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FoG Presentation 2020 Dennis Wang, University of Sheffield: Looking beyond the hype: Applied AI and machine learning in translational medicine

Dennis graduated from the University of British Columbia (Vancouver, Canada) with a BSc in Computer Science, Microbiology and Immunology. He then moved to the University of Cambridge for an MPhil in Computational Biology working on bio-networks modeling with Prof Jasmin Fisher (Microsoft Research), and a PhD in Biostatistics working on statistical analysis of epigenetics with Prof Lorenz Wernisch (MRC Biostatistics Unit).

Following the completion of my PhD in 2012, he became a postdoc and a scientific associate at the Princess Margaret Cancer Centre in Toronto working with Prof Ming-Sound Tsao and Prof Frances Shepherd on clinical genomics sequencing and medical informatics techniques. With a greater interest in drug development, he went back to Cambridge in 2014 and joined the early drug discovery division of AstraZeneca Plc. where he developed machine learning methods to identify genetic signatures that predict drug response. He joined the Sheffield Institute for Translational Neuroscience, University of Sheffield in 2016 as a Lecturer of Genomic Medicine and was jointly appointed to the Dept. of Computer Science in 2018. Dennis is also the Scientific Director of the Sheffield Bioinformatics Core and Deputy Theme Lead within the NIHR Sheffield Biomedical Research Centre.

Dennis’ research focuses on translating patterns in the human genome into actionable information that accelerates the development of treatments for complex diseases. His group uses machine learning algorithms and statistical models to integrate genomic and clinical data sets in order to predict patient outcomes. They also develop simple computational methods that catch biases in diagnostic data and help clinical researchers objectively stratify patients.

Dennis Wang, Sheffield University