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I was born and raised in a small city - Linfen in Central China and received my Bachelor's in Electronic and Electrical Engineering from University College London. I went on to Imperial College London for my Master's in Machine Learning. From my university life in the UK, I realised that I want to become a researcher and work in academia, contributing to the frontier of AI. My research is driven by the potential of AI in real-world applications ("AI for good"). I believe that the event is the fundamental element of civilization, conceptual understanding, and learning from experience. My ultimate, ambitious goal is to have events embedded into a symbolic approach, allowing an AI to be tuned with rewards based on its behavior, the consequences of its actions, and its prediction of the future. This reflects the real world, where rewards are multifaceted and not based on a single metric. This foundational vision was crystallized by my initial focus on real-world challenges like the early detection of epidemic outbreaks. Rather than building a single application, my PhD has focused on developing the core methodologies needed to tackle such complex problems. Under the supervision of Professor Nigel Collier, and my advisor Dr. Zaiqiao Meng, my doctoral work now establishes the methodological foundations for this vision. I am honoured to be a part of the Gates Cambridge community and grateful for the generous support of the Gates Cambridge Scholarship.

Previous Education

Imperial College London (University of London) Computing (Machine Learning) 2019
University College London EEE 2018

Links

https://scholar.google.com/citations?user=39a2s0EAAAAJ&hl=en
https://www.linkedin.com/in/meiruzhang

Nutrition during pregnancy is perhaps the most influential non-genetic factor for fetal development and lifelong health thereafter. Global rates of obesity are increasing at an alarming rate and with that, an increase in obesity during pregnancy. Children of obese mothers have an increased lifetime risk of developing cardiometabolic problems and psychiatric disorders. My PhD work will explore how maternal obesity affects immune activity to disrupt nutrient partitioning capabilities in the placenta. Through this, I hope to elucidate the mechanisms by which maternal obesity impacts fetal growth and offspring metabolism in order to develop targeted interventions for affected children. Developmental programing, reproductive health, and diet during pregnancy as a modifiable risk factor are internationally relevant science communications and public health issues. Knowledge must move from the laboratory into policy in order to reach health services. What’s more, relevant audiences have to understand and correctly apply this information. For these reasons, I am an active proponent to improving scientific literacy and access to education and dedicate my community development initiatives to creating such educational resources.

Previous Education

University of Toronto Reproductive Physiology 2020
University of Western Ontario Medical Sciences 2018