Name: Practical Machine Learning for Healthcare
Start: 2019-10-03T18:00:00+01:00
End: 2019-10-03T20:00:00+01:00
Location: AstraZeneca Academy House

We're excited to announce our next AstraZeneca Data Science Meetup in Cambrigde.
If you plan to attend, please read the below carefully.

IMPORTANT DETAILS FOR ATTENDEES:
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• Places for this event are limited, if you registered but then can’t do it anymore, please do unregister yourself to free up a place for someone else.

• Due to AstraZeneca security, we ask every attendee to send us full name and Company or University affiliation (by "Message to the Organiser"), so we’re able to print your name badges for the evening.

• Attendees are asked to bring along some form of ID to show on arrival at Academy House reception
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Speaker:
Dr Tempest van Schaik, Senior Machine Learning Engineer (Healthcare), Microsoft Commercial Software Engineering

Abstract:
Job adverts for machine learning engineers who focus exclusively on healthcare have just begun to emerge, as tech companies enter into the healthcare space, and calls for “AI For Good” fuel engineers’ interest in the area. Tempest will separate hype from the reality of practical ML for health, share advice on how to avoid to common pitfalls, and explore some of the major opportunities in ML for health, as she emphasises the importance of close collaboration with medical domain experts.

Speaker Bio:
Tempest van Schaik is a biomedical engineer with experience in the end-to-end development of health technology, from the lab-bench to the patient’s hand. She has a degree in biomedical engineering covering the foundations of medical school, and covering how engineering is applied to health. She also has a degree in electrical engineering, and did internships in synthetic biology, medical imaging and computer vision. In her PhD in Bioengineering at Imperial College London, she developed in vitro and in vivo medical sensors, mainly for the study of cancer. After studying she worked in a Cambridge startup called Repositive, with the aim of improving access to genomic data. Following this, she worked in health-tech product agency startups (Science Practice and Ctrl Group), consulting to health & pharma, and also researching portable genetic sequencing. She developed an app for clinical trials, with novel ways to measure mood and cognition on wearables (a venture with Cambridge Cognition), and a low-cost microfluidic sensor for point-of-care diagnostics. She currently works in Microsoft Commercial Software Engineering (London) as a senior machine learning engineer focusing on health and pharma, solving diverse, real-world problems with AI.

Agenda:
18:00 – 18:30 Attendees arrive, pizza and drinks
18:30 – 19:30 Speaker presentation, Q&A
19:30 – 20:00 Questions, network, and slowly head out (no later than 20:00)

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