Meet the duo

Bella Ngo & Prashant Aparajeya are teaming up to put women first. 

 

When women are uncomfortable in their bras, they think there’s something wrong with their body because it doesn’t fit into the bra, when really, the bra is the problem. Brarista is working to fix this problem so women can be confident, comfortable and educated about their bodies and bras.

Finding a perfect bra for you shouldn't be daunting or time-consuming. From the comfort of your own home, you can discover a range of bras that work for your body and shape.

Bella Trang Ngo

 

A professional bra fitter (who fits by sight) plus MSc graduate in Tech Entrepreneurship - Bella is combining everything she knows about bras to make bra-shopping a more pleasant experience. 

Prashant Aparajeya

 

An award-winning computer vision scientist graduating from Goldsmiths University, University of London. His research revolves around mathematical modelling of shape pose and movement computing through 2D static images and videos, information retrieval, machine learning, and shape psychology.

Noemi Gyori

 

PhD student at the Centre for Medical Image Computing at University College London. Her research focuses on real-life applications of deep learning, in particular for biophysical modelling of tissue architecture, image segmentation and super-resolution. 

Prof Frederic Fol Leymarie

Professor of Computing at Goldsmiths, University of London. Frederic is a researcher in A.I. and has been developing a mathematical language for shape representation with potential for applications in various domains and industries, from the Arts and Performance areas to Biology, Medicine, Computer-Aided Design (CAD), Architecture and Robotics.

Vandita Shukla

Master's student in Computer Graphics, Vision and Imaging at University College London, with a special inclination towards machine vision and deep learning. She has previously worked on projects involving image parsing, object recognition, tracking, and application of a series of probabilistic models of images in machine vision systems.