Applying Deep Learning in Medical Research
Join us January 4th at 10am at Collider in Rochester for a discussion on how deep learning is being used in medical research. Our goal is to bridge the advancements in AI to life sciences and medical research communities. We will be hosting a series of AI Medical Challenges in 2017 and look forward to engaging a broad group of AI researchers, Biologists, Geneticists, Oncologists and others in the medical research community.
On January 4th, we'll be talking more about our objectives and opportunities with Mayo Clinic.
What is Deep Learning
Deep learning is a branch of machine learning based on a set of algorithms that model high level abstractions in data. Deep Learning provides advanced computational power for pattern recognition that can far exceed conventional methods.
Why Should We Care?
The Deep Learning field is squarely rooted in Silicon Valley. It is hard to access unless you are a large tech company, or a top tier research university. We're entering a period where all industries will be transformed by Machine Learning. Because of the vast unknowns and incredible amounts of data, the medical field is positioned to reap the largest potential gains from implementing Deep Learning models. We want to help Minnesota's medical research scene access this wave of computational advancement. There are already 90+ startups using AI to aid drug discovery, medical imaging and diagnostics, healthcare research and hospital management, insights and risk analysis.
SVAI Life Sciences Agenda
SVAI has an aggressive Life Sciences and Healthcare Agenda for 2017. We're looking forward to sharing our vision with you and figuring out ways Minnesota Life Sciences and Healthcare can leverage our group to start Deep Learning initiatives.
We are hosting a series of AI Medical Challeneges in 2017 and are looking for organizations to partner with on medical data sets, problem spaces and sponsorships.
Irene Onyeneho PhD
A former Mayo Clinic Research Fellow, Irene recently completed her PhD in Molecular and Cellular Physiology at Stanford University School of Medicine. She studied at Stanford's Design School, and has worked on strategy for digital health startups like HealthTap and new projects spinning out of the University.
Yad is an applied researcher in deep learning and AI, with interests in developing deep learning algorithms to improve up on sequence to sequence learning tasks, introducing deep learning to hard sciences and democratization of AI.
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