants Google after success in a prestigious scholarship competition. Shazia Akbar (21) is a Google Anita Borg Memorial Scholarship 2012 Finalist.
The Scholarship aims to encourage women to excel in computing and technology, and become active role models and leaders. Places on the Scholarship are awarded based on the strength of candidates’ academic performance, leadership experience and demonstrated passion for computer science.
The Zurich networking retreat gave an opportunity for the winners to get together, meet each other, network, share their experiences and create a community of leaders in the computer science field:
“Google Retreat 2012 was a fantastic experience and I met over 100 of the top students all around the world. It was a rare and exciting opportunity to see Google in action and talk to researchers and developers in the company. Special workshop and parallel sessions were arranged by Googlers to give us an insight into the workings of Google's top applications such as YouTube and Chrome, and special sessions were arranged to give us personal tips for our future. To top it all off, we were treated to excellent cuisine and accommodation, and a personal tour around Zurich. My time in Zurich was unforgettable and I have made friends for life which I will continue to keep in touch with. I would recommend Google Retreat to any student in computing who would like to see a top company in action and experience a rare opportunity to network with peers from around the world.”
Shazia is supervised by Professor Stephen McKenna. Her research is developing computer software to analyse histopathology images of breast cancer, in collaboration with Professor Alastair Thompson and Dr Lee Jordan in the University Medical School at Ninewells Hospital. The aim of Shazia’s research is to detect tumour regions in breast tissue automatically with minimal input from a pathologist, using machine learning techniques.
See www.computing.dundee.ac.uk/projects/vision/index.php to find out more about the Computer Vision and Image Processing group.