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Allen Yu

Allen Chi-Shing Yu, Ph.D. is a Chevening Scholar 2017/18. He holds a Ph.D. degree in Biochemistry from the Chinese University of Hong Kong, and a MSc in Computer Science at the University of Oxford. He has 10 years of experience in the field of bioinformatics and BigData analysis. During his research career, Allen published 24 posters and publications which cover diabetes, cancer, infectious diseases, and neuro diseases. Other research highlights include discovering the novel subtype of Spinocerebellar ataxia (SCA40), identifying the cause of pathogenesis for a family with Spastic paraparesis, leading the Gold medalist team in 2011 International Genetically Engineered Machine (iGEM) competition.

Apart from academic research, Allen is the co-founder of Codex Genetics Limited and Qualify Hong Kong Limited, which aims to provide personalised medicine service in Asia through the use of the latest genomics and IoT technology. With the financial and business support from the HKSAR Innovation and Technology Commission, Hong Kong Science Park, and the Chinese University of Hong Kong, Codex Genetics has curated and transformed recent advances in cancer and neuro-genomics research into clinically actionable insights.

Upcoming talks

The next big thing in clinical AI

We are now on the cusp of a technological revolution. Big Data technologies instilled an informational perspective to our understanding of the world. With the advent of molecular techniques and informatics capabilities, exabytes of biological data were generated. The colossal volume of biological data contains a treasure trove of insights, which are yet to be fully deciphered for diagnostic use.

In this talk, we will discuss the latest trends in applying Big Data technologies to several key areas of molecular diagnostics. Recent advances in Big Data technology allow us to interrogate a larger breadth and depth of clinical evidences, and in turn, improve the accuracy and speed of diagnosis. While Big Data has a great impact on the development of molecular diagnostic tests, it comes with potential perils. Fundamental issues such as the management and storage of data can create privacy concerns. Heterogeneous types of data pose challenges in reproducibility and standardization. Professionals may struggle to keep up and thus fail to seize the full benefits of the technology. It is now an opportunity for us to help healthcare professionals, educators, and policy-makers understand the impact of Big Data, and steer the development roadmap to positively impact the molecular diagnostic industry.

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