Across the broad range of ambitions to implement artificial intelligence (AI) in different industries, applications of machine learning in healthcare are amongst the top of the list for funding and press attention in the last several years.
Machine learning offers many opportunities for the healthcare industry, such as disease identification and diagnosis, crowdsourcing treatment options, monitoring drug response and health epidemics. More and more healthcare and technology innovators are collaborating to change our current reality by experimenting with AI and machine learning. Not only have major players jumped into their own AI healthcare projects, several start-ups and smaller organizations have also begun their own efforts to create tools to aid healthcare.
Join us for an evening of talks from world-leading players in machine learning for healthcare, followed by a panel discussion to discuss focal and controversial topics about the applications of machine learning. Topics addressed by the panel will include:
1) safe patient data storage;
2) standards and metrics used to address AI performance;
3) job security;
4) regulations that address machine-based misdiagnosis.
And of course, the evening will end with a networking session as always. Food and drinks are provided during the networking session.
Talk and Panel discussion
In 2000 Antonio Criminisi obtained his doctorate degree (PhD) in computer vision from the University of Oxford. In June 2000 Antonio joined the Machine Learning and Perception group at Microsoft Research in Cambridge as a visiting researcher. In February 2001, he moved to the Interactive Visual Media Group in Redmond (WA, USA) as a post-doctorate researcher. In October 2002, he moved back to the Microsoft Research Cambridge as a Researcher. In 2014, he became Principal Researcher and is now leading Project InnerEye, to develop a tool for assistive AI for cancer treatment.
Antonio has written and co-authored numerous scientific papers and books on machine learning for the analysis of radiological images, decision forests, deep learning and convolutional neural networks, object recognition, image and video analysis and editing, videoconferencing, 3D reconstruction and virtual/augmented reality, forensic science and history of art.
Antonio's research has been awarded a number of best paper prizes in top computer vision and machine learning conferences, amongst which the prestigious David Marr prize at ICCV 2015 for his paper "Deep Neural Decision Forests".
Peyman Gifani, obtained his PhD from the University of Cambridge, Department of Engineering. He then gained a Cambridge/ Wellcome Trust Senior Interdisciplinary Research fellowship to work jointly in the Cambridge Systems Biology Centre (Department of Genetics) and the Machine Intelligence Laboratory, (Department of Engineering). Through his career, he has gained multidisciplinary experience in areas including systems and synthetic biology, drug discovery, nonlinear dynamical systems, machine learning and AI. He is a former committee member of the Cambridge University Entrepreneurs (CUE). Prior to his PhD, he co-founded a biotech company that won several awards.
He is the founder and CEO of AI VIVO as a Cambridge, UK-based company with a unique computational platform to accelerate drug discovery via intelligent systems pharmacology. AI VIVO provides an innovative methodology that captures nonlinear dynamics hidden in biological data and translates them to novel unexpected discoveries. To achieve predictions with unparalleled accuracy, a proprietary data representation algorithm unfolds insightful data associated with diseases, drugs, and targets as input to our optimised prediction engine powered by artificial intelligence (AI). Using the AI prediction engine, AI VIVO develops a portfolio of IP-protected, pre-clinical discovery programmes as differentiated assets with a much higher probability of clinical success.
Monday, April 23rd 2018
18:00 – 21:00 (GMT)
Department of Chemistry