• 18:00

    Welcome & Introduction

  • 18:05
    Muhammad Imran

    Presentation: AI and Social Media for Social Good

    Muhammad Imran - Research Scientist - Crisis Computing Team, Qatar Computing Research Institute

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    Title: AI and Social Media for Social Good

    Muhammad Imran works as a Scientist and Lead of the Crisis Computing team at Qatar Computing Research Institute. His interdisciplinary research focuses on natural language processing, crisis informatics, social computing, and applied machine learning. He analyzes social media communications during time-critical situations such as natural disasters using big data analysis techniques such as data mining, machine learning, and deep neural networks. Dr. Imran received his PhD in computer science from the University of Trento in 2013. He then worked as a Postdoctoral researcher at QCRI from 2013-2015. Dr. Imran has published over 70 research papers in leading international conferences and journals. Three of his papers received the "Best Paper” award and two "Best Paper Runner-up” awards.

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  • 18:20
    Mark Crowley

    Presentation: Adaptation Through Learning: Using Machine Learning to Improve Forest Wildfire Management

    Mark Crowley - Assistant Professor - University of Waterloo

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    Adaptation Through Learning: Using Machine Learning to Improve Forest Wildfire Management

    In this talk I will provide a window into this situation by looking at forest wildfire management as a case study of a rich domain where some work has been done but huge opportunities remain. Existing forest wildfire spread models are complex manual constructions that are struggling to adapt to changing climate as well as changing attitudes towards forests.

    Deep Learning learning algorithms are being applied daily in ever more challenging domains, however in areas of huge societal importance such as ecology, sustainable resource management and environmental modelling, the analytical methods being used have not always caught up with the recent Deep Learning revolution. This is not surprising new methods are hard to master but also because these domains place very high demands on confidence and robustness that AI researchers rarely face with simpler validation domains.

    What is needed are more adaptive models which fuse human experience, experimental data as well current satellite, aerial, weather and other data. Given the recent phase shift in the intensity of forest wildfires around the world, the urgency is increasing for more responsive predictive models and more powerful decision making tools. I will review the few machine learning approaches that have been applied to this important task and present some of our own work on using Deep Reinforcement Learning to learn fire spread prediction models directly from satellite imagery and simulations by treating fire as the agent that is choosing where to spread.

    This "learning of an agent-based model" approach could also apply to prediction and decision making for other instances of spatially spreading processes such as infectious disease and invasive species. Sustainability and environmental domains provide a great opportunity for the AI/ML community to step up and find solutions that will make a real difference to the lives of many people and the health of ecosystems.

    Mark Crowley is an Assistant Professor in the Department of Electrical and Computer Engineering and the Waterloo Artificial Intelligence Institute at the University of Waterloo. He did a postdoc at Oregon State University with Tom Dietterich's machine learning group researching computational sustainability problems and received his PhD from the University of British Columbia in 2011.

    He is also a Faculty Research Fellow at Element AI. His research seeks dependable and transparent ways to augment human decision making in complex domains in the presence of spatial structure, large scale streaming data, and uncertainty. His focus is on developing new algorithms within the fields of Reinforcement Learning, Deep Learning and Ensemble Methods.

    Dr. Crowley often works in collaboration with researchers and policy makers in diverse fields such as sustainable forest management, ecology, autonomous driving, physical chemistry and medical imaging. In particular relation to sustainability, he has validated his algorithms on domains in invasive species control, forest harvest management and forest wildfire management. These types of domains offer unique challenges for traditional artificial intelligence and machine learning algorithms for decision making, prediction and anomaly detection.

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  • 18:35
    Rosana de Oliveira Gomes

    Presentation: AI for Cyclone Response

    Rosana de Oliveira Gomes - Lead Machine Learning Engineer - Omdena

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    AI for Cyclone Response

    When a disaster strikes, humanitarian agencies need to act fast in order to provide the help needed by the affected communities. Determining what items and in what quantity must be deployed is a crucial task on disaster management. In this webinar, I will discuss how our Omdena team used Artificial Intelligence techniques and disaster management guidelines to build an application to determine what items should be present in relief packages for cyclone response. The team has used publicly available data on cyclones and socio-economic features in order to predict the affected population and identify the most suitable relief packages.

    Rosana is a PhD in Astrophysicist, with a 10 years background in academic research. Currently she is transitioning careers into Data Science and Artificial Intelligence for social good, looking for opportunities to apply her knowledge into the nonprofit and humanitarian sector. In the Omdena community, Rosana has participated on Artificial Intelligence challenges, implementing data-driven solutions to real world problems. In particular, she was one of the managers in the Omdena project presented in this webinar, in which a team of 35 persons build an application for cyclone response, identifying what items need to be supplied when a disaster takes place.

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  • 18:50

    Q&A with the Speakers

  • 19:00

    END OF WEBINAR

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