Time-series Machine Learning Models for Healthcare: Advancements and Applications

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• Tingting Zhu, University of Oxford
• Tuesday 28 November 2023, 16:00-17:00
• Computer Lab, FW11 and Online (zoom link on this page).

If you have a question about this talk, please contact Cecilia Mascolo.

https://cam-ac-uk.zoom.us/j/89965171767?pwd=RlJSUCtaVHlPL0laTzM2VzB6d1V6UT09

Artificial intelligence plays a crucial role in digital health and represents a vibrant and dynamic research field with profound implications for how we’ll care for future generations. In my talk, I will discuss the advancement of time-series machine learning models tailored to address the challenges and opportunities posed by the vast volume of modern healthcare data and their practical applications. Furthermore, I will share insights from my journey towards realising digital twins, with a specific emphasis on data synthesis and treatment recommendations.

Biography: Professor Tingting Zhu graduated with the DPhil degree in information and biomedical engineering at Oxford University in 2016. This followed her MSc in Biomedical Engineering at University College London and BEng (Hons) in Electrical Engineering from the University of Malta.

After DPhil, Tingting was awarded a Stipendiary Junior Research Fellowship at St. Hilda’s College, Oxford. In 2018, Tingting was appointed as the first Associate Member of Faculty at the Department of Engineering Science; in 2019, following the award of her Royal Academy of Engineering Research Fellowship, she was appointed to full Member of Faculty at the Department of Engineering Science. Tingting is a Non-Tutorial Fellow at Kellogg College and a Stipendiary College Lecturer at Mansfield College.

This talk is part of the Mobile and Wearable Health Seminar Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge talks
• Computer Lab, FW11
• Computer Lab, FW11 and Online (zoom link on this page)
• Department of Computer Science and Technology talks and seminars
• Interested Talks
• Mobile and Wearable Health Seminar Series
• School of Technology
• Trust & Technology Initiative - interesting events
• bld31

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