Karla’s PhD is about the level of energy services used in different countries with a focus on development. The research involves analysing country-level databases, performing statistical analyses and making visualisations.
Karla holds a Bachelors’ degree in Chemical Engineering from the National Autonomous University of Mexico (UNAM) and an MSc from University College London (UCL).
Karla’s professional experiences before the PhD included working for the non-alcoholic beverage industry in Mexico in the areas of quality, environment protection and operations management.
Dr. Pingfan Song is a senior research associate at the Machine Learning group in University of Cambridge. He is endorsed under the Global Talent by UK Research and Innovation (UKRI) in recognition of his critical contributions.
He has been working on trustworthy machine learning which aims to advance work on key technical underpinnings of interpretability/transparency, fairness, and robustness of machine learning systems, and to develop timely key applications in real world healthcare settings, such as medical imaging diagnosis systems. In addition to machine learning, he is also a professional in image processing, sparse modeling and sampling theory. His research has been applied to multi-disciplinary fields such as medical imaging, biological imaging, and other computational imaging tasks and inverse problems.
He is a STEM ambassador in UK and a mentor of in2scienceUK where he provides free teaching, career guidance and advice for A-level students in order to empower students from disadvantaged backgrounds to achieve their potential and progress to STEM and research careers. He is also Co-president of London PhD Network (LPN), a non-profit academic association promoting interdisciplinary communication.
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