ARTIFICIAL
INTELLIGENCE

in

HEALTHCARE

SYMPOSIUM

2022

MARCH
04-06

2022

BLENDED DELIVERY

LISTER
CONFERENCE

CENTRE

University of Alberta
11613 87 Ave NW, Edmonton, AB T6G 2H6

OR

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SPEAKERS

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Michael Levin

Michael Levin received dual B.S. degrees (computer science and biology), followed by a Ph.D. (Harvard University). After post-doc training (Harvard Medical School), he started his independent lab focusing on the biophysics of cell:cell communication during embryogenesis, regeneration, and cancer. His group at Tufts uses biophysical and computational approaches to study decision-making and basal cognition in cells, tissues, and synthetic living machines. Levin holds the Vannevar Bush chair, and directs the Allen Discovery Center at Tufts, working to crack the morphogenetic code for applications in regenerative medicine and basal cognition. Recent work includes the creation of bioinformatics and machine learning tools for discovery of models and interventions in cancer, birth defects, and organ regeneration.

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Ross Mitchell

Dr. Mitchell is currently a professor in the Department of Medicine and the Senior Program Director of Artificial Intelligence Adoption with AHS. He received his PhD at the University of Western Ontario and has been working in the fields of biomedical imaging, artificial intelligence, and machine learning for 30 years.
Dr. Mitchell was the inaugural Artificial Intelligence Officer at the H. Lee Moffitt Cancer Center and Research Institute in Tampa, Florida from 2019 to 2021. There, he led efforts to develop AI tools to improve the efficiency and quality of cancer care, including models to predict patient outcomes from electronic health record data, and natural language processing to infer diagnostic codes from free-text pathology reports. He was also a Professor of Radiology at Mayo Clinic in Arizona from 2011 to 2019 and Professor of Biomedical Engineering, Radiology, and Clinical Neurosciences at the University of Calgary from 2000 to 2011.

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Tim Sweeney

Tim Sweeney, MD, PhD, is co-founder and CEO of Inflammatix. Tim has extensive experience in medical practice (general & trauma surgery), bench research, and bioinformatics / machine learning. While training at Stanford he helped invent the core computational technology on which Inflammatix is based, is named on over a dozen patents related to medical diagnostics, and has published >100 manuscripts & abstracts.  He is PI (through Inflammatix) on multiple development contracts from DARPA, BARDA, and NIH, and brought Inflammatix to recognition as the ‘Most Disruptive Technology’ at AACC in 2019, and the “Fierce 15” list in 2020.

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Roberto Vega

Roberto Vega is a PhD candidate at the University of Alberta working under the supervision of Russ Greiner. His research focuses on how to combine machine learning with medical expert knowledge to learn accurate predictive models. He also collaborates with the local startup MEDO.ai, where he works alongside their AI team to automatically analyze ultrasound images for the detection of health problems.

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Russ Greiner

After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science and the founding Scientific Director of the Alberta Machine Intelligence Institute. He was elected a Fellow of the AAAI, has been awarded a McCalla Professorship and a Killam Annual Professorship; and in 2021, received the CAIAC Lifetime Achievement Award and became a CIFAR AI Chair. For his mentoring, he received a 2020 FGSR Great Supervisor Award.  He has published over 300 refereed papers, most in the areas of machine learning and recently medical informatics, including 5 that have been awarded Best Paper prizes. 

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Lily Zhou

Lily Zhou is currently a first year PhD student at the Department of Radiology, University of Alberta. Her research interests include medical imaging, deep learning and machine learning. She finished her BSc in Biology at Wuhan University. After that she completed her MSc in Data Science at New York University, focusing on deep learning and computer vision. Her previous research works in AI include disease progression prediction, medical image translation, image segmentation and time series analysis. She worked as an AI intern in a startup in New York in 2021, doing data cleaning and applying NLP models for text data analysis.

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Jake Hayward

Dr. Jake Hayward is an early career academic emergency physician focusing his research on the use of secondary data (administrative and EMR) for point of care clinical decision support, data-informed policy making and improved quality and safety of care. He works his clinical shifts primarily at the Royal Alexandra Hospital where mental health, addictions and care equity are emerging topics of investigation. He completed his emergency medicine residency at the University of Alberta in 2019 and a Masters of Public Health at Johns Hopkins in 2020.

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Abhilash Hareendranathan

Dr. Abhilash Hareendranathan is currently a Research Associate (RA) at the Department of
Radiology, University of Alberta. He is a co-founder of the Collaborative on Ultrasound in Deep Learning(CUDL) and has been working on various research projects in medical robotics,
medical image analysis, ultrasound, and artificial intelligence. He completed his Ph.D. in
Medical Robotics from NTU Singapore in 2012 and worked with R&D teams in Singapore
(Panasonic R&D Center) and Germany (Curefab). Since 2014, he has been working on
applications of medical image analysis and AI in radiology. He was the R&D Lead at MEDO.ai
Inc, which is a Singapore-Edmonton-based startup company that aims to develop AI-augmented cloud-based solutions for ultrasound image analysis.

BOOTHS

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